-
-An open-source Data Science repository to learn and apply -towards solving real world problems.
-This is a shortcut path to start studying Data -Science. Just follow the steps to answer the questions, “What -is Data Science and what should I study to learn Data Science?”
-| Sponsor | -Pitch | -
|---|---|
| — | -Be the first to sponsor! github@academic.io |
-
Data Science is one of the hottest topics on the Computer and -Internet farmland nowadays. People have gathered data from applications -and systems until today and now is the time to analyze them. The next -steps are producing suggestions from the data and creating predictions -about the future. Here -you can find the biggest question for Data Science and -hundreds of answers from experts.
-| Link | -Preview | -
|---|---|
| What is -Data Science @ O’reilly | -Data scientists combine entrepreneurship with patience, the -willingness to build data products incrementally, the ability to -explore, and the ability to iterate over a solution. They are inherently -interdisciplinary. They can tackle all aspects of a problem, from -initial data collection and data conditioning to drawing conclusions. -They can think outside the box to come up with new ways to view the -problem, or to work with very broadly defined problems: “here’s a lot of -data, what can you make from it?” | -
| What is -Data Science @ Quora | -Data Science is a combination of a number of aspects of Data such as -Technology, Algorithm development, and data interference to study the -data, analyse it, and find innovative solutions to difficult problems. -Basically Data Science is all about Analysing data and driving for -business growth by finding creative ways. | -
| The -sexiest job of 21st century | -Data scientists today are akin to Wall Street “quants” of the -1980s and 1990s. In those days people with backgrounds in physics and -math streamed to investment banks and hedge funds, where they could -devise entirely new algorithms and data strategies. Then a variety of -universities developed master’s programs in financial engineering, which -churned out a second generation of talent that was more accessible to -mainstream firms. The pattern was repeated later in the 1990s with -search engineers, whose rarefied skills soon came to be taught in -computer science programs. | -
| Wikipedia | -Data science is an interdisciplinary field that uses scientific -methods, processes, algorithms and systems to extract knowledge and -insights from many structural and unstructured data. Data science is -related to data mining, machine learning and big data. | -
| How -to Become a Data Scientist | -Data scientists are big data wranglers, gathering and analyzing -large sets of structured and unstructured data. A data scientist’s role -combines computer science, statistics, and mathematics. They analyze, -process, and model data then interpret the results to create actionable -plans for companies and other organizations. | -
| a -very short history of #datascience | -The story of how data scientists became sexy is mostly the story -of the coupling of the mature discipline of statistics with a very young -one–computer science. The term “Data Science” has emerged only recently -to specifically designate a new profession that is expected to make -sense of the vast stores of big data. But making sense of data has a -long history and has been discussed by scientists, statisticians, -librarians, computer scientists and others for years. The following -timeline traces the evolution of the term “Data Science” and its use, -attempts to define it, and related terms. | -
| Software -Development Resources for Data Scientists | -Data scientists concentrate on making sense of data through -exploratory analysis, statistics, and models. Software developers apply -a separate set of knowledge with different tools. Although their focus -may seem unrelated, data science teams can benefit from adopting -software development best practices. Version control, automated testing, -and other dev skills help create reproducible, production-ready code and -tools. | -
While not strictly necessary, having a programming language is a -crucial skill to be effective as a data scientist. Currently, the most -popular language is Python, closely followed by R. -Python is a general-purpose scripting language that sees applications in -a wide variety of fields. R is a domain-specific language for -statistics, which contains a lot of common statistics tools out of the -box.
-Python is by far the most popular
-language in science, due in no small part to the ease at which it can be
-used and the vibrant ecosystem of user-generated packages. To install
-packages, there are two main methods: Pip (invoked as
-pip install), the package manager that comes bundled with
-Python, and Anaconda (invoked as
-conda install), a powerful package manager that can install
-packages for Python, R, and can download executables like Git.
Unlike R, Python was not built from the ground up with data science -in mind, but there are plenty of third party libraries to make up for -this. A much more exhaustive list of packages can be found later in this -document, but these four packages are a good set of choices to start -your data science journey with: Scikit-Learn is a -general-purpose data science package which implements the most popular -algorithms - it also includes rich documentation, tutorials, and -examples of the models it implements. Even if you prefer to write your -own implementations, Scikit-Learn is a valuable reference to the -nuts-and-bolts behind many of the common algorithms you’ll find. With Pandas, one can collect and -analyze their data into a convenient table format. Numpy provides very fast tooling for -mathematical operations, with a focus on vectors and matrices. Seaborn, itself based on the Matplotlib package, is a quick way to -generate beautiful visualizations of your data, with many good defaults -available out of the box, as well as a gallery showing how to produce -many common visualizations of your data.
-When embarking on your journey to becoming a data scientist, the -choice of language isn’t particularly important, and both Python and R -have their pros and cons. Pick a language you like, and check out one of -the Free courses we’ve listed below!
-Data science is a powerful tool that is utilized in various fields to -solve real-world problems by extracting insights and patterns from -complex data.
-How do you learn data science? By doing data science, of course! -Okay, okay - that might not be particularly helpful when you’re first -starting out. In this section, we’ve listed some learning resources, in -rough order from least to greatest commitment - Tutorials, Massively Open Online -Courses (MOOCs), Intensive -Programs, and Colleges.
-This section is a collection of packages, tools, algorithms, and -other useful items in the data science world.
-These are some Machine Learning and Data Mining algorithms and models -help you to understand your data and derive meaning from it.
-| Link | -Description | -
|---|---|
| The Data Science Lifecycle -Process | -The Data Science Lifecycle Process is a process for taking data -science teams from Idea to Value repeatedly and sustainably. The process -is documented in this repo | -
| Data Science -Lifecycle Template Repo | -Template repository for data science lifecycle project | -
| RexMex | -A general purpose recommender metrics library for fair -evaluation. | -
| ChemicalX | -A PyTorch based deep learning library for drug pair scoring. | -
| PyTorch -Geometric Temporal | -Representation learning on dynamic graphs. | -
| Little -Ball of Fur | -A graph sampling library for NetworkX with a Scikit-Learn like -API. | -
| Karate -Club | -An unsupervised machine learning extension library for NetworkX with -a Scikit-Learn like API. | -
| ML -Workspace | -All-in-one web-based IDE for machine learning and data science. The -workspace is deployed as a Docker container and is preloaded with a -variety of popular data science libraries (e.g., Tensorflow, PyTorch) -and dev tools (e.g., Jupyter, VS Code) | -
| Neptune.ai | -Community-friendly platform supporting data scientists in creating -and sharing machine learning models. Neptune facilitates teamwork, -infrastructure management, models comparison and reproducibility. | -
| steppy | -Lightweight, Python library for fast and reproducible machine -learning experimentation. Introduces very simple interface that enables -clean machine learning pipeline design. | -
| steppy-toolkit | -Curated collection of the neural networks, transformers and models -that make your machine learning work faster and more effective. | -
| Datalab from -Google | -easily explore, visualize, analyze, and transform data using -familiar languages, such as Python and SQL, interactively. | -
| Hortonworks -Sandbox | -is a personal, portable Hadoop environment that comes with a dozen -interactive Hadoop tutorials. | -
| R | -is a free software environment for statistical computing and -graphics. | -
| Tidyverse | -is an opinionated collection of R packages designed for data -science. All packages share an underlying design philosophy, grammar, -and data structures. | -
| RStudio | -IDE – powerful user interface for R. It’s free and open source, and -works on Windows, Mac, and Linux. | -
| Python - Pandas - -Anaconda | -Completely free enterprise-ready Python distribution for large-scale -data processing, predictive analytics, and scientific computing | -
| Pandas GUI | -Pandas GUI | -
| Scikit-Learn | -Machine Learning in Python | -
| NumPy | -NumPy is fundamental for scientific computing with Python. It -supports large, multi-dimensional arrays and matrices and includes an -assortment of high-level mathematical functions to operate on these -arrays. | -
| Vaex | -Vaex is a Python library that allows you to visualize large datasets -and calculate statistics at high speeds. | -
| SciPy | -SciPy works with NumPy arrays and provides efficient routines for -numerical integration and optimization. | -
| Data -Science Toolbox | -Coursera Course | -
| Data Science -Toolbox | -Blog | -
| Wolfram -Data Science Platform | -Take numerical, textual, image, GIS or other data and give it the -Wolfram treatment, carrying out a full spectrum of data science analysis -and visualization and automatically generate rich interactive -reports—all powered by the revolutionary knowledge-based Wolfram -Language. | -
| Datadog | -Solutions, code, and devops for high-scale data science. | -
| Variance | -Build powerful data visualizations for the web without writing -JavaScript | -
| Kite -Development Kit | -The Kite Software Development Kit (Apache License, Version 2.0), or -Kite for short, is a set of libraries, tools, examples, and -documentation focused on making it easier to build systems on top of the -Hadoop ecosystem. | -
| Domino Data Labs | -Run, scale, share, and deploy your models — without any -infrastructure or setup. | -
| Apache Flink | -A platform for efficient, distributed, general-purpose data -processing. | -
| Apache Hama | -Apache Hama is an Apache Top-Level open source project, allowing you -to do advanced analytics beyond MapReduce. | -
| Weka | -Weka is a collection of machine learning algorithms for data mining -tasks. | -
| Octave | -GNU Octave is a high-level interpreted language, primarily intended -for numerical computations.(Free Matlab) | -
| Apache Spark | -Lightning-fast cluster computing | -
| Hydrosphere -Mist | -a service for exposing Apache Spark analytics jobs and machine -learning models as realtime, batch or reactive web services. | -
| Data Mechanics | -A data science and engineering platform making Apache Spark more -developer-friendly and cost-effective. | -
| Caffe | -Deep Learning Framework | -
| Torch | -A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT | -
| Nervana’s python -based Deep Learning Framework | -Intel® Nervana™ reference deep learning framework committed to best -performance on all hardware. | -
| Skale | -High performance distributed data processing in NodeJS | -
| Aerosolve | -A machine learning package built for humans. | -
| Intel framework | -Intel® Deep Learning Framework | -
| Datawrapper | -An open source data visualization platform helping everyone to -create simple, correct and embeddable charts. Also at github.com | -
| Tensor Flow | -TensorFlow is an Open Source Software Library for Machine -Intelligence | -
| Natural Language Toolkit | -An introductory yet powerful toolkit for natural language processing -and classification | -
| Annotation -Lab | -Free End-to-End No-Code platform for text annotation and DL model -training/tuning. Out-of-the-box support for Named Entity Recognition, -Classification, Relation extraction and Assertion Status Spark NLP -models. Unlimited support for users, teams, projects, documents. | -
| nlp-toolkit for -node.js | -This module covers some basic nlp principles and implementations. -The main focus is performance. When we deal with sample or training data -in nlp, we quickly run out of memory. Therefore every implementation in -this module is written as stream to only hold that data in memory that -is currently processed at any step. | -
| Julia | -high-level, high-performance dynamic programming language for -technical computing | -
| IJulia | -a Julia-language backend combined with the Jupyter interactive -environment | -
| Apache Zeppelin | -Web-based notebook that enables data-driven, interactive data -analytics and collaborative documents with SQL, Scala and more | -
| Featuretools | -An open source framework for automated feature engineering written -in python | -
| Optimus | -Cleansing, pre-processing, feature engineering, exploratory data -analysis and easy ML with PySpark backend. | -
| Albumentations | -А fast and framework agnostic image augmentation library that -implements a diverse set of augmentation techniques. Supports -classification, segmentation, and detection out of the box. Was used to -win a number of Deep Learning competitions at Kaggle, Topcoder and those -that were a part of the CVPR workshops. | -
| DVC | -An open-source data science version control system. It helps track, -organize and make data science projects reproducible. In its very basic -scenario it helps version control and share large data and model -files. | -
| Lambdo | -is a workflow engine that significantly simplifies data analysis by -combining in one analysis pipeline (i) feature engineering and machine -learning (ii) model training and prediction (iii) table population and -column evaluation. | -
| Feast | -A feature store for the management, discovery, and access of machine -learning features. Feast provides a consistent view of feature data for -both model training and model serving. | -
| Polyaxon | -A platform for reproducible and scalable machine learning and deep -learning. | -
| LightTag | -Text Annotation Tool for teams | -
| UBIAI | -Easy-to-use text annotation tool for teams with most comprehensive -auto-annotation features. Supports NER, relations and document -classification as well as OCR annotation for invoice labeling | -
| Trains | -Auto-Magical Experiment Manager, Version Control & DevOps for -AI | -
| Hopsworks | -Open-source data-intensive machine learning platform with a feature -store. Ingest and manage features for both online (MySQL Cluster) and -offline (Apache Hive) access, train and serve models at scale. | -
| MindsDB | -MindsDB is an Explainable AutoML framework for developers. With -MindsDB you can build, train and use state of the art ML models in as -simple as one line of code. | -
| Lightwood | -A Pytorch based framework that breaks down machine learning problems -into smaller blocks that can be glued together seamlessly with an -objective to build predictive models with one line of code. | -
| AWS Data -Wrangler | -An open-source Python package that extends the power of Pandas -library to AWS connecting DataFrames and AWS data related services -(Amazon Redshift, AWS Glue, Amazon Athena, Amazon EMR, etc). | -
| Amazon -Rekognition | -AWS Rekognition is a service that lets developers working with -Amazon Web Services add image analysis to their applications. Catalog -assets, automate workflows, and extract meaning from your media and -applications. | -
| Amazon Textract | -Automatically extract printed text, handwriting, and data from any -document. | -
| Amazon Lookout -for Vision | -Spot product defects using computer vision to automate quality -inspection. Identify missing product components, vehicle and structure -damage, and irregularities for comprehensive quality control. | -
| Amazon CodeGuru | -Automate code reviews and optimize application performance with -ML-powered recommendations. | -
| CML | -An open source toolkit for using continuous integration in data -science projects. Automatically train and test models in production-like -environments with GitHub Actions & GitLab CI, and autogenerate -visual reports on pull/merge requests. | -
| Dask | -An open source Python library to painlessly transition your -analytics code to distributed computing systems (Big Data) | -
| Statsmodels | -A Python-based inferential statistics, hypothesis testing and -regression framework | -
| Gensim | -An open-source library for topic modeling of natural language -text | -
| spaCy | -A performant natural language processing toolkit | -
| Grid -Studio | -Grid studio is a web-based spreadsheet application with full -integration of the Python programming language. | -
| Python Data -Science Handbook | -Python Data Science Handbook: full text in Jupyter Notebooks | -
| Shapley | -A data-driven framework to quantify the value of classifiers in a -machine learning ensemble. | -
| DAGsHub | -A platform built on open source tools for data, model and pipeline -management. | -
| Deepnote | -A new kind of data science notebook. Jupyter-compatible, with -real-time collaboration and running in the cloud. | -
| Valohai | -An MLOps platform that handles machine orchestration, automatic -reproducibility and deployment. | -
| PyMC3 | -A Python Library for Probabalistic Programming (Bayesian Inference -and Machine Learning) | -
| PyStan | -Python interface to Stan (Bayesian inference and modeling) | -
| hmmlearn | -Unsupervised learning and inference of Hidden Markov Models | -
| Chaos -Genius | -ML powered analytics engine for outlier/anomaly detection and root -cause analysis | -
| Nimblebox | -A full-stack MLOps platform designed to help data scientists and -machine learning practitioners around the world discover, create, and -launch multi-cloud apps from their web browser. | -
| Towhee | -A Python library that helps you encode your unstructured data into -embeddings. | -
| LineaPy | -Ever been frustrated with cleaning up long, messy Jupyter notebooks? -With LineaPy, an open source Python library, it takes as little as two -lines of code to transform messy development code into production -pipelines. | -
| envd | -🏕️ machine learning development environment for data science and -AI/ML engineering teams | -
| Explore -Data Science Libraries | -A search engine 🔎 tool to discover & find a curated list of -popular & new libraries, top authors, trending project kits, -discussions, tutorials & learning resources | -
| MLEM | -🐶 Version and deploy your ML models following GitOps -principles | -
| MLflow | -MLOps framework for managing ML models across their full -lifecycle | -
| cleanlab | -Python library for data-centric AI and automatically detecting -various issues in ML datasets | -
| AutoGluon | -AutoML to easily produce accurate predictions for image, text, -tabular, time-series, and multi-modal data | -
| Arize AI | -Arize AI community tier observability tool for monitoring machine -learning models in production and root-causing issues such as data -quality and performance drift. | -
| Aureo.io | -Aureo.io is a low-code platform that focuses on building artificial -intelligence. It provides users with the capability to create pipelines, -automations and integrate them with artificial intelligence models – all -with their basic data. | -
| ERD Lab | -Free cloud based entity relationship diagram (ERD) tool made for -developers. | -
| Arize-Phoenix | -MLOps in a notebook - uncover insights, surface problems, monitor, -and fine tune your models. | -
| Comet | -An MLOps platform with experiment tracking, model production -management, a model registry, and full data lineage to support your ML -workflow from training straight through to production. | -
| CometLLM | -Log, track, visualize, and search your LLM prompts and chains in one -easy-to-use, 100% open-source tool. | -
| Synthical | -AI-powered collaborative environment for research. Find relevant -papers, create collections to manage bibliography, and summarize content -— all in one place | -
| teeplot | -Workflow tool to automatically organize data visualization -output | -
This section includes some additional reading material, channels to -watch, and talks to listen to.
-Below are some Social Media links. Connect with other data -scientists!
-| Description | -|
|---|---|
| Big Data -Combine | -Rapid-fire, live tryouts for data scientists seeking to monetize -their models as trading strategies | -
| Big Data Mania | -Data Viz Wiz, Data Journalist, Growth Hacker, Author of Data Science -for Dummies (2015) | -
| Big Data -Science | -Big Data, Data Science, Predictive Modeling, Business Analytics, -Hadoop, Decision and Operations Research. | -
| Charlie Greenbacker | -Director of Data Science at @ExploreAltamira | -
| Chris Said | -Data scientist at Twitter | -
| Clare Corthell | -Dev, Design, Data Science @mattermark #hackerei | -
| DADI -Charles-Abner | -#datascientist @Ekimetrics. , #machinelearning #dataviz -#DynamicCharts #Hadoop #R #Python #NLP #Bitcoin #dataenthousiast | -
| Data Science -Central | -Data Science Central is the industry’s single resource for Big Data -practitioners. | -
| Data Science London | -Data Science. Big Data. Data Hacks. Data Junkies. Data Startups. -Open Data | -
| Data Science -Renee | -Documenting my path from SQL Data Analyst pursuing an Engineering -Master’s Degree to Data Scientist | -
| Data Science -Report | -Mission is to help guide & advance careers in Data Science & -Analytics | -
| Data Science -Tips | -Tips and Tricks for Data Scientists around the world! #datascience -#bigdata | -
| Data Vizzard | -DataViz, Security, Military | -
| DataScienceX | -- |
| deeplearning4j | -- |
| DJ Patil | -White House Data Chief, VP @ RelateIQ. | -
| Domino Data Lab | -- |
| Drew Conway | -Data nerd, hacker, student of conflict. | -
| Emilio Ferrara | -#Networks, #MachineLearning and #DataScience. I work on #Social -Media. Postdoc at @IndianaUniv | -
| Erin Bartolo | -Running with #BigData–enjoying a love/hate relationship with its -hype. @iSchoolSU -#DataScience Program Mgr. | -
| Greg Reda | -Working @ GrubHub about data and pandas | -
| Gregory Piatetsky | -KDnuggets President, Analytics/Big Data/Data Mining/Data Science -expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, -part-time philosopher. | -
| Hadley Wickham | -Chief Scientist at RStudio, and an Adjunct Professor of Statistics -at the University of Auckland, Stanford University, and Rice -University. | -
| Hakan Kardas | -Data Scientist | -
| Hilary Mason | -Data Scientist in Residence at @accel. | -
| Jeff Hammerbacher | -ReTweeting about data science | -
| John Myles -White | -Scientist at Facebook and Julia developer. Author of Machine -Learning for Hackers and Bandit Algorithms for Website Optimization. -Tweets reflect my views only. | -
| Juan Miguel -Lavista | -Principal Data Scientist @ Microsoft Data Science Team | -
| Julia Evans | -Hacker - Pandas - Data Analyze | -
| Kenneth Cukier | -The Economist’s Data Editor and co-author of Big Data -(http://www.big-data-book.com/). | -
| Kevin Davenport | -Organizer of -https://www.meetup.com/San-Diego-Data-Science-R-Users-Group/ | -
| Kevin Markham | -Data science instructor, and founder of Data School | -
| Kim Rees | -Interactive data visualization and tools. Data flaneur. | -
| Kirk Borne | -DataScientist, PhD Astrophysicist, Top #BigData Influencer. | -
| Linda Regber | -Data storyteller, visualizations. | -
| Luis Rei | -PhD Student. Programming, Mobile, Web. Artificial Intelligence, -Intelligent Robotics Machine Learning, Data Mining, Natural Language -Processing, Data Science. | -
| Mark Stevenson | -Data Analytics Recruitment Specialist at Salt (@SaltJobs) Analytics - -Insight - Big Data - Data science | -
| Matt Harrison | -Opinions of full-stack Python guy, author, instructor, currently -playing Data Scientist. Occasional fathering, husbanding, organic -gardening. | -
| Matthew Russell | -Mining the Social Web. | -
| Mert Nuhoğlu | -Data Scientist at BizQualify, Developer | -
| Monica Rogati | -Data @ Jawbone. Turned data into stories & products at LinkedIn. -Text mining, applied machine learning, recommender systems. Ex-gamer, -ex-machine coder; namer. | -
| Noah Iliinsky | -Visualization & interaction designer. Practical cyclist. Author -of vis books: https://www.oreilly.com/pub/au/4419 | -
| Paul Miller | -Cloud Computing/ Big Data/ Open Data Analyst & Consultant. -Writer, Speaker & Moderator. Gigaom Research Analyst. | -
| Peter Skomoroch | -Creating intelligent systems to automate tasks & improve -decisions. Entrepreneur, ex-Principal Data Scientist @LinkedIn. Machine -Learning, ProductRei, Networks | -
| Prash Chan | -Solution Architect @ IBM, Master Data Management, Data Quality & -Data Governance Blogger. Data Science, Hadoop, Big Data & -Cloud. | -
| Quora Data -Science | -Quora’s data science topic | -
| R-Bloggers | -Tweet blog posts from the R blogosphere, data science conferences, -and (!) open jobs for data scientists. | -
| Rand Hindi | -- |
| Randy Olson | -Computer scientist researching artificial intelligence. Data -tinkerer. Community leader for @DataIsBeautiful. #OpenScience -advocate. | -
| Recep Erol | -Data Science geek @ UALR | -
| Ryan Orban | -Data scientist, genetic origamist, hardware aficionado | -
| Sean J. Taylor | -Social Scientist. Hacker. Facebook Data Science Team. Keywords: -Experiments, Causal Inference, Statistics, Machine Learning, -Economics. | -
| Silvia K. Spiva | -#DataScience at Cisco | -
| Harsh B. Gupta | -Data Scientist at BBVA Compass | -
| Spencer Nelson | -Data nerd | -
| Talha Oz | -Enjoys ABM, SNA, DM, ML, NLP, HI, Python, Java. Top percentile -Kaggler/data scientist | -
| Tasos Skarlatidis | -Complex Event Processing, Big Data, Artificial Intelligence and -Machine Learning. Passionate about programming and open-source. | -
| Terry Timko | -InfoGov; Bigdata; Data as a Service; Data Science; Open, Social -& Business Data Convergence | -
| Tony Baer | -IT analyst with Ovum covering Big Data & data management with -some systems engineering thrown in. | -
| Tony Ojeda | -Data Scientist , Author , Entrepreneur. Co-founder @DataCommunityDC. -Founder @DistrictDataLab. #DataScience -#BigData #DataDC | -
| Vamshi Ambati | -Data Science @ PayPal. #NLP, #machinelearning; PhD, Carnegie Mellon -alumni (Blog: https://allthingsds.wordpress.com ) | -
| Wes McKinney | -Pandas (Python Data Analysis library). | -
| WileyEd | -Senior Manager - @Seagate Big Data Analytics @McKinsey Alum #BigData + -#Analytics Evangelist #Hadoop, #Cloud, #Digital, & #R -Enthusiast | -
| WNYC Data News Team | -The data news crew at @WNYC. Practicing data-driven journalism, -making it visual, and showing our work. | -
| Alexey Grigorev | -Data science author | -
| İlker Arslan | -Data science author. Shares mostly about Julia programming | -
| INEVITABLE | -AI & Data Science Start-up Company based in England, UK | -
Some data mining competition platforms
- -| Preview | -Description | -
|---|---|
 |
-Key -differences of a data scientist vs. data engineer | -
 |
-A visual guide to Becoming a Data Scientist in 8 Steps by DataCamp (img) | -
 |
-Mindmap on required skills (img) | -
 |
-Swami Chandrasekaran made a Curriculum -via Metro map. | -
 |
-by @kzawadz via twitter | -
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-By Data Science -Central | -
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-Data Science Wars: R vs Python | -
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-How to select statistical or machine learning techniques | -
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-Choosing the Right Estimator | -
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-The Data Science Industry: Who Does What | -
 |
-Data Science |
-
 |
-Different Data Science Skills and Roles from this -article by Springboard | -
 |
-A simple and friendly way of teaching your non-data -scientist/non-statistician colleagues how to avoid -mistakes with data. From Geckoboard’s Data Literacy -Lessons. | -
A curated list of amazingly awesome open source data science +resources.
+Data Visualization
+A JavaScript visualization library for HTML and SVG - +http://d3js.org
+Real-time visualization library - https://github.com/fastly/epoch
diff --git a/html/datascience.md2.html b/html/datascience.md2.html index c1c9d2a..ba6d85c 100644 --- a/html/datascience.md2.html +++ b/html/datascience.md2.html @@ -1,7 +1,2886 @@ -A curated list of amazingly awesome open source data science -resources.
-Data Visualization
-A JavaScript visualization library for HTML and SVG - -http://d3js.org
-Real-time visualization library - https://github.com/fastly/epoch
+
+An open-source Data Science repository to learn and apply +towards solving real world problems.
+This is a shortcut path to start studying Data +Science. Just follow the steps to answer the questions, “What +is Data Science and what should I study to learn Data Science?”
+| Sponsor | +Pitch | +
|---|---|
| — | +Be the first to sponsor! github@academic.io |
+
Data Science is one of the hottest topics on the Computer and +Internet farmland nowadays. People have gathered data from applications +and systems until today and now is the time to analyze them. The next +steps are producing suggestions from the data and creating predictions +about the future. Here +you can find the biggest question for Data Science and +hundreds of answers from experts.
+| Link | +Preview | +
|---|---|
| What is +Data Science @ O’reilly | +Data scientists combine entrepreneurship with patience, the +willingness to build data products incrementally, the ability to +explore, and the ability to iterate over a solution. They are inherently +interdisciplinary. They can tackle all aspects of a problem, from +initial data collection and data conditioning to drawing conclusions. +They can think outside the box to come up with new ways to view the +problem, or to work with very broadly defined problems: “here’s a lot of +data, what can you make from it?” | +
| What is +Data Science @ Quora | +Data Science is a combination of a number of aspects of Data such as +Technology, Algorithm development, and data interference to study the +data, analyse it, and find innovative solutions to difficult problems. +Basically Data Science is all about Analysing data and driving for +business growth by finding creative ways. | +
| The +sexiest job of 21st century | +Data scientists today are akin to Wall Street “quants” of the +1980s and 1990s. In those days people with backgrounds in physics and +math streamed to investment banks and hedge funds, where they could +devise entirely new algorithms and data strategies. Then a variety of +universities developed master’s programs in financial engineering, which +churned out a second generation of talent that was more accessible to +mainstream firms. The pattern was repeated later in the 1990s with +search engineers, whose rarefied skills soon came to be taught in +computer science programs. | +
| Wikipedia | +Data science is an interdisciplinary field that uses scientific +methods, processes, algorithms and systems to extract knowledge and +insights from many structural and unstructured data. Data science is +related to data mining, machine learning and big data. | +
| How +to Become a Data Scientist | +Data scientists are big data wranglers, gathering and analyzing +large sets of structured and unstructured data. A data scientist’s role +combines computer science, statistics, and mathematics. They analyze, +process, and model data then interpret the results to create actionable +plans for companies and other organizations. | +
| a +very short history of #datascience | +The story of how data scientists became sexy is mostly the story +of the coupling of the mature discipline of statistics with a very young +one–computer science. The term “Data Science” has emerged only recently +to specifically designate a new profession that is expected to make +sense of the vast stores of big data. But making sense of data has a +long history and has been discussed by scientists, statisticians, +librarians, computer scientists and others for years. The following +timeline traces the evolution of the term “Data Science” and its use, +attempts to define it, and related terms. | +
| Software +Development Resources for Data Scientists | +Data scientists concentrate on making sense of data through +exploratory analysis, statistics, and models. Software developers apply +a separate set of knowledge with different tools. Although their focus +may seem unrelated, data science teams can benefit from adopting +software development best practices. Version control, automated testing, +and other dev skills help create reproducible, production-ready code and +tools. | +
While not strictly necessary, having a programming language is a +crucial skill to be effective as a data scientist. Currently, the most +popular language is Python, closely followed by R. +Python is a general-purpose scripting language that sees applications in +a wide variety of fields. R is a domain-specific language for +statistics, which contains a lot of common statistics tools out of the +box.
+Python is by far the most popular
+language in science, due in no small part to the ease at which it can be
+used and the vibrant ecosystem of user-generated packages. To install
+packages, there are two main methods: Pip (invoked as
+pip install), the package manager that comes bundled with
+Python, and Anaconda (invoked as
+conda install), a powerful package manager that can install
+packages for Python, R, and can download executables like Git.
Unlike R, Python was not built from the ground up with data science +in mind, but there are plenty of third party libraries to make up for +this. A much more exhaustive list of packages can be found later in this +document, but these four packages are a good set of choices to start +your data science journey with: Scikit-Learn is a +general-purpose data science package which implements the most popular +algorithms - it also includes rich documentation, tutorials, and +examples of the models it implements. Even if you prefer to write your +own implementations, Scikit-Learn is a valuable reference to the +nuts-and-bolts behind many of the common algorithms you’ll find. With Pandas, one can collect and +analyze their data into a convenient table format. Numpy provides very fast tooling for +mathematical operations, with a focus on vectors and matrices. Seaborn, itself based on the Matplotlib package, is a quick way to +generate beautiful visualizations of your data, with many good defaults +available out of the box, as well as a gallery showing how to produce +many common visualizations of your data.
+When embarking on your journey to becoming a data scientist, the +choice of language isn’t particularly important, and both Python and R +have their pros and cons. Pick a language you like, and check out one of +the Free courses we’ve listed below!
+Data science is a powerful tool that is utilized in various fields to +solve real-world problems by extracting insights and patterns from +complex data.
+How do you learn data science? By doing data science, of course! +Okay, okay - that might not be particularly helpful when you’re first +starting out. In this section, we’ve listed some learning resources, in +rough order from least to greatest commitment - Tutorials, Massively Open Online +Courses (MOOCs), Intensive +Programs, and Colleges.
+This section is a collection of packages, tools, algorithms, and +other useful items in the data science world.
+These are some Machine Learning and Data Mining algorithms and models +help you to understand your data and derive meaning from it.
+| Link | +Description | +
|---|---|
| The Data Science Lifecycle +Process | +The Data Science Lifecycle Process is a process for taking data +science teams from Idea to Value repeatedly and sustainably. The process +is documented in this repo | +
| Data Science +Lifecycle Template Repo | +Template repository for data science lifecycle project | +
| RexMex | +A general purpose recommender metrics library for fair +evaluation. | +
| ChemicalX | +A PyTorch based deep learning library for drug pair scoring. | +
| PyTorch +Geometric Temporal | +Representation learning on dynamic graphs. | +
| Little +Ball of Fur | +A graph sampling library for NetworkX with a Scikit-Learn like +API. | +
| Karate +Club | +An unsupervised machine learning extension library for NetworkX with +a Scikit-Learn like API. | +
| ML +Workspace | +All-in-one web-based IDE for machine learning and data science. The +workspace is deployed as a Docker container and is preloaded with a +variety of popular data science libraries (e.g., Tensorflow, PyTorch) +and dev tools (e.g., Jupyter, VS Code) | +
| Neptune.ai | +Community-friendly platform supporting data scientists in creating +and sharing machine learning models. Neptune facilitates teamwork, +infrastructure management, models comparison and reproducibility. | +
| steppy | +Lightweight, Python library for fast and reproducible machine +learning experimentation. Introduces very simple interface that enables +clean machine learning pipeline design. | +
| steppy-toolkit | +Curated collection of the neural networks, transformers and models +that make your machine learning work faster and more effective. | +
| Datalab from +Google | +easily explore, visualize, analyze, and transform data using +familiar languages, such as Python and SQL, interactively. | +
| Hortonworks +Sandbox | +is a personal, portable Hadoop environment that comes with a dozen +interactive Hadoop tutorials. | +
| R | +is a free software environment for statistical computing and +graphics. | +
| Tidyverse | +is an opinionated collection of R packages designed for data +science. All packages share an underlying design philosophy, grammar, +and data structures. | +
| RStudio | +IDE – powerful user interface for R. It’s free and open source, and +works on Windows, Mac, and Linux. | +
| Python - Pandas - +Anaconda | +Completely free enterprise-ready Python distribution for large-scale +data processing, predictive analytics, and scientific computing | +
| Pandas GUI | +Pandas GUI | +
| Scikit-Learn | +Machine Learning in Python | +
| NumPy | +NumPy is fundamental for scientific computing with Python. It +supports large, multi-dimensional arrays and matrices and includes an +assortment of high-level mathematical functions to operate on these +arrays. | +
| Vaex | +Vaex is a Python library that allows you to visualize large datasets +and calculate statistics at high speeds. | +
| SciPy | +SciPy works with NumPy arrays and provides efficient routines for +numerical integration and optimization. | +
| Data +Science Toolbox | +Coursera Course | +
| Data Science +Toolbox | +Blog | +
| Wolfram +Data Science Platform | +Take numerical, textual, image, GIS or other data and give it the +Wolfram treatment, carrying out a full spectrum of data science analysis +and visualization and automatically generate rich interactive +reports—all powered by the revolutionary knowledge-based Wolfram +Language. | +
| Datadog | +Solutions, code, and devops for high-scale data science. | +
| Variance | +Build powerful data visualizations for the web without writing +JavaScript | +
| Kite +Development Kit | +The Kite Software Development Kit (Apache License, Version 2.0), or +Kite for short, is a set of libraries, tools, examples, and +documentation focused on making it easier to build systems on top of the +Hadoop ecosystem. | +
| Domino Data Labs | +Run, scale, share, and deploy your models — without any +infrastructure or setup. | +
| Apache Flink | +A platform for efficient, distributed, general-purpose data +processing. | +
| Apache Hama | +Apache Hama is an Apache Top-Level open source project, allowing you +to do advanced analytics beyond MapReduce. | +
| Weka | +Weka is a collection of machine learning algorithms for data mining +tasks. | +
| Octave | +GNU Octave is a high-level interpreted language, primarily intended +for numerical computations.(Free Matlab) | +
| Apache Spark | +Lightning-fast cluster computing | +
| Hydrosphere +Mist | +a service for exposing Apache Spark analytics jobs and machine +learning models as realtime, batch or reactive web services. | +
| Data Mechanics | +A data science and engineering platform making Apache Spark more +developer-friendly and cost-effective. | +
| Caffe | +Deep Learning Framework | +
| Torch | +A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT | +
| Nervana’s python +based Deep Learning Framework | +Intel® Nervana™ reference deep learning framework committed to best +performance on all hardware. | +
| Skale | +High performance distributed data processing in NodeJS | +
| Aerosolve | +A machine learning package built for humans. | +
| Intel framework | +Intel® Deep Learning Framework | +
| Datawrapper | +An open source data visualization platform helping everyone to +create simple, correct and embeddable charts. Also at github.com | +
| Tensor Flow | +TensorFlow is an Open Source Software Library for Machine +Intelligence | +
| Natural Language Toolkit | +An introductory yet powerful toolkit for natural language processing +and classification | +
| Annotation +Lab | +Free End-to-End No-Code platform for text annotation and DL model +training/tuning. Out-of-the-box support for Named Entity Recognition, +Classification, Relation extraction and Assertion Status Spark NLP +models. Unlimited support for users, teams, projects, documents. | +
| nlp-toolkit for +node.js | +This module covers some basic nlp principles and implementations. +The main focus is performance. When we deal with sample or training data +in nlp, we quickly run out of memory. Therefore every implementation in +this module is written as stream to only hold that data in memory that +is currently processed at any step. | +
| Julia | +high-level, high-performance dynamic programming language for +technical computing | +
| IJulia | +a Julia-language backend combined with the Jupyter interactive +environment | +
| Apache Zeppelin | +Web-based notebook that enables data-driven, interactive data +analytics and collaborative documents with SQL, Scala and more | +
| Featuretools | +An open source framework for automated feature engineering written +in python | +
| Optimus | +Cleansing, pre-processing, feature engineering, exploratory data +analysis and easy ML with PySpark backend. | +
| Albumentations | +А fast and framework agnostic image augmentation library that +implements a diverse set of augmentation techniques. Supports +classification, segmentation, and detection out of the box. Was used to +win a number of Deep Learning competitions at Kaggle, Topcoder and those +that were a part of the CVPR workshops. | +
| DVC | +An open-source data science version control system. It helps track, +organize and make data science projects reproducible. In its very basic +scenario it helps version control and share large data and model +files. | +
| Lambdo | +is a workflow engine that significantly simplifies data analysis by +combining in one analysis pipeline (i) feature engineering and machine +learning (ii) model training and prediction (iii) table population and +column evaluation. | +
| Feast | +A feature store for the management, discovery, and access of machine +learning features. Feast provides a consistent view of feature data for +both model training and model serving. | +
| Polyaxon | +A platform for reproducible and scalable machine learning and deep +learning. | +
| LightTag | +Text Annotation Tool for teams | +
| UBIAI | +Easy-to-use text annotation tool for teams with most comprehensive +auto-annotation features. Supports NER, relations and document +classification as well as OCR annotation for invoice labeling | +
| Trains | +Auto-Magical Experiment Manager, Version Control & DevOps for +AI | +
| Hopsworks | +Open-source data-intensive machine learning platform with a feature +store. Ingest and manage features for both online (MySQL Cluster) and +offline (Apache Hive) access, train and serve models at scale. | +
| MindsDB | +MindsDB is an Explainable AutoML framework for developers. With +MindsDB you can build, train and use state of the art ML models in as +simple as one line of code. | +
| Lightwood | +A Pytorch based framework that breaks down machine learning problems +into smaller blocks that can be glued together seamlessly with an +objective to build predictive models with one line of code. | +
| AWS Data +Wrangler | +An open-source Python package that extends the power of Pandas +library to AWS connecting DataFrames and AWS data related services +(Amazon Redshift, AWS Glue, Amazon Athena, Amazon EMR, etc). | +
| Amazon +Rekognition | +AWS Rekognition is a service that lets developers working with +Amazon Web Services add image analysis to their applications. Catalog +assets, automate workflows, and extract meaning from your media and +applications. | +
| Amazon Textract | +Automatically extract printed text, handwriting, and data from any +document. | +
| Amazon Lookout +for Vision | +Spot product defects using computer vision to automate quality +inspection. Identify missing product components, vehicle and structure +damage, and irregularities for comprehensive quality control. | +
| Amazon CodeGuru | +Automate code reviews and optimize application performance with +ML-powered recommendations. | +
| CML | +An open source toolkit for using continuous integration in data +science projects. Automatically train and test models in production-like +environments with GitHub Actions & GitLab CI, and autogenerate +visual reports on pull/merge requests. | +
| Dask | +An open source Python library to painlessly transition your +analytics code to distributed computing systems (Big Data) | +
| Statsmodels | +A Python-based inferential statistics, hypothesis testing and +regression framework | +
| Gensim | +An open-source library for topic modeling of natural language +text | +
| spaCy | +A performant natural language processing toolkit | +
| Grid +Studio | +Grid studio is a web-based spreadsheet application with full +integration of the Python programming language. | +
| Python Data +Science Handbook | +Python Data Science Handbook: full text in Jupyter Notebooks | +
| Shapley | +A data-driven framework to quantify the value of classifiers in a +machine learning ensemble. | +
| DAGsHub | +A platform built on open source tools for data, model and pipeline +management. | +
| Deepnote | +A new kind of data science notebook. Jupyter-compatible, with +real-time collaboration and running in the cloud. | +
| Valohai | +An MLOps platform that handles machine orchestration, automatic +reproducibility and deployment. | +
| PyMC3 | +A Python Library for Probabalistic Programming (Bayesian Inference +and Machine Learning) | +
| PyStan | +Python interface to Stan (Bayesian inference and modeling) | +
| hmmlearn | +Unsupervised learning and inference of Hidden Markov Models | +
| Chaos +Genius | +ML powered analytics engine for outlier/anomaly detection and root +cause analysis | +
| Nimblebox | +A full-stack MLOps platform designed to help data scientists and +machine learning practitioners around the world discover, create, and +launch multi-cloud apps from their web browser. | +
| Towhee | +A Python library that helps you encode your unstructured data into +embeddings. | +
| LineaPy | +Ever been frustrated with cleaning up long, messy Jupyter notebooks? +With LineaPy, an open source Python library, it takes as little as two +lines of code to transform messy development code into production +pipelines. | +
| envd | +🏕️ machine learning development environment for data science and +AI/ML engineering teams | +
| Explore +Data Science Libraries | +A search engine 🔎 tool to discover & find a curated list of +popular & new libraries, top authors, trending project kits, +discussions, tutorials & learning resources | +
| MLEM | +🐶 Version and deploy your ML models following GitOps +principles | +
| MLflow | +MLOps framework for managing ML models across their full +lifecycle | +
| cleanlab | +Python library for data-centric AI and automatically detecting +various issues in ML datasets | +
| AutoGluon | +AutoML to easily produce accurate predictions for image, text, +tabular, time-series, and multi-modal data | +
| Arize AI | +Arize AI community tier observability tool for monitoring machine +learning models in production and root-causing issues such as data +quality and performance drift. | +
| Aureo.io | +Aureo.io is a low-code platform that focuses on building artificial +intelligence. It provides users with the capability to create pipelines, +automations and integrate them with artificial intelligence models – all +with their basic data. | +
| ERD Lab | +Free cloud based entity relationship diagram (ERD) tool made for +developers. | +
| Arize-Phoenix | +MLOps in a notebook - uncover insights, surface problems, monitor, +and fine tune your models. | +
| Comet | +An MLOps platform with experiment tracking, model production +management, a model registry, and full data lineage to support your ML +workflow from training straight through to production. | +
| CometLLM | +Log, track, visualize, and search your LLM prompts and chains in one +easy-to-use, 100% open-source tool. | +
| Synthical | +AI-powered collaborative environment for research. Find relevant +papers, create collections to manage bibliography, and summarize content +— all in one place | +
| teeplot | +Workflow tool to automatically organize data visualization +output | +
This section includes some additional reading material, channels to +watch, and talks to listen to.
+Below are some Social Media links. Connect with other data +scientists!
+| Description | +|
|---|---|
| Big Data +Combine | +Rapid-fire, live tryouts for data scientists seeking to monetize +their models as trading strategies | +
| Big Data Mania | +Data Viz Wiz, Data Journalist, Growth Hacker, Author of Data Science +for Dummies (2015) | +
| Big Data +Science | +Big Data, Data Science, Predictive Modeling, Business Analytics, +Hadoop, Decision and Operations Research. | +
| Charlie Greenbacker | +Director of Data Science at @ExploreAltamira | +
| Chris Said | +Data scientist at Twitter | +
| Clare Corthell | +Dev, Design, Data Science @mattermark #hackerei | +
| DADI +Charles-Abner | +#datascientist @Ekimetrics. , #machinelearning #dataviz +#DynamicCharts #Hadoop #R #Python #NLP #Bitcoin #dataenthousiast | +
| Data Science +Central | +Data Science Central is the industry’s single resource for Big Data +practitioners. | +
| Data Science London | +Data Science. Big Data. Data Hacks. Data Junkies. Data Startups. +Open Data | +
| Data Science +Renee | +Documenting my path from SQL Data Analyst pursuing an Engineering +Master’s Degree to Data Scientist | +
| Data Science +Report | +Mission is to help guide & advance careers in Data Science & +Analytics | +
| Data Science +Tips | +Tips and Tricks for Data Scientists around the world! #datascience +#bigdata | +
| Data Vizzard | +DataViz, Security, Military | +
| DataScienceX | ++ |
| deeplearning4j | ++ |
| DJ Patil | +White House Data Chief, VP @ RelateIQ. | +
| Domino Data Lab | ++ |
| Drew Conway | +Data nerd, hacker, student of conflict. | +
| Emilio Ferrara | +#Networks, #MachineLearning and #DataScience. I work on #Social +Media. Postdoc at @IndianaUniv | +
| Erin Bartolo | +Running with #BigData–enjoying a love/hate relationship with its +hype. @iSchoolSU +#DataScience Program Mgr. | +
| Greg Reda | +Working @ GrubHub about data and pandas | +
| Gregory Piatetsky | +KDnuggets President, Analytics/Big Data/Data Mining/Data Science +expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, +part-time philosopher. | +
| Hadley Wickham | +Chief Scientist at RStudio, and an Adjunct Professor of Statistics +at the University of Auckland, Stanford University, and Rice +University. | +
| Hakan Kardas | +Data Scientist | +
| Hilary Mason | +Data Scientist in Residence at @accel. | +
| Jeff Hammerbacher | +ReTweeting about data science | +
| John Myles +White | +Scientist at Facebook and Julia developer. Author of Machine +Learning for Hackers and Bandit Algorithms for Website Optimization. +Tweets reflect my views only. | +
| Juan Miguel +Lavista | +Principal Data Scientist @ Microsoft Data Science Team | +
| Julia Evans | +Hacker - Pandas - Data Analyze | +
| Kenneth Cukier | +The Economist’s Data Editor and co-author of Big Data +(http://www.big-data-book.com/). | +
| Kevin Davenport | +Organizer of +https://www.meetup.com/San-Diego-Data-Science-R-Users-Group/ | +
| Kevin Markham | +Data science instructor, and founder of Data School | +
| Kim Rees | +Interactive data visualization and tools. Data flaneur. | +
| Kirk Borne | +DataScientist, PhD Astrophysicist, Top #BigData Influencer. | +
| Linda Regber | +Data storyteller, visualizations. | +
| Luis Rei | +PhD Student. Programming, Mobile, Web. Artificial Intelligence, +Intelligent Robotics Machine Learning, Data Mining, Natural Language +Processing, Data Science. | +
| Mark Stevenson | +Data Analytics Recruitment Specialist at Salt (@SaltJobs) Analytics - +Insight - Big Data - Data science | +
| Matt Harrison | +Opinions of full-stack Python guy, author, instructor, currently +playing Data Scientist. Occasional fathering, husbanding, organic +gardening. | +
| Matthew Russell | +Mining the Social Web. | +
| Mert Nuhoğlu | +Data Scientist at BizQualify, Developer | +
| Monica Rogati | +Data @ Jawbone. Turned data into stories & products at LinkedIn. +Text mining, applied machine learning, recommender systems. Ex-gamer, +ex-machine coder; namer. | +
| Noah Iliinsky | +Visualization & interaction designer. Practical cyclist. Author +of vis books: https://www.oreilly.com/pub/au/4419 | +
| Paul Miller | +Cloud Computing/ Big Data/ Open Data Analyst & Consultant. +Writer, Speaker & Moderator. Gigaom Research Analyst. | +
| Peter Skomoroch | +Creating intelligent systems to automate tasks & improve +decisions. Entrepreneur, ex-Principal Data Scientist @LinkedIn. Machine +Learning, ProductRei, Networks | +
| Prash Chan | +Solution Architect @ IBM, Master Data Management, Data Quality & +Data Governance Blogger. Data Science, Hadoop, Big Data & +Cloud. | +
| Quora Data +Science | +Quora’s data science topic | +
| R-Bloggers | +Tweet blog posts from the R blogosphere, data science conferences, +and (!) open jobs for data scientists. | +
| Rand Hindi | ++ |
| Randy Olson | +Computer scientist researching artificial intelligence. Data +tinkerer. Community leader for @DataIsBeautiful. #OpenScience +advocate. | +
| Recep Erol | +Data Science geek @ UALR | +
| Ryan Orban | +Data scientist, genetic origamist, hardware aficionado | +
| Sean J. Taylor | +Social Scientist. Hacker. Facebook Data Science Team. Keywords: +Experiments, Causal Inference, Statistics, Machine Learning, +Economics. | +
| Silvia K. Spiva | +#DataScience at Cisco | +
| Harsh B. Gupta | +Data Scientist at BBVA Compass | +
| Spencer Nelson | +Data nerd | +
| Talha Oz | +Enjoys ABM, SNA, DM, ML, NLP, HI, Python, Java. Top percentile +Kaggler/data scientist | +
| Tasos Skarlatidis | +Complex Event Processing, Big Data, Artificial Intelligence and +Machine Learning. Passionate about programming and open-source. | +
| Terry Timko | +InfoGov; Bigdata; Data as a Service; Data Science; Open, Social +& Business Data Convergence | +
| Tony Baer | +IT analyst with Ovum covering Big Data & data management with +some systems engineering thrown in. | +
| Tony Ojeda | +Data Scientist , Author , Entrepreneur. Co-founder @DataCommunityDC. +Founder @DistrictDataLab. #DataScience +#BigData #DataDC | +
| Vamshi Ambati | +Data Science @ PayPal. #NLP, #machinelearning; PhD, Carnegie Mellon +alumni (Blog: https://allthingsds.wordpress.com ) | +
| Wes McKinney | +Pandas (Python Data Analysis library). | +
| WileyEd | +Senior Manager - @Seagate Big Data Analytics @McKinsey Alum #BigData + +#Analytics Evangelist #Hadoop, #Cloud, #Digital, & #R +Enthusiast | +
| WNYC Data News Team | +The data news crew at @WNYC. Practicing data-driven journalism, +making it visual, and showing our work. | +
| Alexey Grigorev | +Data science author | +
| İlker Arslan | +Data science author. Shares mostly about Julia programming | +
| INEVITABLE | +AI & Data Science Start-up Company based in England, UK | +
Some data mining competition platforms
+ +| Preview | +Description | +
|---|---|
|
+Key +differences of a data scientist vs. data engineer | +
|
+A visual guide to Becoming a Data Scientist in 8 Steps by DataCamp (img) | +
|
+Mindmap on required skills (img) | +
|
+Swami Chandrasekaran made a Curriculum +via Metro map. | +
|
+by @kzawadz via twitter | +
|
+By Data Science +Central | +
|
+Data Science Wars: R vs Python | +
|
+How to select statistical or machine learning techniques | +
|
+Choosing the Right Estimator | +
|
+The Data Science Industry: Who Does What | +
|
+Data Science |
+
|
+Different Data Science Skills and Roles from this +article by Springboard | +
|
+A simple and friendly way of teaching your non-data +scientist/non-statistician colleagues how to avoid +mistakes with data. From Geckoboard’s Data Literacy +Lessons. | +
+====================================================================================================+
| ___ ___ | |
@@ -23,7 +22,7 @@ https://awesm.sh/
| |
+ ------------------------------------------ THANKS ------------------------------------------------ +
| |
-| List of awesome pages collected from awesome-awesome-awesome <1> awesome page. |
+| List of awesome pages collected from awesome-awesome-awesome ^1 awesome page. |
| |
| Big shoutout to @t3chnoboy and @sindresorhus for their meta meta (meta) awesome pages! |
| Also to @bradoyler, @emirjp, @erichs, @oyvindrobertsen, @bayandin, @jnv and @scooperma for their |
@@ -31,12 +30,12 @@ https://awesm.sh/
| |
| And of course to all the people curating such awesome link lists! You are awesome :) |
| |
-| Highly inspired by cheat.sh <2>. Give it a try. It's awesome too! |
+| Highly inspired by cheat.sh ^2. Give it a try. It's awesome too! |
| |
+ ------------------------------------------ LINKS ------------------------------------------------- +
| |
-| <1> https://github.com/t3chnoboy/awesome-awesome-awesome |
-| <2> https://cheat.sh |
+| ^1 https://github.com/t3chnoboy/awesome-awesome-awesome |
+| ^2 https://cheat.sh |
| |
+====================================================================================================+Awesome indexed:
diff --git a/lists/awesome-index b/lists/awesome-index
index 14cdde2..aec812f 160000
--- a/lists/awesome-index
+++ b/lists/awesome-index
@@ -1 +1 @@
-Subproject commit 14cdde274b69606a567bb26e31c616a1ea6bc7c8
+Subproject commit aec812f074be62f560a1f7ec30142b9496100259
diff --git a/readmes/datascience.md b/readmes/datascience.md
index 0658fb8..78df40a 100644
--- a/readmes/datascience.md
+++ b/readmes/datascience.md
@@ -1,1120 +1,10 @@
-
+awesome-data-science
+====================
-# AWESOME DATA SCIENCE
+A curated list of amazingly awesome open source data science resources.
-[](https://github.com/sindresorhus/awesome)
+Data Visualization
-**An open-source Data Science repository to learn and apply towards solving real world problems.**
+A JavaScript visualization library for HTML and SVG - http://d3js.org
-This is a shortcut path to start studying **Data Science**. Just follow the steps to answer the questions, "What is Data Science and what should I study to learn Data Science?"
-
-## Sponsors
-
-| Sponsor | Pitch |
-| --- | --- |
-| --- | Be the first to sponsor! `github@academic.io` |
-
-
-
-
-
-## Table of Contents
-
-- [What is Data Science?](#what-is-data-science)
-- [Where do I Start?](#where-do-i-start)
-- [Training Resources](#training-resources)
- - [Tutorials](#tutorials)
- - [Free Courses](#free-courses)
- - [Massively Open Online Courses](#moocs)
- - [Intensive Programs](#intensive-programs)
- - [Colleges](#colleges)
-- [The Data Science Toolbox](#the-data-science-toolbox)
- - [Algorithms](#algorithms)
- - [Supervised Learning](#supervised-learning)
- - [Unsupervised Learning](#unsupervised-learning)
- - [Semi-Supervised Learning](#semi-supervised-learning)
- - [Reinforcement Learning](#reinforcement-learning)
- - [Data Mining Algorithms](#data-mining-algorithms)
- - [Deep Learning Architectures](#deep-learning-architectures)
- - [General Machine Learning Packages](#general-machine-learning-packages)
- - [Deep Learning Packages](#deep-learning-packages)
- - [PyTorch Ecosystem](#pytorch-ecosystem)
- - [TensorFlow Ecosystem](#tensorflow-ecosystem)
- - [Keras Ecosystem](#keras-ecosystem)
- - [Visualization Tools](#visualization-tools)
- - [Miscellaneous Tools](#miscellaneous-tools)
-- [Literature and Media](#literature-and-media)
- - [Books](#books)
- - [Book Deals (Affiliated)](#book-deals-affiliated-)
- - [Journals, Publications, and Magazines](#journals-publications-and-magazines)
- - [Newsletters](#newsletters)
- - [Bloggers](#bloggers)
- - [Presentations](#presentations)
- - [Podcasts](#podcasts)
- - [YouTube Videos & Channels](#youtube-videos--channels)
-- [Socialize](#socialize)
- - [Facebook Accounts](#facebook-accounts)
- - [Twitter Accounts](#twitter-accounts)
- - [Telegram Channels](#telegram-channels)
- - [Slack Communities](#slack-communities)
- - [GitHub Groups](#github-groups)
- - [Data Science Competitions](#data-science-competitions)
-- [Fun](#fun)
- - [Infographics](#infographics)
- - [Datasets](#datasets)
- - [Comics](#comics)
-- [Other Awesome Lists](#other-awesome-lists)
- - [Hobby](#hobby)
-
-## What is Data Science?
-**[`^ back to top ^`](#awesome-data-science)**
-
-Data Science is one of the hottest topics on the Computer and Internet farmland nowadays. People have gathered data from applications and systems until today and now is the time to analyze them. The next steps are producing suggestions from the data and creating predictions about the future. [Here](https://www.quora.com/Data-Science/What-is-data-science) you can find the biggest question for **Data Science** and hundreds of answers from experts.
-
-
-| Link | Preview |
-| --- | --- |
-| [What is Data Science @ O'reilly](https://www.oreilly.com/ideas/what-is-data-science) | _Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: “here’s a lot of data, what can you make from it?”_ |
-| [What is Data Science @ Quora](https://www.quora.com/Data-Science/What-is-data-science) | Data Science is a combination of a number of aspects of Data such as Technology, Algorithm development, and data interference to study the data, analyse it, and find innovative solutions to difficult problems. Basically Data Science is all about Analysing data and driving for business growth by finding creative ways. |
-| [The sexiest job of 21st century](https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century) | _Data scientists today are akin to Wall Street “quants” of the 1980s and 1990s. In those days people with backgrounds in physics and math streamed to investment banks and hedge funds, where they could devise entirely new algorithms and data strategies. Then a variety of universities developed master’s programs in financial engineering, which churned out a second generation of talent that was more accessible to mainstream firms. The pattern was repeated later in the 1990s with search engineers, whose rarefied skills soon came to be taught in computer science programs._ |
-| [Wikipedia](https://en.wikipedia.org/wiki/Data_science) | _Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data._ |
-| [How to Become a Data Scientist](https://www.mastersindatascience.org/careers/data-scientist/) | _Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations._ |
-| [a very short history of #datascience](https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/) | _The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science. The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms._ |
-|[Software Development Resources for Data Scientists](https://www.rstudio.com/blog/software-development-resources-for-data-scientists/)|_Data scientists concentrate on making sense of data through exploratory analysis, statistics, and models. Software developers apply a separate set of knowledge with different tools. Although their focus may seem unrelated, data science teams can benefit from adopting software development best practices. Version control, automated testing, and other dev skills help create reproducible, production-ready code and tools._|
-
-## Where do I Start?
-**[`^ back to top ^`](#awesome-data-science)**
-
-While not strictly necessary, having a programming language is a crucial skill to be effective as a data scientist. Currently, the most popular language is _Python_, closely followed by _R_. Python is a general-purpose scripting language that sees applications in a wide variety of fields. R is a domain-specific language for statistics, which contains a lot of common statistics tools out of the box.
-
-[Python](https://python.org/) is by far the most popular language in science, due in no small part to the ease at which it can be used and the vibrant ecosystem of user-generated packages. To install packages, there are two main methods: Pip (invoked as `pip install`), the package manager that comes bundled with Python, and [Anaconda](https://www.anaconda.com) (invoked as `conda install`), a powerful package manager that can install packages for Python, R, and can download executables like Git.
-
-Unlike R, Python was not built from the ground up with data science in mind, but there are plenty of third party libraries to make up for this. A much more exhaustive list of packages can be found later in this document, but these four packages are a good set of choices to start your data science journey with: [Scikit-Learn](https://scikit-learn.org/stable/index.html) is a general-purpose data science package which implements the most popular algorithms - it also includes rich documentation, tutorials, and examples of the models it implements. Even if you prefer to write your own implementations, Scikit-Learn is a valuable reference to the nuts-and-bolts behind many of the common algorithms you'll find. With [Pandas](https://pandas.pydata.org/), one can collect and analyze their data into a convenient table format. [Numpy](https://numpy.org/) provides very fast tooling for mathematical operations, with a focus on vectors and matrices. [Seaborn](https://seaborn.pydata.org/), itself based on the [Matplotlib](https://matplotlib.org/) package, is a quick way to generate beautiful visualizations of your data, with many good defaults available out of the box, as well as a gallery showing how to produce many common visualizations of your data.
-
- When embarking on your journey to becoming a data scientist, the choice of language isn't particularly important, and both Python and R have their pros and cons. Pick a language you like, and check out one of the [Free courses](#free-courses) we've listed below!
-
-## Real World
-**[`^ back to top ^`](#awesome-data-science)**
-
-Data science is a powerful tool that is utilized in various fields to solve real-world problems by extracting insights and patterns from complex data.
-
-### Disaster
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [deprem-ml](https://huggingface.co/deprem-ml) [AYA: Açık Yazılım Ağı](https://linktr.ee/acikyazilimagi) (+25k developers) is trying to help disaster response using artificial intelligence. Everything is open-sourced [afet.org](https://afet.org).
-
-
-
-## Training Resources
-**[`^ back to top ^`](#awesome-data-science)**
-
-How do you learn data science? By doing data science, of course! Okay, okay - that might not be particularly helpful when you're first starting out. In this section, we've listed some learning resources, in rough order from least to greatest commitment - [Tutorials](#tutorials), [Massively Open Online Courses (MOOCs)](#moocs), [Intensive Programs](#intensive-programs), and [Colleges](#colleges).
-
-
-### Tutorials
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [1000 Data Science Projects](https://cloud.blobcity.com/#/ps/explore) you can run on the browser with IPython.
-- [#tidytuesday](https://github.com/rfordatascience/tidytuesday) A weekly data project aimed at the R ecosystem.
-- [Data science your way](https://github.com/jadianes/data-science-your-way)
-- [PySpark Cheatsheet](https://github.com/kevinschaich/pyspark-cheatsheet)
-- [Machine Learning, Data Science and Deep Learning with Python ](https://www.manning.com/livevideo/machine-learning-data-science-and-deep-learning-with-python)
-- [How To Label Data](https://www.lighttag.io/how-to-label-data/)
-- [Your Guide to Latent Dirichlet Allocation](https://medium.com/@lettier/how-does-lda-work-ill-explain-using-emoji-108abf40fa7d)
-- [Over 1000 Data Science Online Courses at Classpert Online Search Engine](https://classpert.com/search/data-science)
-- [Tutorials of source code from the book Genetic Algorithms with Python by Clinton Sheppard](https://github.com/handcraftsman/GeneticAlgorithmsWithPython)
-- [Tutorials to get started on signal processing for machine learning](https://github.com/jinglescode/python-signal-processing)
-- [Realtime deployment](https://www.microprediction.com/python-1) Tutorial on Python time-series model deployment.
-- [Python for Data Science: A Beginner’s Guide](https://learntocodewith.me/posts/python-for-data-science/)
-- [Minimum Viable Study Plan for Machine Learning Interviews](https://github.com/khangich/machine-learning-interview)
-- [Understand and Know Machine Learning Engineering by Building Solid Projects](http://mlzoomcamp.com/)
-- [12 free Data Science projects to practice Python and Pandas](https://www.datawars.io/articles/12-free-data-science-projects-to-practice-python-and-pandas)
-
-
-### Free Courses
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [Data Scientist with R](https://www.datacamp.com/tracks/data-scientist-with-r)
-- [Data Scientist with Python](https://www.datacamp.com/tracks/data-scientist-with-python)
-- [Genetic Algorithms OCW Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-1-introduction-and-scope/)
-- [AI Expert Roadmap](https://github.com/AMAI-GmbH/AI-Expert-Roadmap) - Roadmap to becoming an Artificial Intelligence Expert
-- [Convex Optimization](https://www.edx.org/course/convex-optimization) - Convex Optimization (basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory...)
-- [Skillcombo - Data Science](https://skillcombo.com/courses/development/data-science/free/) - 1000+ free online Data Science courses
-- [Learning from Data](https://home.work.caltech.edu/telecourse.html) - Introduction to machine learning covering basic theory, algorithms and applications
-- [Kaggle](https://www.kaggle.com/learn) - Learn about Data Science, Machine Learning, Python etc
-- [ML Observability Fundamentals](https://arize.com/ml-observability-fundamentals/) - Learn how to monitor and root-cause production ML issues.
-- [Weights & Biases Effective MLOps: Model Development](https://www.wandb.courses/courses/effective-mlops-model-development) - Free Course and Certification for building an end-to-end machine using W&B
-- [Python for Machine Learning](https://globalaihub.com/courses/introduction-to-python-the-road-to-machine-learning/) - Start your journey to machine learning with Python, one of the most powerful programming languages.
-- [Python for Data Science by Scaler](https://www.scaler.com/topics/course/python-for-data-science/) - This course is designed to empower beginners with the essential skills to excel in today's data-driven world. The comprehensive curriculum will give you a solid foundation in statistics, programming, data visualization, and machine learning.
-- [MLSys-NYU-2022](https://github.com/jacopotagliabue/MLSys-NYU-2022/tree/main) - Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022.
-- [Hands-on Train and Deploy ML](https://github.com/Paulescu/hands-on-train-and-deploy-ml) - A hands-on course to train and deploy a serverless API that predicts crypto prices.
-- [LLMOps: Building Real-World Applications With Large Language Models](https://www.comet.com/site/llm-course/) - Learn to build modern software with LLMs using the newest tools and techniques in the field.
-
-
-### MOOC's
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [Coursera Introduction to Data Science](https://www.coursera.org/specializations/data-science)
-- [Data Science - 9 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/jhu-data-science)
-- [Data Mining - 5 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/data-mining)
-- [Machine Learning – 5 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/machine-learning)
-- [CS 109 Data Science](https://cs109.github.io/2015/)
-- [OpenIntro](https://www.openintro.org/)
-- [CS 171 Visualization](https://www.cs171.org/#!index.md)
-- [Process Mining: Data science in Action](https://www.coursera.org/learn/process-mining)
-- [Oxford Deep Learning](https://www.cs.ox.ac.uk/projects/DeepLearn/)
-- [Oxford Deep Learning - video](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu)
-- [Oxford Machine Learning](https://www.cs.ox.ac.uk/research/ai_ml/index.html)
-- [UBC Machine Learning - video](https://www.cs.ubc.ca/~nando/540-2013/lectures.html)
-- [Data Science Specialization](https://github.com/DataScienceSpecialization/courses)
-- [Coursera Big Data Specialization](https://www.coursera.org/specializations/big-data)
-- [Statistical Thinking for Data Science and Analytics by Edx](https://www.edx.org/course/statistical-thinking-for-data-science-and-analytic)
-- [Cognitive Class AI by IBM](https://cognitiveclass.ai/)
-- [Udacity - Deep Learning](https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187)
-- [Keras in Motion](https://www.manning.com/livevideo/keras-in-motion)
-- [Microsoft Professional Program for Data Science](https://academy.microsoft.com/en-us/professional-program/tracks/data-science/)
-- [COMP3222/COMP6246 - Machine Learning Technologies](https://tdgunes.com/COMP6246-2019Fall/)
-- [CS 231 - Convolutional Neural Networks for Visual Recognition](https://cs231n.github.io/)
-- [Coursera Tensorflow in practice](https://www.coursera.org/professional-certificates/tensorflow-in-practice)
-- [Coursera Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning)
-- [365 Data Science Course](https://365datascience.com/)
-- [Coursera Natural Language Processing Specialization](https://www.coursera.org/specializations/natural-language-processing)
-- [Coursera GAN Specialization](https://www.coursera.org/specializations/generative-adversarial-networks-gans)
-- [Codecademy's Data Science](https://www.codecademy.com/learn/paths/data-science)
-- [Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/) - Linear Algebra course by Gilbert Strang
-- [A 2020 Vision of Linear Algebra (G. Strang)](https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-linear-algebra-spring-2020/)
-- [Python for Data Science Foundation Course](https://intellipaat.com/academy/course/python-for-data-science-free-training/)
-- [Data Science: Statistics & Machine Learning](https://www.coursera.org/specializations/data-science-statistics-machine-learning)
-- [Machine Learning Engineering for Production (MLOps)](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops)
-- [Recommender Systems Specialization from University of Minnesota](https://www.coursera.org/specializations/recommender-systems) is an intermediate/advanced level specialization focused on Recommender System on the Coursera platform.
-- [Stanford Artificial Intelligence Professional Program](https://online.stanford.edu/programs/artificial-intelligence-professional-program)
-- [Data Scientist with Python](https://app.datacamp.com/learn/career-tracks/data-scientist-with-python)
-- [Programming with Julia](https://www.udemy.com/course/programming-with-julia/)
-- [Scaler Data Science & Machine Learning Program](https://www.scaler.com/data-science-course/)
-
-
-
-### Intensive Programs
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [S2DS](https://www.s2ds.org/)
-
-
-### Colleges
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [A list of colleges and universities offering degrees in data science.](https://github.com/ryanswanstrom/awesome-datascience-colleges)
-- [Data Science Degree @ Berkeley](https://ischoolonline.berkeley.edu/data-science/)
-- [Data Science Degree @ UVA](https://datascience.virginia.edu/)
-- [Data Science Degree @ Wisconsin](https://datasciencedegree.wisconsin.edu/)
-- [BS in Data Science & Applications](https://study.iitm.ac.in/ds/)
-- [MS in Computer Information Systems @ Boston University](https://www.bu.edu/online/programs/graduate-programs/computer-information-systems-masters-degree/)
-- [MS in Business Analytics @ ASU Online](https://asuonline.asu.edu/online-degree-programs/graduate/master-science-business-analytics/)
-- [MS in Applied Data Science @ Syracuse](https://ischool.syr.edu/academics/applied-data-science-masters-degree/)
-- [M.S. Management & Data Science @ Leuphana](https://www.leuphana.de/en/graduate-school/masters-programmes/management-data-science.html)
-- [Master of Data Science @ Melbourne University](https://study.unimelb.edu.au/find/courses/graduate/master-of-data-science/#overview)
-- [Msc in Data Science @ The University of Edinburgh](https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=902)
-- [Master of Management Analytics @ Queen's University](https://smith.queensu.ca/grad_studies/mma/index.php)
-- [Master of Data Science @ Illinois Institute of Technology](https://www.iit.edu/academics/programs/data-science-mas)
-- [Master of Applied Data Science @ The University of Michigan](https://www.si.umich.edu/programs/master-applied-data-science-online)
-- [Master Data Science and Artificial Intelligence @ Eindhoven University of Technology](https://www.tue.nl/en/education/graduate-school/master-data-science-and-artificial-intelligence/)
-- [Master's Degree in Data Science and Computer Engineering @ University of Granada](https://masteres.ugr.es/datcom/)
-
-## The Data Science Toolbox
-**[`^ back to top ^`](#awesome-data-science)**
-
-This section is a collection of packages, tools, algorithms, and other useful items in the data science world.
-
-### Algorithms
-**[`^ back to top ^`](#awesome-data-science)**
-
-These are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it.
-
-#### Three kinds of Machine Learning Systems
-
-- Based on training with human supervision
-- Based on learning incrementally on fly
-- Based on data points comparison and pattern detection
-
-#### Supervised Learning
-
-- [Regression](https://en.wikipedia.org/wiki/Regression)
-- [Linear Regression](https://en.wikipedia.org/wiki/Linear_regression)
-- [Ordinary Least Squares](https://en.wikipedia.org/wiki/Ordinary_least_squares)
-- [Logistic Regression](https://en.wikipedia.org/wiki/Logistic_regression)
-- [Stepwise Regression](https://en.wikipedia.org/wiki/Stepwise_regression)
-- [Multivariate Adaptive Regression Splines](https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_spline)
-- [Softmax Regression](https://d2l.ai/chapter_linear-classification/softmax-regression.html)
-- [Locally Estimated Scatterplot Smoothing](https://en.wikipedia.org/wiki/Local_regression)
-- Classification
- - [k-nearest neighbor](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)
- - [Support Vector Machines](https://en.wikipedia.org/wiki/Support_vector_machine)
- - [Decision Trees](https://en.wikipedia.org/wiki/Decision_tree)
- - [ID3 algorithm](https://en.wikipedia.org/wiki/ID3_algorithm)
- - [C4.5 algorithm](https://en.wikipedia.org/wiki/C4.5_algorithm)
-- [Ensemble Learning](https://scikit-learn.org/stable/modules/ensemble.html)
- - [Boosting](https://en.wikipedia.org/wiki/Boosting_(machine_learning))
- - [Stacking](https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python)
- - [Bagging](https://en.wikipedia.org/wiki/Bootstrap_aggregating)
- - [Random Forest](https://en.wikipedia.org/wiki/Random_forest)
- - [AdaBoost](https://en.wikipedia.org/wiki/AdaBoost)
-
-#### Unsupervised Learning
-- [Clustering](https://scikit-learn.org/stable/modules/clustering.html#clustering)
- - [Hierchical clustering](https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering)
- - [k-means](https://scikit-learn.org/stable/modules/clustering.html#k-means)
- - [Density-based clustering](https://scikit-learn.org/stable/modules/clustering.html#dbscan)
- - [Fuzzy clustering](https://en.wikipedia.org/wiki/Fuzzy_clustering)
- - [Mixture models](https://en.wikipedia.org/wiki/Mixture_model)
-- [Dimension Reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction)
- - [Principal Component Analysis (PCA)](https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca)
- - [t-SNE; t-distributed Stochastic Neighbor Embedding](https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca)
- - [Factor Analysis](https://scikit-learn.org/stable/modules/decomposition.html#factor-analysis)
- - [Latent Dirichlet Allocation (LDA)](https://scikit-learn.org/stable/modules/decomposition.html#latent-dirichlet-allocation-lda)
-- [Neural Networks](https://en.wikipedia.org/wiki/Neural_network)
-- [Self-organizing map](https://en.wikipedia.org/wiki/Self-organizing_map)
-- [Adaptive resonance theory](https://en.wikipedia.org/wiki/Adaptive_resonance_theory)
-- [Hidden Markov Models (HMM)](https://en.wikipedia.org/wiki/Hidden_Markov_model)
-
-#### Semi-Supervised Learning
-
-- S3VM
-- [Clustering](https://en.wikipedia.org/wiki/Weak_supervision#Cluster_assumption)
-- [Generative models](https://en.wikipedia.org/wiki/Weak_supervision#Generative_models)
-- [Low-density separation](https://en.wikipedia.org/wiki/Weak_supervision#Low-density_separation)
-- [Laplacian regularization](https://en.wikipedia.org/wiki/Weak_supervision#Laplacian_regularization)
-- [Heuristic approaches](https://en.wikipedia.org/wiki/Weak_supervision#Heuristic_approaches)
-
-#### Reinforcement Learning
-
-- [Q Learning](https://en.wikipedia.org/wiki/Q-learning)
-- [SARSA (State-Action-Reward-State-Action) algorithm](https://en.wikipedia.org/wiki/State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action)
-- [Temporal difference learning](https://en.wikipedia.org/wiki/Temporal_difference_learning#:~:text=Temporal%20difference%20(TD)%20learning%20refers,estimate%20of%20the%20value%20function.)
-
-#### Data Mining Algorithms
-
-- [C4.5](https://en.wikipedia.org/wiki/C4.5_algorithm)
-- [k-Means](https://en.wikipedia.org/wiki/K-means_clustering)
-- [SVM (Support Vector Machine)](https://en.wikipedia.org/wiki/Support_vector_machine)
-- [Apriori](https://en.wikipedia.org/wiki/Apriori_algorithm)
-- [EM (Expectation-Maximization)](https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
-- [PageRank](https://en.wikipedia.org/wiki/PageRank)
-- [AdaBoost](https://en.wikipedia.org/wiki/AdaBoost)
-- [KNN (K-Nearest Neighbors)](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)
-- [Naive Bayes](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
-- [CART (Classification and Regression Trees)](https://en.wikipedia.org/wiki/Decision_tree_learning)
-
-
-
-#### Deep Learning architectures
-
-- [Multilayer Perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron)
-- [Convolutional Neural Network (CNN)](https://en.wikipedia.org/wiki/Convolutional_neural_network)
-- [Recurrent Neural Network (RNN)](https://en.wikipedia.org/wiki/Recurrent_neural_network)
-- [Boltzmann Machines](https://en.wikipedia.org/wiki/Boltzmann_machine)
-- [Autoencoder](https://www.tensorflow.org/tutorials/generative/autoencoder)
-- [Generative Adversarial Network (GAN)](https://developers.google.com/machine-learning/gan/gan_structure)
-- [Self-Organized Maps](https://en.wikipedia.org/wiki/Self-organizing_map)
-- [Transformer](https://www.tensorflow.org/text/tutorials/transformer)
-- [Conditional Random Field (CRF)](https://towardsdatascience.com/conditional-random-fields-explained-e5b8256da776)
-
-### General Machine Learning Packages
-**[`^ back to top ^`](#awesome-data-science)**
-
-* [scikit-learn](https://scikit-learn.org/)
-* [scikit-multilearn](https://github.com/scikit-multilearn/scikit-multilearn)
-* [sklearn-expertsys](https://github.com/tmadl/sklearn-expertsys)
-* [scikit-feature](https://github.com/jundongl/scikit-feature)
-* [scikit-rebate](https://github.com/EpistasisLab/scikit-rebate)
-* [seqlearn](https://github.com/larsmans/seqlearn)
-* [sklearn-bayes](https://github.com/AmazaspShumik/sklearn-bayes)
-* [sklearn-crfsuite](https://github.com/TeamHG-Memex/sklearn-crfsuite)
-* [sklearn-deap](https://github.com/rsteca/sklearn-deap)
-* [sigopt_sklearn](https://github.com/sigopt/sigopt-sklearn)
-* [sklearn-evaluation](https://github.com/edublancas/sklearn-evaluation)
-* [scikit-image](https://github.com/scikit-image/scikit-image)
-* [scikit-opt](https://github.com/guofei9987/scikit-opt)
-* [scikit-posthocs](https://github.com/maximtrp/scikit-posthocs)
-* [pystruct](https://github.com/pystruct/pystruct)
-* [Shogun](https://www.shogun-toolbox.org/)
-* [xLearn](https://github.com/aksnzhy/xlearn)
-* [cuML](https://github.com/rapidsai/cuml)
-* [causalml](https://github.com/uber/causalml)
-* [mlpack](https://github.com/mlpack/mlpack)
-* [MLxtend](https://github.com/rasbt/mlxtend)
-* [modAL](https://github.com/modAL-python/modAL)
-* [Sparkit-learn](https://github.com/lensacom/sparkit-learn)
-* [hyperlearn](https://github.com/danielhanchen/hyperlearn)
-* [dlib](https://github.com/davisking/dlib)
-* [imodels](https://github.com/csinva/imodels)
-* [RuleFit](https://github.com/christophM/rulefit)
-* [pyGAM](https://github.com/dswah/pyGAM)
-* [Deepchecks](https://github.com/deepchecks/deepchecks)
-* [scikit-survival](https://scikit-survival.readthedocs.io/en/stable)
-
-### Deep Learning Packages
-
-#### PyTorch Ecosystem
-* [PyTorch](https://github.com/pytorch/pytorch)
-* [torchvision](https://github.com/pytorch/vision)
-* [torchtext](https://github.com/pytorch/text)
-* [torchaudio](https://github.com/pytorch/audio)
-* [ignite](https://github.com/pytorch/ignite)
-* [PyTorchNet](https://github.com/pytorch/tnt)
-* [PyToune](https://github.com/GRAAL-Research/poutyne)
-* [skorch](https://github.com/skorch-dev/skorch)
-* [PyVarInf](https://github.com/ctallec/pyvarinf)
-* [pytorch_geometric](https://github.com/pyg-team/pytorch_geometric)
-* [GPyTorch](https://github.com/cornellius-gp/gpytorch)
-* [pyro](https://github.com/pyro-ppl/pyro)
-* [Catalyst](https://github.com/catalyst-team/catalyst)
-* [pytorch_tabular](https://github.com/manujosephv/pytorch_tabular)
-* [Yolov3](https://github.com/ultralytics/yolov3)
-* [Yolov5](https://github.com/ultralytics/yolov5)
-* [Yolov8](https://github.com/ultralytics/ultralytics)
-
-#### TensorFlow Ecosystem
-* [TensorFlow](https://github.com/tensorflow/tensorflow)
-* [TensorLayer](https://github.com/tensorlayer/TensorLayer)
-* [TFLearn](https://github.com/tflearn/tflearn)
-* [Sonnet](https://github.com/deepmind/sonnet)
-* [tensorpack](https://github.com/tensorpack/tensorpack)
-* [TRFL](https://github.com/deepmind/trfl)
-* [Polyaxon](https://github.com/polyaxon/polyaxon)
-* [NeuPy](https://github.com/itdxer/neupy)
-* [tfdeploy](https://github.com/riga/tfdeploy)
-* [tensorflow-upstream](https://github.com/ROCmSoftwarePlatform/tensorflow-upstream)
-* [TensorFlow Fold](https://github.com/tensorflow/fold)
-* [tensorlm](https://github.com/batzner/tensorlm)
-* [TensorLight](https://github.com/bsautermeister/tensorlight)
-* [Mesh TensorFlow](https://github.com/tensorflow/mesh)
-* [Ludwig](https://github.com/ludwig-ai/ludwig)
-* [TF-Agents](https://github.com/tensorflow/agents)
-* [TensorForce](https://github.com/tensorforce/tensorforce)
-
-#### Keras Ecosystem
-
-* [Keras](https://keras.io)
-* [keras-contrib](https://github.com/keras-team/keras-contrib)
-* [Hyperas](https://github.com/maxpumperla/hyperas)
-* [Elephas](https://github.com/maxpumperla/elephas)
-* [Hera](https://github.com/keplr-io/hera)
-* [Spektral](https://github.com/danielegrattarola/spektral)
-* [qkeras](https://github.com/google/qkeras)
-* [keras-rl](https://github.com/keras-rl/keras-rl)
-* [Talos](https://github.com/autonomio/talos)
-
-#### Visualization Tools
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [altair](https://altair-viz.github.io/)
-- [addepar](https://opensource.addepar.com/ember-charts/#/overview)
-- [amcharts](https://www.amcharts.com/)
-- [anychart](https://www.anychart.com/)
-- [bokeh](https://bokeh.org/)
-- [Comet](https://www.comet.com/site/products/ml-experiment-tracking/?utm_source=awesome-datascience)
-- [slemma](https://slemma.com/)
-- [cartodb](https://cartodb.github.io/odyssey.js/)
-- [Cube](https://square.github.io/cube/)
-- [d3plus](https://d3plus.org/)
-- [Data-Driven Documents(D3js)](https://d3js.org/)
-- [dygraphs](https://dygraphs.com/)
-- [ECharts](https://echarts.baidu.com/index-en.html)
-- [exhibit](https://www.simile-widgets.org/exhibit/)
-- [gephi](https://gephi.org/)
-- [ggplot2](https://ggplot2.tidyverse.org/)
-- [Glue](http://docs.glueviz.org/en/latest/index.html)
-- [Google Chart Gallery](https://developers.google.com/chart/interactive/docs/gallery)
-- [highcarts](https://www.highcharts.com/)
-- [import.io](https://www.import.io/)
-- [jqplot](https://www.jqplot.com/)
-- [Matplotlib](https://matplotlib.org/)
-- [nvd3](https://nvd3.org/)
-- [Netron](https://github.com/lutzroeder/netron)
-- [Openrefine](https://openrefine.org/)
-- [plot.ly](https://plot.ly/)
-- [raw](https://rawgraphs.io)
-- [Resseract Lite](https://github.com/abistarun/resseract-lite)
-- [Seaborn](https://seaborn.pydata.org/)
-- [techanjs](https://techanjs.org/)
-- [Timeline](https://timeline.knightlab.com/)
-- [variancecharts](https://variancecharts.com/index.html)
-- [vida](https://vida.io/)
-- [vizzu](https://github.com/vizzuhq/vizzu-lib)
-- [Wrangler](http://vis.stanford.edu/wrangler/)
-- [r2d3](https://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
-- [NetworkX](https://networkx.org/)
-- [Redash](https://redash.io/)
-- [C3](https://c3js.org/)
-- [TensorWatch](https://github.com/microsoft/tensorwatch)
-- [geomap](https://pypi.org/project/geomap/)
-
-### Miscellaneous Tools
-**[`^ back to top ^`](#awesome-data-science)**
-
-| Link | Description |
-| --- | --- |
-| [The Data Science Lifecycle Process](https://github.com/dslp/dslp) | The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo |
-| [Data Science Lifecycle Template Repo](https://github.com/dslp/dslp-repo-template) | Template repository for data science lifecycle project |
-| [RexMex](https://github.com/AstraZeneca/rexmex) | A general purpose recommender metrics library for fair evaluation. |
-| [ChemicalX](https://github.com/AstraZeneca/chemicalx) | A PyTorch based deep learning library for drug pair scoring. |
-| [PyTorch Geometric Temporal](https://github.com/benedekrozemberczki/pytorch_geometric_temporal) | Representation learning on dynamic graphs. |
-| [Little Ball of Fur](https://github.com/benedekrozemberczki/littleballoffur) | A graph sampling library for NetworkX with a Scikit-Learn like API. |
-| [Karate Club](https://github.com/benedekrozemberczki/karateclub) | An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API. |
-| [ML Workspace](https://github.com/ml-tooling/ml-workspace) | All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a Docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code) |
-| [Neptune.ai](https://neptune.ai) | Community-friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility. |
-| [steppy](https://github.com/minerva-ml/steppy) | Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces very simple interface that enables clean machine learning pipeline design. |
-| [steppy-toolkit](https://github.com/minerva-ml/steppy-toolkit) | Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective. |
-| [Datalab from Google](https://cloud.google.com/datalab/docs/) | easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively. |
-| [Hortonworks Sandbox](https://www.cloudera.com/downloads/hortonworks-sandbox.html) | is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials. |
-| [R](https://www.r-project.org/) | is a free software environment for statistical computing and graphics. |
-| [Tidyverse](https://www.tidyverse.org/) | is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. |
-| [RStudio](https://www.rstudio.com) | IDE – powerful user interface for R. It’s free and open source, and works on Windows, Mac, and Linux. |
-| [Python - Pandas - Anaconda](https://www.anaconda.com) | Completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing |
-| [Pandas GUI](https://github.com/adrotog/PandasGUI) | Pandas GUI |
-| [Scikit-Learn](https://scikit-learn.org/stable/) | Machine Learning in Python |
-| [NumPy](https://numpy.org/) | NumPy is fundamental for scientific computing with Python. It supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays. |
-| [Vaex](https://vaex.io/) | Vaex is a Python library that allows you to visualize large datasets and calculate statistics at high speeds. |
-| [SciPy](https://scipy.org/) | SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization. |
-| [Data Science Toolbox](https://www.coursera.org/learn/data-scientists-tools) | Coursera Course |
-| [Data Science Toolbox](https://datasciencetoolbox.org/) | Blog |
-| [Wolfram Data Science Platform](https://www.wolfram.com/data-science-platform/) | Take numerical, textual, image, GIS or other data and give it the Wolfram treatment, carrying out a full spectrum of data science analysis and visualization and automatically generate rich interactive reports—all powered by the revolutionary knowledge-based Wolfram Language. |
-| [Datadog](https://www.datadoghq.com/) | Solutions, code, and devops for high-scale data science. |
-| [Variance](https://variancecharts.com/) | Build powerful data visualizations for the web without writing JavaScript |
-| [Kite Development Kit](https://kitesdk.org/docs/current/index.html) | The Kite Software Development Kit (Apache License, Version 2.0), or Kite for short, is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem. |
-| [Domino Data Labs](https://www.dominodatalab.com) | Run, scale, share, and deploy your models — without any infrastructure or setup. |
-| [Apache Flink](https://flink.apache.org/) | A platform for efficient, distributed, general-purpose data processing. |
-| [Apache Hama](https://hama.apache.org/) | Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce. |
-| [Weka](https://www.cs.waikato.ac.nz/ml/weka/) | Weka is a collection of machine learning algorithms for data mining tasks. |
-| [Octave](https://www.gnu.org/software/octave/) | GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab) |
-| [Apache Spark](https://spark.apache.org/) | Lightning-fast cluster computing |
-| [Hydrosphere Mist](https://github.com/Hydrospheredata/mist) | a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services. |
-| [Data Mechanics](https://www.datamechanics.co) | A data science and engineering platform making Apache Spark more developer-friendly and cost-effective. |
-| [Caffe](https://caffe.berkeleyvision.org/) | Deep Learning Framework |
-| [Torch](https://torch.ch/) | A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT |
-| [Nervana's python based Deep Learning Framework](https://github.com/NervanaSystems/neon) | Intel® Nervana™ reference deep learning framework committed to best performance on all hardware. |
-| [Skale](https://github.com/skale-me/skale) | High performance distributed data processing in NodeJS |
-| [Aerosolve](https://airbnb.io/aerosolve/) | A machine learning package built for humans. |
-| [Intel framework](https://github.com/intel/idlf) | Intel® Deep Learning Framework |
-| [Datawrapper](https://www.datawrapper.de/) | An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at [github.com](https://github.com/datawrapper/datawrapper) |
-| [Tensor Flow](https://www.tensorflow.org/) | TensorFlow is an Open Source Software Library for Machine Intelligence |
-| [Natural Language Toolkit](https://www.nltk.org/) | An introductory yet powerful toolkit for natural language processing and classification |
-| [Annotation Lab](https://www.johnsnowlabs.com/annotation-lab/) | Free End-to-End No-Code platform for text annotation and DL model training/tuning. Out-of-the-box support for Named Entity Recognition, Classification, Relation extraction and Assertion Status Spark NLP models. Unlimited support for users, teams, projects, documents. |
-| [nlp-toolkit for node.js](https://www.npmjs.com/package/nlp-toolkit) | This module covers some basic nlp principles and implementations. The main focus is performance. When we deal with sample or training data in nlp, we quickly run out of memory. Therefore every implementation in this module is written as stream to only hold that data in memory that is currently processed at any step. |
-| [Julia](https://julialang.org) | high-level, high-performance dynamic programming language for technical computing |
-| [IJulia](https://github.com/JuliaLang/IJulia.jl) | a Julia-language backend combined with the Jupyter interactive environment |
-| [Apache Zeppelin](https://zeppelin.apache.org/) | Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more |
-| [Featuretools](https://github.com/alteryx/featuretools) | An open source framework for automated feature engineering written in python |
-| [Optimus](https://github.com/hi-primus/optimus) | Cleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend. |
-| [Albumentations](https://github.com/albumentations-team/albumentations) | А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, and detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops. |
-| [DVC](https://github.com/iterative/dvc) | An open-source data science version control system. It helps track, organize and make data science projects reproducible. In its very basic scenario it helps version control and share large data and model files. |
-| [Lambdo](https://github.com/asavinov/lambdo) | is a workflow engine that significantly simplifies data analysis by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation. |
-| [Feast](https://github.com/feast-dev/feast) | A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving. |
-| [Polyaxon](https://github.com/polyaxon/polyaxon) | A platform for reproducible and scalable machine learning and deep learning. |
-| [LightTag](https://www.lighttag.io/) | Text Annotation Tool for teams |
-| [UBIAI](https://ubiai.tools) | Easy-to-use text annotation tool for teams with most comprehensive auto-annotation features. Supports NER, relations and document classification as well as OCR annotation for invoice labeling |
-| [Trains](https://github.com/allegroai/clearml) | Auto-Magical Experiment Manager, Version Control & DevOps for AI |
-| [Hopsworks](https://github.com/logicalclocks/hopsworks) | Open-source data-intensive machine learning platform with a feature store. Ingest and manage features for both online (MySQL Cluster) and offline (Apache Hive) access, train and serve models at scale. |
-| [MindsDB](https://github.com/mindsdb/mindsdb) | MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code. |
-| [Lightwood](https://github.com/mindsdb/lightwood) | A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with an objective to build predictive models with one line of code. |
-| [AWS Data Wrangler](https://github.com/awslabs/aws-data-wrangler) | An open-source Python package that extends the power of Pandas library to AWS connecting DataFrames and AWS data related services (Amazon Redshift, AWS Glue, Amazon Athena, Amazon EMR, etc). |
-| [Amazon Rekognition](https://aws.amazon.com/rekognition/) | AWS Rekognition is a service that lets developers working with Amazon Web Services add image analysis to their applications. Catalog assets, automate workflows, and extract meaning from your media and applications.|
-| [Amazon Textract](https://aws.amazon.com/textract/) | Automatically extract printed text, handwriting, and data from any document. |
-| [Amazon Lookout for Vision](https://aws.amazon.com/lookout-for-vision/) | Spot product defects using computer vision to automate quality inspection. Identify missing product components, vehicle and structure damage, and irregularities for comprehensive quality control.|
-| [Amazon CodeGuru](https://aws.amazon.com/codeguru/) | Automate code reviews and optimize application performance with ML-powered recommendations.|
-| [CML](https://github.com/iterative/cml) | An open source toolkit for using continuous integration in data science projects. Automatically train and test models in production-like environments with GitHub Actions & GitLab CI, and autogenerate visual reports on pull/merge requests. |
-| [Dask](https://dask.org/) | An open source Python library to painlessly transition your analytics code to distributed computing systems (Big Data) |
-| [Statsmodels](https://www.statsmodels.org/stable/index.html) | A Python-based inferential statistics, hypothesis testing and regression framework |
-| [Gensim](https://radimrehurek.com/gensim/) | An open-source library for topic modeling of natural language text |
-| [spaCy](https://spacy.io/) | A performant natural language processing toolkit |
-| [Grid Studio](https://github.com/ricklamers/gridstudio) | Grid studio is a web-based spreadsheet application with full integration of the Python programming language. |
-|[Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook)|Python Data Science Handbook: full text in Jupyter Notebooks|
-| [Shapley](https://github.com/benedekrozemberczki/shapley) | A data-driven framework to quantify the value of classifiers in a machine learning ensemble. |
-| [DAGsHub](https://dagshub.com) | A platform built on open source tools for data, model and pipeline management. |
-| [Deepnote](https://deepnote.com) | A new kind of data science notebook. Jupyter-compatible, with real-time collaboration and running in the cloud. |
-| [Valohai](https://valohai.com) | An MLOps platform that handles machine orchestration, automatic reproducibility and deployment. |
-| [PyMC3](https://docs.pymc.io/) | A Python Library for Probabalistic Programming (Bayesian Inference and Machine Learning) |
-| [PyStan](https://pypi.org/project/pystan/) | Python interface to Stan (Bayesian inference and modeling) |
-| [hmmlearn](https://pypi.org/project/hmmlearn/) | Unsupervised learning and inference of Hidden Markov Models |
-| [Chaos Genius](https://github.com/chaos-genius/chaos_genius/) | ML powered analytics engine for outlier/anomaly detection and root cause analysis |
-| [Nimblebox](https://nimblebox.ai/) | A full-stack MLOps platform designed to help data scientists and machine learning practitioners around the world discover, create, and launch multi-cloud apps from their web browser. |
-| [Towhee](https://github.com/towhee-io/towhee) | A Python library that helps you encode your unstructured data into embeddings. |
-| [LineaPy](https://github.com/LineaLabs/lineapy) | Ever been frustrated with cleaning up long, messy Jupyter notebooks? With LineaPy, an open source Python library, it takes as little as two lines of code to transform messy development code into production pipelines. |
-| [envd](https://github.com/tensorchord/envd) | 🏕️ machine learning development environment for data science and AI/ML engineering teams |
-| [Explore Data Science Libraries](https://kandi.openweaver.com/explore/data-science) | A search engine 🔎 tool to discover & find a curated list of popular & new libraries, top authors, trending project kits, discussions, tutorials & learning resources |
-| [MLEM](https://github.com/iterative/mlem) | 🐶 Version and deploy your ML models following GitOps principles |
-| [MLflow](https://mlflow.org/) | MLOps framework for managing ML models across their full lifecycle |
-| [cleanlab](https://github.com/cleanlab/cleanlab) | Python library for data-centric AI and automatically detecting various issues in ML datasets |
-| [AutoGluon](https://github.com/awslabs/autogluon) | AutoML to easily produce accurate predictions for image, text, tabular, time-series, and multi-modal data |
-| [Arize AI](https://arize.com/) | Arize AI community tier observability tool for monitoring machine learning models in production and root-causing issues such as data quality and performance drift. |
-| [Aureo.io](https://aureo.io) | Aureo.io is a low-code platform that focuses on building artificial intelligence. It provides users with the capability to create pipelines, automations and integrate them with artificial intelligence models – all with their basic data. |
-| [ERD Lab](https://www.erdlab.io/) | Free cloud based entity relationship diagram (ERD) tool made for developers.
-| [Arize-Phoenix](https://docs.arize.com/phoenix) | MLOps in a notebook - uncover insights, surface problems, monitor, and fine tune your models. |
-| [Comet](https://github.com/comet-ml/comet-examples) | An MLOps platform with experiment tracking, model production management, a model registry, and full data lineage to support your ML workflow from training straight through to production. |
-| [CometLLM](https://github.com/comet-ml/comet-llm) | Log, track, visualize, and search your LLM prompts and chains in one easy-to-use, 100% open-source tool. |
-| [Synthical](https://synthical.com) | AI-powered collaborative environment for research. Find relevant papers, create collections to manage bibliography, and summarize content — all in one place |
-| [teeplot](https://github.com/mmore500/teeplot) | Workflow tool to automatically organize data visualization output |
-
-
-## Literature and Media
-**[`^ back to top ^`](#awesome-data-science)**
-
-This section includes some additional reading material, channels to watch, and talks to listen to.
-
-### Books
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [Data Science From Scratch: First Principles with Python](https://www.amazon.com/Data-Science-Scratch-Principles-Python-dp-1492041130/dp/1492041130/ref=dp_ob_title_bk)
-- [Artificial Intelligence with Python - Tutorialspoint](https://www.tutorialspoint.com/artificial_intelligence_with_python/artificial_intelligence_with_python_tutorial.pdf)
-- [Machine Learning from Scratch](https://dafriedman97.github.io/mlbook/content/introduction.html)
-- [Probabilistic Machine Learning: An Introduction](https://probml.github.io/pml-book/book1.html)
-- [A Comprehensive Guide to Machine Learning](https://www.eecs189.org/static/resources/comprehensive-guide.pdf)
-- [How to Lead in Data Science](https://www.manning.com/books/how-to-lead-in-data-science) - Early Access
-- [Fighting Churn With Data](https://www.manning.com/books/fighting-churn-with-data)
-- [Data Science at Scale with Python and Dask](https://www.manning.com/books/data-science-with-python-and-dask)
-- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)
-- [The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists](https://www.thedatasciencehandbook.com/)
-- [Think Like a Data Scientist](https://www.manning.com/books/think-like-a-data-scientist)
-- [Introducing Data Science](https://www.manning.com/books/introducing-data-science)
-- [Practical Data Science with R](https://www.manning.com/books/practical-data-science-with-r)
-- [Everyday Data Science](https://www.amazon.com/dp/B08TZ1MT3W/ref=cm_sw_r_cp_apa_fabc_a0ceGbWECF9A8) & [(cheaper PDF version)](https://gum.co/everydaydata)
-- [Exploring Data Science](https://www.manning.com/books/exploring-data-science) - free eBook sampler
-- [Exploring the Data Jungle](https://www.manning.com/books/exploring-the-data-jungle) - free eBook sampler
-- [Classic Computer Science Problems in Python](https://www.manning.com/books/classic-computer-science-problems-in-python)
-- [Math for Programmers](https://www.manning.com/books/math-for-programmers) Early access
-- [R in Action, Third Edition](https://www.manning.com/books/r-in-action-third-edition) Early Access
-- [Data Science Bookcamp](https://www.manning.com/books/data-science-bookcamp) Early access
-- [Data Science Thinking: The Next Scientific, Technological and Economic Revolution](https://www.springer.com/gp/book/9783319950914)
-- [Applied Data Science: Lessons Learned for the Data-Driven Business](https://www.springer.com/gp/book/9783030118204)
-- [The Data Science Handbook](https://www.amazon.com/Data-Science-Handbook-Field-Cady/dp/1119092949)
-- [Essential Natural Language Processing](https://www.manning.com/books/getting-started-with-natural-language-processing) - Early access
-- [Mining Massive Datasets](https://www.mmds.org/) - free e-book comprehended by an online course
-- [Pandas in Action](https://www.manning.com/books/pandas-in-action) - Early access
-- [Genetic Algorithms and Genetic Programming](https://www.taylorfrancis.com/books/9780429141973)
-- [Advances in Evolutionary Algorithms](https://www.intechopen.com/books/advances_in_evolutionary_algorithms) - Free Download
-- [Genetic Programming: New Approaches and Successful Applications](https://www.intechopen.com/books/genetic-programming-new-approaches-and-successful-applications) - Free Download
-- [Evolutionary Algorithms](https://www.intechopen.com/books/evolutionary-algorithms) - Free Download
-- [Advances in Genetic Programming, Vol. 3](https://www.cs.bham.ac.uk/~wbl/aigp3/) - Free Download
-- [Global Optimization Algorithms: Theory and Application](https://www.it-weise.de/projects/book.pdf) - Free Download
-- [Genetic Algorithms and Evolutionary Computation](https://www.talkorigins.org/faqs/genalg/genalg.html) - Free Download
-- [Convex Optimization](https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf) - Convex Optimization book by Stephen Boyd - Free Download
-- [Data Analysis with Python and PySpark](https://www.manning.com/books/data-analysis-with-python-and-pyspark) - Early Access
-- [R for Data Science](https://r4ds.had.co.nz/)
-- [Build a Career in Data Science](https://www.manning.com/books/build-a-career-in-data-science)
-- [Machine Learning Bookcamp](https://mlbookcamp.com/) - Early access
-- [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)
-- [Effective Data Science Infrastructure](https://www.manning.com/books/effective-data-science-infrastructure)
-- [Practical MLOps: How to Get Ready for Production Models](https://valohai.com/mlops-ebook/)
-- [Data Analysis with Python and PySpark](https://www.manning.com/books/data-analysis-with-python-and-pyspark)
-- [Regression, a Friendly guide](https://www.manning.com/books/regression-a-friendly-guide) - Early Access
-- [Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing](https://www.oreilly.com/library/view/streaming-systems/9781491983867/)
-- [Data Science at the Command Line: Facing the Future with Time-Tested Tools](https://www.oreilly.com/library/view/data-science-at/9781491947845/)
-- [Machine Learning - CIn UFPE](https://www.cin.ufpe.br/~cavmj/Machine%20-%20Learning%20-%20Tom%20Mitchell.pdf)
-- [Machine Learning with Python - Tutorialspoint](https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_tutorial.pdf)
-- [Deep Learning](https://www.deeplearningbook.org/)
-- [Designing Cloud Data Platforms](https://www.manning.com/books/designing-cloud-data-platforms) - Early Access
-- [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)
-- [The Elements of Statistical Learning: Data Mining, Inference, and Prediction](https://hastie.su.domains/ElemStatLearn/)
-- [Deep Learning with PyTorch](https://www.simonandschuster.com/books/Deep-Learning-with-PyTorch/Eli-Stevens/9781617295263)
-- [Neural Networks and Deep Learning](https://neuralnetworksanddeeplearning.com)
-- [Deep Learning Cookbook](https://www.oreilly.com/library/view/deep-learning-cookbook/9781491995839/)
-- [Introduction to Machine Learning with Python](https://www.oreilly.com/library/view/introduction-to-machine/9781449369880/)
-- [Artificial Intelligence: Foundations of Computational Agents, 2nd Edition](https://artint.info/index.html) - Free HTML version
-- [The Quest for Artificial Intelligence: A History of Ideas and Achievements](https://ai.stanford.edu/~nilsson/QAI/qai.pdf) - Free Download
-- [Graph Algorithms for Data Science](https://www.manning.com/books/graph-algorithms-for-data-science) - Early Access
-- [Data Mesh in Action](https://www.manning.com/books/data-mesh-in-action) - Early Access
-- [Julia for Data Analysis](https://www.manning.com/books/julia-for-data-analysis) - Early Access
-- [Casual Inference for Data Science](https://www.manning.com/books/julia-for-data-analysis) - Early Access
-- [Regular Expression Puzzles and AI Coding Assistants](https://www.manning.com/books/regular-expression-puzzles-and-ai-coding-assistants) by David Mertz
-- [Dive into Deep Learning](https://d2l.ai/)
-- [Data for All](https://www.manning.com/books/data-for-all)
-- [Interpretable Machine Learning: A Guide for Making Black Box Models Explainable](https://christophm.github.io/interpretable-ml-book/) - Free GitHub version
-- [Foundations of Data Science](https://www.cs.cornell.edu/jeh/book.pdf) Free Download
-- [Comet for DataScience: Enhance your ability to manage and optimize the life cycle of your data science project](https://www.amazon.com/Comet-Data-Science-Enhance-optimize/dp/1801814430)
-- [Software Engineering for Data Scientists](https://www.manning.com/books/software-engineering-for-data-scientists) - Early Access
-- [Julia for Data Science](https://www.manning.com/books/julia-for-data-science) - Early Access
-- [An Introduction to Statistical Learning](https://www.statlearning.com/) - Download Page
-- [Machine Learning For Absolute Beginners](https://www.amazon.in/Machine-Learning-Absolute-Beginners-Introduction-ebook/dp/B07335JNW1)
-
-#### Book Deals (Affiliated) 🛍
-
-- [eBook sale - Save up to 45% on eBooks!](https://www.manning.com/?utm_source=mikrobusiness&utm_medium=affiliate&utm_campaign=ebook_sale_8_8_22)
-
-- [Causal Machine Learning](https://www.manning.com/books/causal-machine-learning?utm_source=mikrobusiness&utm_medium=affiliate&utm_campaign=book_ness_causal_7_26_22&a_aid=mikrobusiness&a_bid=43a2198b
-)
-- [Managing ML Projects](https://www.manning.com/books/managing-machine-learning-projects?utm_source=mikrobusiness&utm_medium=affiliate&utm_campaign=book_thompson_managing_6_14_22)
-- [Causal Inference for Data Science](https://www.manning.com/books/causal-inference-for-data-science?utm_source=mikrobusiness&utm_medium=affiliate&utm_campaign=book_ruizdevilla_causal_6_6_22)
-- [Data for All](https://www.manning.com/books/data-for-all?utm_source=mikrobusiness&utm_medium=affiliate)
-
-### Journals, Publications and Magazines
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [ICML](https://icml.cc/2015/) - International Conference on Machine Learning
-- [GECCO](https://gecco-2019.sigevo.org/index.html/HomePage) - The Genetic and Evolutionary Computation Conference (GECCO)
-- [epjdatascience](https://epjdatascience.springeropen.com/)
-- [Journal of Data Science](https://jds-online.org/journal/JDS) - an international journal devoted to applications of statistical methods at large
-- [Big Data Research](https://www.journals.elsevier.com/big-data-research)
-- [Journal of Big Data](https://journalofbigdata.springeropen.com/)
-- [Big Data & Society](https://journals.sagepub.com/home/bds)
-- [Data Science Journal](https://www.jstage.jst.go.jp/browse/dsj)
-- [datatau.com/news](https://www.datatau.com/news) - Like Hacker News, but for data
-- [Data Science Trello Board](https://trello.com/b/rbpEfMld/data-science)
-- [Medium Data Science Topic](https://medium.com/tag/data-science) - Data Science related publications on medium
-- [Towards Data Science Genetic Algorithm Topic](https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3#:~:text=A%20genetic%20algorithm%20is%20a,offspring%20of%20the%20next%20generation.) -Genetic Algorithm related Publications towards Data Science
-- [all AI news](https://allainews.com/) - The AI/ML/Big Data news aggregator platform
-
-### Newsletters
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [AI Digest](https://aidigest.net/). A weekly newsletter to keep up to date with AI, machine learning, and data science. [Archive](https://aidigest.net/digests).
-- [DataTalks.Club](https://datatalks.club). A weekly newsletter about data-related things. [Archive](https://us19.campaign-archive.com/home/?u=0d7822ab98152f5afc118c176&id=97178021aa).
-- [The Analytics Engineering Roundup](https://roundup.getdbt.com/about). A newsletter about data science. [Archive](https://roundup.getdbt.com/archive).
-
-### Bloggers
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [Wes McKinney](https://wesmckinney.com/archives.html) - Wes McKinney Archives.
-- [Matthew Russell](https://miningthesocialweb.com/) - Mining The Social Web.
-- [Greg Reda](https://www.gregreda.com/) - Greg Reda Personal Blog
-- [Kevin Davenport](https://kldavenport.com/) - Kevin Davenport Personal Blog
-- [Julia Evans](https://jvns.ca/) - Recurse Center alumna
-- [Hakan Kardas](https://www.cse.unr.edu/~hkardes/) - Personal Web Page
-- [Sean J. Taylor](https://seanjtaylor.com/) - Personal Web Page
-- [Drew Conway](https://drewconway.com/) - Personal Web Page
-- [Hilary Mason](https://hilarymason.com/) - Personal Web Page
-- [Noah Iliinsky](https://complexdiagrams.com/) - Personal Blog
-- [Matt Harrison](https://hairysun.com/) - Personal Blog
-- [Vamshi Ambati](https://allthingsds.wordpress.com/) - AllThings Data Sciene
-- [Prash Chan](https://www.mdmgeek.com/) - Tech Blog on Master Data Management And Every Buzz Surrounding It
-- [Clare Corthell](https://datasciencemasters.org/) - The Open Source Data Science Masters
-- [Paul Miller](https://cloudofdata.com/) Based in the UK and working globally, Cloud of Data's consultancy services help clients understand the implications of taking data and more to the Cloud.
-- [Data Science London](https://datasciencelondon.org/) Data Science London is a non-profit organization dedicated to the free, open, dissemination of data science.
- We are the largest data science community in Europe.
- We are more than 3,190 data scientists and data geeks in our community.
-- [Datawrangling](http://www.datawrangling.org) by Peter Skomoroch. MACHINE LEARNING, DATA MINING, AND MORE
-- [Quora Data Science](https://www.quora.com/topic/Data-Science) - Data Science Questions and Answers from experts
-- [Siah](https://openresearch.wordpress.com/) a PhD student at Berkeley
-- [Louis Dorard](https://www.ownml.co/blog/) a technology guy with a penchant for the web and for data, big and small
-- [Machine Learning Mastery](https://machinelearningmastery.com/) about helping professional programmers confidently apply machine learning algorithms to address complex problems.
-- [Daniel Forsyth](https://www.danielforsyth.me/) - Personal Blog
-- [Data Science Weekly](https://www.datascienceweekly.org/) - Weekly News Blog
-- [Revolution Analytics](https://blog.revolutionanalytics.com/) - Data Science Blog
-- [R Bloggers](https://www.r-bloggers.com/) - R Bloggers
-- [The Practical Quant](https://practicalquant.blogspot.com/) Big data
-- [Yet Another Data Blog](https://yet-another-data-blog.blogspot.com/) Yet Another Data Blog
-- [Spenczar](https://spenczar.com/) a data scientist at _Twitch_. I handle the whole data pipeline, from tracking to model-building to reporting.
-- [KD Nuggets](https://www.kdnuggets.com/) Data Mining, Analytics, Big Data, Data, Science not a blog a portal
-- [Meta Brown](https://www.metabrown.com/blog/) - Personal Blog
-- [Data Scientist](https://datascientists.net/) is building the data scientist culture.
-- [WhatSTheBigData](https://whatsthebigdata.com/) is some of, all of, or much more than the above and this blog explores its impact on information technology, the business world, government agencies, and our lives.
-- [Tevfik Kosar](https://magnus-notitia.blogspot.com/) - Magnus Notitia
-- [New Data Scientist](https://newdatascientist.blogspot.com/) How a Social Scientist Jumps into the World of Big Data
-- [Harvard Data Science](https://harvarddatascience.com/) - Thoughts on Statistical Computing and Visualization
-- [Data Science 101](https://ryanswanstrom.com/datascience101/) - Learning To Be A Data Scientist
-- [Kaggle Past Solutions](https://www.chioka.in/kaggle-competition-solutions/)
-- [DataScientistJourney](https://datascientistjourney.wordpress.com/category/data-science/)
-- [NYC Taxi Visualization Blog](https://chriswhong.github.io/nyctaxi/)
-- [Learning Lover](https://learninglover.com/blog/)
-- [Dataists](https://www.dataists.com/)
-- [Data-Mania](https://www.data-mania.com/)
-- [Data-Magnum](https://data-magnum.com/)
-- [P-value](https://www.p-value.info/) - Musings on data science, machine learning, and stats.
-- [datascopeanalytics](https://datascopeanalytics.com/blog/)
-- [Digital transformation](https://tarrysingh.com/)
-- [datascientistjourney](https://datascientistjourney.wordpress.com/category/data-science/)
-- [Data Mania Blog](https://www.data-mania.com/blog/) - [The File Drawer](https://chris-said.io/) - Chris Said's science blog
-- [Emilio Ferrara's web page](https://www.emilio.ferrara.name/)
-- [DataNews](https://datanews.tumblr.com/)
-- [Reddit TextMining](https://www.reddit.com/r/textdatamining/)
-- [Periscopic](https://periscopic.com/#!/news)
-- [Hilary Parker](https://hilaryparker.com/)
-- [Data Stories](https://datastori.es/)
-- [Data Science Lab](https://datasciencelab.wordpress.com/)
-- [Meaning of](https://www.kennybastani.com/)
-- [Adventures in Data Land](https://blog.smola.org)
-- [DATA MINERS BLOG](https://blog.data-miners.com/)
-- [Dataclysm](https://theblog.okcupid.com/)
-- [FlowingData](https://flowingdata.com/) - Visualization and Statistics
-- [Calculated Risk](https://www.calculatedriskblog.com/)
-- [O'reilly Learning Blog](https://www.oreilly.com/content/topics/oreilly-learning/)
-- [Dominodatalab](https://blog.dominodatalab.com/)
-- [i am trask](https://iamtrask.github.io/) - A Machine Learning Craftsmanship Blog
-- [Vademecum of Practical Data Science](https://datasciencevademecum.wordpress.com/) - Handbook and recipes for data-driven solutions of real-world problems
-- [Dataconomy](https://dataconomy.com/) - A blog on the newly emerging data economy
-- [Springboard](https://www.springboard.com/blog/) - A blog with resources for data science learners
-- [Analytics Vidhya](https://www.analyticsvidhya.com/) - A full-fledged website about data science and analytics study material.
-- [Occam's Razor](https://www.kaushik.net/avinash/) - Focused on Web Analytics.
-- [Data School](https://www.dataschool.io/) - Data science tutorials for beginners!
-- [Colah's Blog](https://colah.github.io) - Blog for understanding Neural Networks!
-- [Sebastian's Blog](https://ruder.io/#open) - Blog for NLP and transfer learning!
-- [Distill](https://distill.pub) - Dedicated to clear explanations of machine learning!
-- [Chris Albon's Website](https://chrisalbon.com/) - Data Science and AI notes
-- [Andrew Carr](https://andrewnc.github.io/blog/blog.html) - Data Science with Esoteric programming languages
-- [floydhub](https://blog.floydhub.com/introduction-to-genetic-algorithms/) - Blog for Evolutionary Algorithms
-- [Jingles](https://jinglescode.github.io/) - Review and extract key concepts from academic papers
-- [nbshare](https://www.nbshare.io/notebooks/data-science/) - Data Science notebooks
-- [Deep and Shallow](https://deep-and-shallow.com/) - All things Deep and Shallow in Data Science
-- [Loic Tetrel](https://ltetrel.github.io/) - Data science blog
-- [Chip Huyen's Blog](https://huyenchip.com/blog/) - ML Engineering, MLOps, and the use of ML in startups
-- [Maria Khalusova](https://www.mariakhalusova.com/) - Data science blog
-- [Aditi Rastogi](https://medium.com/@aditi2507rastogi) - ML,DL,Data Science blog
-- [Santiago Basulto](https://medium.com/@santiagobasulto) - Data Science with Python
-- [Akhil Soni](https://medium.com/@akhil0435) - ML, DL and Data Science
-- [Akhil Soni](https://akhilworld.hashnode.dev/) - ML, DL and Data Science
-
-### Presentations
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [How to Become a Data Scientist](https://www.slideshare.net/ryanorban/how-to-become-a-data-scientist)
-- [Introduction to Data Science](https://www.slideshare.net/NikoVuokko/introduction-to-data-science-25391618)
-- [Intro to Data Science for Enterprise Big Data](https://www.slideshare.net/pacoid/intro-to-data-science-for-enterprise-big-data)
-- [How to Interview a Data Scientist](https://www.slideshare.net/dtunkelang/how-to-interview-a-data-scientist)
-- [How to Share Data with a Statistician](https://github.com/jtleek/datasharing)
-- [The Science of a Great Career in Data Science](https://www.slideshare.net/katemats/the-science-of-a-great-career-in-data-science)
-- [What Does a Data Scientist Do?](https://www.slideshare.net/datasciencelondon/big-data-sorry-data-science-what-does-a-data-scientist-do)
-- [Building Data Start-Ups: Fast, Big, and Focused](https://www.slideshare.net/medriscoll/driscoll-strata-buildingdatastartups25may2011clean)
-- [How to win data science competitions with Deep Learning](https://www.slideshare.net/0xdata/how-to-win-data-science-competitions-with-deep-learning)
-- [Full-Stack Data Scientist](https://www.slideshare.net/AlexeyGrigorev/fullstack-data-scientist)
-
-### Podcasts
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [AI at Home](https://podcasts.apple.com/us/podcast/data-science-at-home/id1069871378)
-- [AI Today](https://www.cognilytica.com/aitoday/)
-- [Adversarial Learning](https://adversariallearning.com/)
-- [Becoming a Data Scientist](https://www.becomingadatascientist.com/category/podcast/)
-- [Chai time Data Science](https://www.youtube.com/playlist?list=PLLvvXm0q8zUbiNdoIazGzlENMXvZ9bd3x)
-- [Data Crunch](https://datacrunchcorp.com/data-crunch-podcast/)
-- [Data Engineering Podcast](https://www.dataengineeringpodcast.com/)
-- [Data Science at Home](https://datascienceathome.com/)
-- [Data Science Mixer](https://community.alteryx.com/t5/Data-Science-Mixer/bg-p/mixer)
-- [Data Skeptic](https://dataskeptic.com/)
-- [Data Stories](https://datastori.es/)
-- [Datacast](https://jameskle.com/writes/category/Datacast)
-- [DataFramed](https://www.datacamp.com/community/podcast)
-- [DataTalks.Club](https://anchor.fm/datatalksclub)
-- [Gradient Dissent](https://wandb.ai/fully-connected/gradient-dissent)
-- [Learning Machines 101](https://www.learningmachines101.com/)
-- [Let's Data (Brazil)](https://www.youtube.com/playlist?list=PLn_z5E4dh_Lj5eogejMxfOiNX3nOhmhmM)
-- [Linear Digressions](https://lineardigressions.com/)
-- [Not So Standard Deviations](https://nssdeviations.com/)
-- [O'Reilly Data Show Podcast](https://www.oreilly.com/radar/topics/oreilly-data-show-podcast/)
-- [Partially Derivative](https://partiallyderivative.com/)
-- [Superdatascience](https://www.superdatascience.com/podcast/)
-- [The Data Engineering Show](https://www.dataengineeringshow.com/)
-- [The Radical AI Podcast](https://www.radicalai.org/)
-- [The Robot Brains Podcast](https://www.therobotbrains.ai/)
-- [What's The Point](https://fivethirtyeight.com/tag/whats-the-point/)
-- [How AI Built This](https://how-ai-built-this.captivate.fm/)
-
-### YouTube Videos & Channels
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [What is machine learning?](https://www.youtube.com/watch?v=WXHM_i-fgGo)
-- [Andrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24)
-- [Data36 - Data Science for Beginners by Tomi Mester](https://www.youtube.com/c/TomiMesterData36comDataScienceForBeginners)
-- [Deep Learning: Intelligence from Big Data](https://www.youtube.com/watch?v=czLI3oLDe8M)
-- [Interview with Google's AI and Deep Learning 'Godfather' Geoffrey Hinton](https://www.youtube.com/watch?v=1Wp3IIpssEc)
-- [Introduction to Deep Learning with Python](https://www.youtube.com/watch?v=S75EdAcXHKk)
-- [What is machine learning, and how does it work?](https://www.youtube.com/watch?v=elojMnjn4kk)
-- [Data School](https://www.youtube.com/channel/UCnVzApLJE2ljPZSeQylSEyg) - Data Science Education
-- [Neural Nets for Newbies by Melanie Warrick (May 2015)](https://www.youtube.com/watch?v=Cu6A96TUy_o)
-- [Neural Networks video series by Hugo Larochelle](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)
-- [Google DeepMind co-founder Shane Legg - Machine Super Intelligence](https://www.youtube.com/watch?v=evNCyRL3DOU)
-- [Data Science Primer](https://www.youtube.com/watch?v=cHzvYxBN9Ls&list=PLPqVjP3T4RIRsjaW07zoGzH-Z4dBACpxY)
-- [Data Science with Genetic Algorithms](https://www.youtube.com/watch?v=lpD38NxTOnk)
-- [Data Science for Beginners](https://www.youtube.com/playlist?list=PL2zq7klxX5ATMsmyRazei7ZXkP1GHt-vs)
-- [DataTalks.Club](https://www.youtube.com/channel/UCDvErgK0j5ur3aLgn6U-LqQ)
-- [Mildlyoverfitted - Tutorials on intermediate ML/DL topics](https://www.youtube.com/channel/UCYBSjwkGTK06NnDnFsOcR7g)
-- [mlops.community - Interviews of industry experts about production ML](https://www.youtube.com/channel/UCYBSjwkGTK06NnDnFsOcR7g)
-- [ML Street Talk - Unabashedly technical and non-commercial, so you will hear no annoying pitches.](https://www.youtube.com/c/machinelearningstreettalk)
-- [Neural networks by 3Blue1Brown ](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
-- [Neural networks from scratch by Sentdex](https://www.youtube.com/playlist?list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3)
-- [Manning Publications YouTube channel](https://www.youtube.com/c/ManningPublications/featured)
-- [Ask Dr Chong: How to Lead in Data Science - Part 1](https://youtu.be/JYuQZii5o58)
-- [Ask Dr Chong: How to Lead in Data Science - Part 2](https://youtu.be/SzqIXV-O-ko)
-- [Ask Dr Chong: How to Lead in Data Science - Part 3](https://youtu.be/Ogwm7k_smTA)
-- [Ask Dr Chong: How to Lead in Data Science - Part 4](https://youtu.be/a9usjdzTxTU)
-- [Ask Dr Chong: How to Lead in Data Science - Part 5](https://youtu.be/MYdQq-F3Ws0)
-- [Ask Dr Chong: How to Lead in Data Science - Part 6](https://youtu.be/LOOt4OVC3hY)
-- [Regression Models: Applying simple Poisson regression](https://www.youtube.com/watch?v=9Hk8K8jhiOo)
-- [Deep Learning Architectures](https://www.youtube.com/playlist?list=PLv8Cp2NvcY8DpVcsmOT71kymgMmcr59Mf)
-- [Time Series Modelling and Analysis](https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK)
-
-## Socialize
-**[`^ back to top ^`](#awesome-data-science)**
-
-Below are some Social Media links. Connect with other data scientists!
-
-- [Facebook Accounts](#facebook-accounts)
-- [Twitter Accounts](#twitter-accounts)
-- [Telegram Channels](#telegram-channels)
-- [Slack Communities](#slack-communities)
-- [GitHub Groups](#github-groups)
-- [Data Science Competitions](#data-science-competitions)
-
-
-### Facebook Accounts
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [Data](https://www.facebook.com/data)
-- [Big Data Scientist](https://www.facebook.com/Bigdatascientist)
-- [Data Science Day](https://www.facebook.com/datascienceday/)
-- [Data Science Academy](https://www.facebook.com/nycdatascience)
-- [Facebook Data Science Page](https://www.facebook.com/pages/Data-science/431299473579193?ref=br_rs)
-- [Data Science London](https://www.facebook.com/pages/Data-Science-London/226174337471513)
-- [Data Science Technology and Corporation](https://www.facebook.com/DataScienceTechnologyCorporation?ref=br_rs)
-- [Data Science - Closed Group](https://www.facebook.com/groups/1394010454157077/?ref=br_rs)
-- [Center for Data Science](https://www.facebook.com/centerdatasciences?ref=br_rs)
-- [Big data hadoop NOSQL Hive Hbase](https://www.facebook.com/groups/bigdatahadoop/)
-- [Analytics, Data Mining, Predictive Modeling, Artificial Intelligence](https://www.facebook.com/groups/data.analytics/)
-- [Big Data Analytics using R](https://www.facebook.com/groups/434352233255448/)
-- [Big Data Analytics with R and Hadoop](https://www.facebook.com/groups/rhadoop/)
-- [Big Data Learnings](https://www.facebook.com/groups/bigdatalearnings/)
-- [Big Data, Data Science, Data Mining & Statistics](https://www.facebook.com/groups/bigdatastatistics/)
-- [BigData/Hadoop Expert](https://www.facebook.com/groups/BigDataExpert/)
-- [Data Mining / Machine Learning / AI](https://www.facebook.com/groups/machinelearningforum/)
-- [Data Mining/Big Data - Social Network Ana](https://www.facebook.com/groups/dataminingsocialnetworks/)
-- [Vademecum of Practical Data Science](https://www.facebook.com/datasciencevademecum)
-- [Veri Bilimi Istanbul](https://www.facebook.com/groups/veribilimiistanbul/)
-- [The Data Science Blog](https://www.facebook.com/theDataScienceBlog/)
-
-
-### Twitter Accounts
-**[`^ back to top ^`](#awesome-data-science)**
-
-| Twitter | Description |
-| --- | --- |
-| [Big Data Combine](https://twitter.com/BigDataCombine) | Rapid-fire, live tryouts for data scientists seeking to monetize their models as trading strategies |
-| Big Data Mania | Data Viz Wiz, Data Journalist, Growth Hacker, Author of Data Science for Dummies (2015) |
-| [Big Data Science](https://twitter.com/analyticbridge) | Big Data, Data Science, Predictive Modeling, Business Analytics, Hadoop, Decision and Operations Research. |
-| Charlie Greenbacker | Director of Data Science at @ExploreAltamira |
-| [Chris Said](https://twitter.com/Chris_Said) | Data scientist at Twitter |
-| [Clare Corthell](https://twitter.com/clarecorthell) | Dev, Design, Data Science @mattermark #hackerei |
-| [DADI Charles-Abner](https://twitter.com/DadiCharles) | #datascientist @Ekimetrics. , #machinelearning #dataviz #DynamicCharts #Hadoop #R #Python #NLP #Bitcoin #dataenthousiast |
-| [Data Science Central](https://twitter.com/DataScienceCtrl) | Data Science Central is the industry's single resource for Big Data practitioners. |
-| [Data Science London](https://twitter.com/ds_ldn) | Data Science. Big Data. Data Hacks. Data Junkies. Data Startups. Open Data |
-| [Data Science Renee](https://twitter.com/BecomingDataSci) | Documenting my path from SQL Data Analyst pursuing an Engineering Master's Degree to Data Scientist |
-| [Data Science Report](https://twitter.com/TedOBrien93) | Mission is to help guide & advance careers in Data Science & Analytics |
-| [Data Science Tips](https://twitter.com/datasciencetips) | Tips and Tricks for Data Scientists around the world! #datascience #bigdata |
-| [Data Vizzard](https://twitter.com/DataVisualizati) | DataViz, Security, Military |
-| [DataScienceX](https://twitter.com/DataScienceX) | |
-| deeplearning4j | |
-| [DJ Patil](https://twitter.com/dpatil) | White House Data Chief, VP @ RelateIQ. |
-| [Domino Data Lab](https://twitter.com/DominoDataLab) | |
-| [Drew Conway](https://twitter.com/drewconway) | Data nerd, hacker, student of conflict. |
-| Emilio Ferrara | #Networks, #MachineLearning and #DataScience. I work on #Social Media. Postdoc at @IndianaUniv |
-| [Erin Bartolo](https://twitter.com/erinbartolo) | Running with #BigData--enjoying a love/hate relationship with its hype. @iSchoolSU #DataScience Program Mgr. |
-| [Greg Reda](https://twitter.com/gjreda) | Working @ _GrubHub_ about data and pandas |
-| [Gregory Piatetsky](https://twitter.com/kdnuggets) | KDnuggets President, Analytics/Big Data/Data Mining/Data Science expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, part-time philosopher. |
-| [Hadley Wickham](https://twitter.com/hadleywickham) | Chief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University. |
-| [Hakan Kardas](https://twitter.com/hakan_kardes) | Data Scientist |
-| [Hilary Mason](https://twitter.com/hmason) | Data Scientist in Residence at @accel. |
-| [Jeff Hammerbacher](https://twitter.com/hackingdata) | ReTweeting about data science |
-| [John Myles White](https://twitter.com/johnmyleswhite) | Scientist at Facebook and Julia developer. Author of Machine Learning for Hackers and Bandit Algorithms for Website Optimization. Tweets reflect my views only. |
-| [Juan Miguel Lavista](https://twitter.com/BDataScientist) | Principal Data Scientist @ Microsoft Data Science Team |
-| [Julia Evans](https://twitter.com/b0rk) | Hacker - Pandas - Data Analyze |
-| [Kenneth Cukier](https://twitter.com/kncukier) | The Economist's Data Editor and co-author of Big Data (http://www.big-data-book.com/). |
-| Kevin Davenport | Organizer of https://www.meetup.com/San-Diego-Data-Science-R-Users-Group/ |
-| [Kevin Markham](https://twitter.com/justmarkham) | Data science instructor, and founder of [Data School](https://www.dataschool.io/) |
-| [Kim Rees](https://twitter.com/krees) | Interactive data visualization and tools. Data flaneur. |
-| [Kirk Borne](https://twitter.com/KirkDBorne) | DataScientist, PhD Astrophysicist, Top #BigData Influencer. |
-| Linda Regber | Data storyteller, visualizations. |
-| [Luis Rei](https://twitter.com/lmrei) | PhD Student. Programming, Mobile, Web. Artificial Intelligence, Intelligent Robotics Machine Learning, Data Mining, Natural Language Processing, Data Science. |
-| Mark Stevenson | Data Analytics Recruitment Specialist at Salt (@SaltJobs) Analytics - Insight - Big Data - Data science |
-| [Matt Harrison](https://twitter.com/__mharrison__) | Opinions of full-stack Python guy, author, instructor, currently playing Data Scientist. Occasional fathering, husbanding, organic gardening. |
-| [Matthew Russell](https://twitter.com/ptwobrussell) | Mining the Social Web. |
-| [Mert Nuhoğlu](https://twitter.com/mertnuhoglu) | Data Scientist at BizQualify, Developer |
-| [Monica Rogati](https://twitter.com/mrogati) | Data @ Jawbone. Turned data into stories & products at LinkedIn. Text mining, applied machine learning, recommender systems. Ex-gamer, ex-machine coder; namer. |
-| [Noah Iliinsky](https://twitter.com/noahi) | Visualization & interaction designer. Practical cyclist. Author of vis books: https://www.oreilly.com/pub/au/4419 |
-| [Paul Miller](https://twitter.com/PaulMiller) | Cloud Computing/ Big Data/ Open Data Analyst & Consultant. Writer, Speaker & Moderator. Gigaom Research Analyst. |
-| [Peter Skomoroch](https://twitter.com/peteskomoroch) | Creating intelligent systems to automate tasks & improve decisions. Entrepreneur, ex-Principal Data Scientist @LinkedIn. Machine Learning, ProductRei, Networks |
-| [Prash Chan](https://twitter.com/MDMGeek) | Solution Architect @ IBM, Master Data Management, Data Quality & Data Governance Blogger. Data Science, Hadoop, Big Data & Cloud. |
-| [Quora Data Science](https://twitter.com/q_datascience) | Quora's data science topic |
-| [R-Bloggers](https://twitter.com/Rbloggers) | Tweet blog posts from the R blogosphere, data science conferences, and (!) open jobs for data scientists. |
-| [Rand Hindi](https://twitter.com/randhindi) | |
-| [Randy Olson](https://twitter.com/randal_olson) | Computer scientist researching artificial intelligence. Data tinkerer. Community leader for @DataIsBeautiful. #OpenScience advocate. |
-| [Recep Erol](https://twitter.com/EROLRecep) | Data Science geek @ UALR |
-| [Ryan Orban](https://twitter.com/ryanorban) | Data scientist, genetic origamist, hardware aficionado |
-| [Sean J. Taylor](https://twitter.com/seanjtaylor) | Social Scientist. Hacker. Facebook Data Science Team. Keywords: Experiments, Causal Inference, Statistics, Machine Learning, Economics. |
-| [Silvia K. Spiva](https://twitter.com/silviakspiva) | #DataScience at Cisco |
-| [Harsh B. Gupta](https://twitter.com/harshbg) | Data Scientist at BBVA Compass |
-| [Spencer Nelson](https://twitter.com/spenczar_n) | Data nerd |
-| [Talha Oz](https://twitter.com/tozCSS) | Enjoys ABM, SNA, DM, ML, NLP, HI, Python, Java. Top percentile Kaggler/data scientist |
-| [Tasos Skarlatidis](https://twitter.com/anskarl) | Complex Event Processing, Big Data, Artificial Intelligence and Machine Learning. Passionate about programming and open-source. |
-| [Terry Timko](https://twitter.com/Terry_Timko) | InfoGov; Bigdata; Data as a Service; Data Science; Open, Social & Business Data Convergence |
-| [Tony Baer](https://twitter.com/TonyBaer) | IT analyst with Ovum covering Big Data & data management with some systems engineering thrown in. |
-| [Tony Ojeda](https://twitter.com/tonyojeda3) | Data Scientist , Author , Entrepreneur. Co-founder @DataCommunityDC. Founder @DistrictDataLab. #DataScience #BigData #DataDC |
-| [Vamshi Ambati](https://twitter.com/vambati) | Data Science @ PayPal. #NLP, #machinelearning; PhD, Carnegie Mellon alumni (Blog: https://allthingsds.wordpress.com ) |
-| [Wes McKinney](https://twitter.com/wesmckinn) | Pandas (Python Data Analysis library). |
-| [WileyEd](https://twitter.com/WileyEd) | Senior Manager - @Seagate Big Data Analytics @McKinsey Alum #BigData + #Analytics Evangelist #Hadoop, #Cloud, #Digital, & #R Enthusiast |
-| [WNYC Data News Team](https://twitter.com/datanews) | The data news crew at @WNYC. Practicing data-driven journalism, making it visual, and showing our work. |
-| [Alexey Grigorev](https://twitter.com/Al_Grigor) | Data science author |
-| [İlker Arslan](https://twitter.com/ilkerarslan_35) | Data science author. Shares mostly about Julia programming |
-| [INEVITABLE](https://twitter.com/WeAreInevitable) | AI & Data Science Start-up Company based in England, UK |
-
-### Telegram Channels
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [Open Data Science](https://t.me/opendatascience) – First Telegram Data Science channel. Covering all technical and popular staff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former.
-- [Loss function porn](https://t.me/loss_function_porn) — Beautiful posts on DS/ML theme with video or graphic visualization.
-- [Machinelearning](https://t.me/ai_machinelearning_big_data) – Daily ML news.
-
-
-### Slack Communities
-[top](#awesome-data-science)
-
-- [DataTalks.Club](https://datatalks.club)
-- [Women Who Code - Data Science](https://www.womenwhocode.com/datascience)
-
-### GitHub Groups
-- [Berkeley Institute for Data Science](https://github.com/BIDS)
-
-### Data Science Competitions
-
-Some data mining competition platforms
-
-- [Kaggle](https://www.kaggle.com/)
-- [DrivenData](https://www.drivendata.org/)
-- [Analytics Vidhya](https://datahack.analyticsvidhya.com/)
-- [InnoCentive](https://www.innocentive.com/)
-- [Microprediction](https://www.microprediction.com/python-1)
-
-## Fun
-
-- [Infographic](#infographics)
-- [Datasets](#datasets)
-- [Comics](#comics)
-
-
-### Infographics
-**[`^ back to top ^`](#awesome-data-science)**
-
-| Preview | Description |
-| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| [](https://i.imgur.com/0OoLaa5.png) | [Key differences of a data scientist vs. data engineer](https://searchbusinessanalytics.techtarget.com/feature/Key-differences-of-a-data-scientist-vs-data-engineer) |
-| [](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/DataScienceEightSteps_Full.png) | A visual guide to Becoming a Data Scientist in 8 Steps by [DataCamp](https://www.datacamp.com) [(img)](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/DataScienceEightSteps_Full.png) |
-| [](https://i.imgur.com/FxsL3b8.png) | Mindmap on required skills ([img](https://i.imgur.com/FxsL3b8.png)) |
-| [](https://nirvacana.com/thoughts/wp-content/uploads/2013/07/RoadToDataScientist1.png) | Swami Chandrasekaran made a [Curriculum via Metro map](http://nirvacana.com/thoughts/2013/07/08/becoming-a-data-scientist/). |
-| [](https://i.imgur.com/4ZBBvb0.png) | by [@kzawadz](https://twitter.com/kzawadz) via [twitter](https://twitter.com/MktngDistillery/status/538671811991715840) |
-| [](https://i.imgur.com/xLY3XZn.jpg) | By [Data Science Central](https://www.datasciencecentral.com/) |
-| [](https://i.imgur.com/0TydZ4M.png) | Data Science Wars: R vs Python |
-| [](https://i.imgur.com/HnRwlce.png) | How to select statistical or machine learning techniques |
-| [](https://scikit-learn.org/stable/_static/ml_map.png) | Choosing the Right Estimator |
-| [](https://i.imgur.com/uEqMwZa.png) | The Data Science Industry: Who Does What |
-| [](https://i.imgur.com/RsHqY84.png) | Data Science ~~Venn~~ Euler Diagram |
-| [](https://www.springboard.com/blog/wp-content/uploads/2016/03/20160324_springboard_vennDiagram.png) | Different Data Science Skills and Roles from [this article](https://www.springboard.com/blog/data-science-career-paths-different-roles-industry/) by Springboard |
-| [](https://data-literacy.geckoboard.com/poster/) | A simple and friendly way of teaching your non-data scientist/non-statistician colleagues [how to avoid mistakes with data](https://data-literacy.geckoboard.com/poster/). From Geckoboard's [Data Literacy Lessons](https://data-literacy.geckoboard.com/). |
-
-### Datasets
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [Academic Torrents](https://academictorrents.com/)
-- [ADS-B Exchange](https://www.adsbexchange.com/data-samples/) - Specific datasets for aircraft and Automatic Dependent Surveillance-Broadcast (ADS-B) sources.
-- [hadoopilluminated.com](https://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html)
-- [data.gov](https://catalog.data.gov/dataset) - The home of the U.S. Government's open data
-- [United States Census Bureau](https://www.census.gov/)
-- [usgovxml.com](https://usgovxml.com/)
-- [enigma.com](https://enigma.com/) - Navigate the world of public data - Quickly search and analyze billions of public records published by governments, companies and organizations.
-- [datahub.io](https://datahub.io/)
-- [aws.amazon.com/datasets](https://aws.amazon.com/datasets/)
-- [datacite.org](https://datacite.org/)
-- [The official portal for European data](https://data.europa.eu/en)
-- [NASDAQ:DATA](https://data.nasdaq.com/) - Nasdaq Data Link A premier source for financial, economic and alternative datasets.
-- [figshare.com](https://figshare.com/)
-- [GeoLite Legacy Downloadable Databases](https://dev.maxmind.com/geoip)
-- [Quora's Big Datasets Answer](https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public)
-- [Public Big Data Sets](https://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html)
-- [Kaggle Datasets](https://www.kaggle.com/datasets)
-- [A Deep Catalog of Human Genetic Variation](https://www.internationalgenome.org/data)
-- [A community-curated database of well-known people, places, and things](https://developers.google.com/freebase/)
-- [Google Public Data](https://www.google.com/publicdata/directory)
-- [World Bank Data](https://data.worldbank.org/)
-- [NYC Taxi data](https://chriswhong.github.io/nyctaxi/)
-- [Open Data Philly](https://www.opendataphilly.org/) Connecting people with data for Philadelphia
-- [grouplens.org](https://grouplens.org/datasets/) Sample movie (with ratings), book and wiki datasets
-- [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/) - contains data sets good for machine learning
-- [research-quality data sets](https://web.archive.org/web/20150320022752/https://bitly.com/bundles/hmason/1) by [Hilary Mason](https://web.archive.org/web/20150501033715/https://bitly.com/u/hmason/bundles)
-- [National Centers for Environmental Information](https://www.ncei.noaa.gov/)
-- [ClimateData.us](https://www.climatedata.us/) (related: [U.S. Climate Resilience Toolkit](https://toolkit.climate.gov/))
-- [r/datasets](https://www.reddit.com/r/datasets/)
-- [MapLight](https://www.maplight.org/data-series) - provides a variety of data free of charge for uses that are freely available to the general public. Click on a data set below to learn more
-- [GHDx](https://ghdx.healthdata.org/) - Institute for Health Metrics and Evaluation - a catalog of health and demographic datasets from around the world and including IHME results
-- [St. Louis Federal Reserve Economic Data - FRED](https://fred.stlouisfed.org/)
-- [New Zealand Institute of Economic Research – Data1850](https://data1850.nz/)
-- [Open Data Sources](https://github.com/datasciencemasters/data)
-- [UNICEF Data](https://data.unicef.org/)
-- [undata](https://data.un.org/)
-- [NASA SocioEconomic Data and Applications Center - SEDAC](https://sedac.ciesin.columbia.edu/)
-- [The GDELT Project](https://www.gdeltproject.org/)
-- [Sweden, Statistics](https://www.scb.se/en/)
-- [StackExchange Data Explorer](https://data.stackexchange.com) - an open source tool for running arbitrary queries against public data from the Stack Exchange network.
-- [SocialGrep](https://socialgrep.com/datasets) - a collection of open Reddit datasets.
-- [San Fransisco Government Open Data](https://datasf.org/opendata/)
-- [IBM Asset Dataset](https://developer.ibm.com/exchanges/data/)
-- [Open data Index](https://index.okfn.org/)
-- [Public Git Archive](https://github.com/src-d/datasets/tree/master/PublicGitArchive)
-- [GHTorrent](https://ghtorrent.org/)
-- [Microsoft Research Open Data](https://msropendata.com/)
-- [Open Government Data Platform India](https://data.gov.in/)
-- [Google Dataset Search (beta)](https://datasetsearch.research.google.com/)
-- [NAYN.CO Turkish News with categories](https://github.com/naynco/nayn.data)
-- [Covid-19](https://github.com/datasets/covid-19)
-- [Covid-19 Google](https://github.com/google-research/open-covid-19-data)
-- [Enron Email Dataset](https://www.cs.cmu.edu/~./enron/)
-- [5000 Images of Clothes](https://github.com/alexeygrigorev/clothing-dataset)
-- [IBB Open Portal](https://data.ibb.gov.tr/en/)
-- [The Humanitarian Data Exchange](https://data.humdata.org/)
-
-### Comics
-**[`^ back to top ^`](#awesome-data-science)**
-
-- [Comic compilation](https://medium.com/@nikhil_garg/a-compilation-of-comics-explaining-statistics-data-science-and-machine-learning-eeefbae91277)
-- [Cartoons](https://www.kdnuggets.com/websites/cartoons.html)
-
-## Other Awesome Lists
-
-- Other amazingly awesome lists can be found in the [awesome-awesomeness](https://github.com/bayandin/awesome-awesomeness)
-- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)
-- [lists](https://github.com/jnv/lists)
-- [awesome-dataviz](https://github.com/javierluraschi/awesome-dataviz)
-- [awesome-python](https://github.com/vinta/awesome-python)
-- [Data Science IPython Notebooks.](https://github.com/donnemartin/data-science-ipython-notebooks)
-- [awesome-r](https://github.com/qinwf/awesome-R)
-- [awesome-datasets](https://github.com/awesomedata/awesome-public-datasets)
-- [awesome-Machine Learning & Deep Learning Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md)
-- [Awesome Data Science Ideas](https://github.com/JosPolfliet/awesome-ai-usecases)
-- [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)
-- [Community Curated Data Science Resources](https://hackr.io/tutorials/learn-data-science)
-- [Awesome Machine Learning On Source Code](https://github.com/src-d/awesome-machine-learning-on-source-code)
-- [Awesome Community Detection](https://github.com/benedekrozemberczki/awesome-community-detection)
-- [Awesome Graph Classification](https://github.com/benedekrozemberczki/awesome-graph-classification)
-- [Awesome Decision Tree Papers](https://github.com/benedekrozemberczki/awesome-decision-tree-papers)
-- [Awesome Fraud Detection Papers](https://github.com/benedekrozemberczki/awesome-fraud-detection-papers)
-- [Awesome Gradient Boosting Papers](https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers)
-- [Awesome Computer Vision Models](https://github.com/nerox8664/awesome-computer-vision-models)
-- [Awesome Monte Carlo Tree Search](https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers)
-- [Glossary of common statistics and ML terms](https://www.analyticsvidhya.com/glossary-of-common-statistics-and-machine-learning-terms/)
-- [100 NLP Papers](https://github.com/mhagiwara/100-nlp-papers)
-- [Awesome Game Datasets](https://github.com/leomaurodesenv/game-datasets#readme)
-- [Data Science Interviews Questions](https://github.com/alexeygrigorev/data-science-interviews)
-- [Awesome Explainable Graph Reasoning](https://github.com/AstraZeneca/awesome-explainable-graph-reasoning)
-- [Top Data Science Interview Questions](https://www.interviewbit.com/data-science-interview-questions/)
-- [Awesome Drug Synergy, Interaction and Polypharmacy Prediction](https://github.com/AstraZeneca/awesome-drug-pair-scoring)
-- [Deep Learning Interview Questions](https://www.adaface.com/blog/deep-learning-interview-questions/)
-- [Top Future Trends in Data Science in 2023](https://medium.com/the-modern-scientist/top-future-trends-in-data-science-in-2023-3e616c8998b8)
-- [How Generative AI Is Changing Creative Work](https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work)
-- [What is generative AI?](https://www.techtarget.com/searchenterpriseai/definition/generative-AI)
-
-### Hobby
-- [Awesome Music Production](https://github.com/ad-si/awesome-music-production)
-
-
-
-
+Real-time visualization library - https://github.com/fastly/epoch
diff --git a/readmes/datascience.md2 b/readmes/datascience.md2
index 78df40a..0658fb8 100644
--- a/readmes/datascience.md2
+++ b/readmes/datascience.md2
@@ -1,10 +1,1120 @@
-awesome-data-science
-====================
+
-A curated list of amazingly awesome open source data science resources.
+# AWESOME DATA SCIENCE
-Data Visualization
+[](https://github.com/sindresorhus/awesome)
-A JavaScript visualization library for HTML and SVG - http://d3js.org
+**An open-source Data Science repository to learn and apply towards solving real world problems.**
-Real-time visualization library - https://github.com/fastly/epoch
+This is a shortcut path to start studying **Data Science**. Just follow the steps to answer the questions, "What is Data Science and what should I study to learn Data Science?"
+
+## Sponsors
+
+| Sponsor | Pitch |
+| --- | --- |
+| --- | Be the first to sponsor! `github@academic.io` |
+
+
+
+
+
+## Table of Contents
+
+- [What is Data Science?](#what-is-data-science)
+- [Where do I Start?](#where-do-i-start)
+- [Training Resources](#training-resources)
+ - [Tutorials](#tutorials)
+ - [Free Courses](#free-courses)
+ - [Massively Open Online Courses](#moocs)
+ - [Intensive Programs](#intensive-programs)
+ - [Colleges](#colleges)
+- [The Data Science Toolbox](#the-data-science-toolbox)
+ - [Algorithms](#algorithms)
+ - [Supervised Learning](#supervised-learning)
+ - [Unsupervised Learning](#unsupervised-learning)
+ - [Semi-Supervised Learning](#semi-supervised-learning)
+ - [Reinforcement Learning](#reinforcement-learning)
+ - [Data Mining Algorithms](#data-mining-algorithms)
+ - [Deep Learning Architectures](#deep-learning-architectures)
+ - [General Machine Learning Packages](#general-machine-learning-packages)
+ - [Deep Learning Packages](#deep-learning-packages)
+ - [PyTorch Ecosystem](#pytorch-ecosystem)
+ - [TensorFlow Ecosystem](#tensorflow-ecosystem)
+ - [Keras Ecosystem](#keras-ecosystem)
+ - [Visualization Tools](#visualization-tools)
+ - [Miscellaneous Tools](#miscellaneous-tools)
+- [Literature and Media](#literature-and-media)
+ - [Books](#books)
+ - [Book Deals (Affiliated)](#book-deals-affiliated-)
+ - [Journals, Publications, and Magazines](#journals-publications-and-magazines)
+ - [Newsletters](#newsletters)
+ - [Bloggers](#bloggers)
+ - [Presentations](#presentations)
+ - [Podcasts](#podcasts)
+ - [YouTube Videos & Channels](#youtube-videos--channels)
+- [Socialize](#socialize)
+ - [Facebook Accounts](#facebook-accounts)
+ - [Twitter Accounts](#twitter-accounts)
+ - [Telegram Channels](#telegram-channels)
+ - [Slack Communities](#slack-communities)
+ - [GitHub Groups](#github-groups)
+ - [Data Science Competitions](#data-science-competitions)
+- [Fun](#fun)
+ - [Infographics](#infographics)
+ - [Datasets](#datasets)
+ - [Comics](#comics)
+- [Other Awesome Lists](#other-awesome-lists)
+ - [Hobby](#hobby)
+
+## What is Data Science?
+**[`^ back to top ^`](#awesome-data-science)**
+
+Data Science is one of the hottest topics on the Computer and Internet farmland nowadays. People have gathered data from applications and systems until today and now is the time to analyze them. The next steps are producing suggestions from the data and creating predictions about the future. [Here](https://www.quora.com/Data-Science/What-is-data-science) you can find the biggest question for **Data Science** and hundreds of answers from experts.
+
+
+| Link | Preview |
+| --- | --- |
+| [What is Data Science @ O'reilly](https://www.oreilly.com/ideas/what-is-data-science) | _Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: “here’s a lot of data, what can you make from it?”_ |
+| [What is Data Science @ Quora](https://www.quora.com/Data-Science/What-is-data-science) | Data Science is a combination of a number of aspects of Data such as Technology, Algorithm development, and data interference to study the data, analyse it, and find innovative solutions to difficult problems. Basically Data Science is all about Analysing data and driving for business growth by finding creative ways. |
+| [The sexiest job of 21st century](https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century) | _Data scientists today are akin to Wall Street “quants” of the 1980s and 1990s. In those days people with backgrounds in physics and math streamed to investment banks and hedge funds, where they could devise entirely new algorithms and data strategies. Then a variety of universities developed master’s programs in financial engineering, which churned out a second generation of talent that was more accessible to mainstream firms. The pattern was repeated later in the 1990s with search engineers, whose rarefied skills soon came to be taught in computer science programs._ |
+| [Wikipedia](https://en.wikipedia.org/wiki/Data_science) | _Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data._ |
+| [How to Become a Data Scientist](https://www.mastersindatascience.org/careers/data-scientist/) | _Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations._ |
+| [a very short history of #datascience](https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/) | _The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science. The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms._ |
+|[Software Development Resources for Data Scientists](https://www.rstudio.com/blog/software-development-resources-for-data-scientists/)|_Data scientists concentrate on making sense of data through exploratory analysis, statistics, and models. Software developers apply a separate set of knowledge with different tools. Although their focus may seem unrelated, data science teams can benefit from adopting software development best practices. Version control, automated testing, and other dev skills help create reproducible, production-ready code and tools._|
+
+## Where do I Start?
+**[`^ back to top ^`](#awesome-data-science)**
+
+While not strictly necessary, having a programming language is a crucial skill to be effective as a data scientist. Currently, the most popular language is _Python_, closely followed by _R_. Python is a general-purpose scripting language that sees applications in a wide variety of fields. R is a domain-specific language for statistics, which contains a lot of common statistics tools out of the box.
+
+[Python](https://python.org/) is by far the most popular language in science, due in no small part to the ease at which it can be used and the vibrant ecosystem of user-generated packages. To install packages, there are two main methods: Pip (invoked as `pip install`), the package manager that comes bundled with Python, and [Anaconda](https://www.anaconda.com) (invoked as `conda install`), a powerful package manager that can install packages for Python, R, and can download executables like Git.
+
+Unlike R, Python was not built from the ground up with data science in mind, but there are plenty of third party libraries to make up for this. A much more exhaustive list of packages can be found later in this document, but these four packages are a good set of choices to start your data science journey with: [Scikit-Learn](https://scikit-learn.org/stable/index.html) is a general-purpose data science package which implements the most popular algorithms - it also includes rich documentation, tutorials, and examples of the models it implements. Even if you prefer to write your own implementations, Scikit-Learn is a valuable reference to the nuts-and-bolts behind many of the common algorithms you'll find. With [Pandas](https://pandas.pydata.org/), one can collect and analyze their data into a convenient table format. [Numpy](https://numpy.org/) provides very fast tooling for mathematical operations, with a focus on vectors and matrices. [Seaborn](https://seaborn.pydata.org/), itself based on the [Matplotlib](https://matplotlib.org/) package, is a quick way to generate beautiful visualizations of your data, with many good defaults available out of the box, as well as a gallery showing how to produce many common visualizations of your data.
+
+ When embarking on your journey to becoming a data scientist, the choice of language isn't particularly important, and both Python and R have their pros and cons. Pick a language you like, and check out one of the [Free courses](#free-courses) we've listed below!
+
+## Real World
+**[`^ back to top ^`](#awesome-data-science)**
+
+Data science is a powerful tool that is utilized in various fields to solve real-world problems by extracting insights and patterns from complex data.
+
+### Disaster
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [deprem-ml](https://huggingface.co/deprem-ml) [AYA: Açık Yazılım Ağı](https://linktr.ee/acikyazilimagi) (+25k developers) is trying to help disaster response using artificial intelligence. Everything is open-sourced [afet.org](https://afet.org).
+
+
+
+## Training Resources
+**[`^ back to top ^`](#awesome-data-science)**
+
+How do you learn data science? By doing data science, of course! Okay, okay - that might not be particularly helpful when you're first starting out. In this section, we've listed some learning resources, in rough order from least to greatest commitment - [Tutorials](#tutorials), [Massively Open Online Courses (MOOCs)](#moocs), [Intensive Programs](#intensive-programs), and [Colleges](#colleges).
+
+
+### Tutorials
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [1000 Data Science Projects](https://cloud.blobcity.com/#/ps/explore) you can run on the browser with IPython.
+- [#tidytuesday](https://github.com/rfordatascience/tidytuesday) A weekly data project aimed at the R ecosystem.
+- [Data science your way](https://github.com/jadianes/data-science-your-way)
+- [PySpark Cheatsheet](https://github.com/kevinschaich/pyspark-cheatsheet)
+- [Machine Learning, Data Science and Deep Learning with Python ](https://www.manning.com/livevideo/machine-learning-data-science-and-deep-learning-with-python)
+- [How To Label Data](https://www.lighttag.io/how-to-label-data/)
+- [Your Guide to Latent Dirichlet Allocation](https://medium.com/@lettier/how-does-lda-work-ill-explain-using-emoji-108abf40fa7d)
+- [Over 1000 Data Science Online Courses at Classpert Online Search Engine](https://classpert.com/search/data-science)
+- [Tutorials of source code from the book Genetic Algorithms with Python by Clinton Sheppard](https://github.com/handcraftsman/GeneticAlgorithmsWithPython)
+- [Tutorials to get started on signal processing for machine learning](https://github.com/jinglescode/python-signal-processing)
+- [Realtime deployment](https://www.microprediction.com/python-1) Tutorial on Python time-series model deployment.
+- [Python for Data Science: A Beginner’s Guide](https://learntocodewith.me/posts/python-for-data-science/)
+- [Minimum Viable Study Plan for Machine Learning Interviews](https://github.com/khangich/machine-learning-interview)
+- [Understand and Know Machine Learning Engineering by Building Solid Projects](http://mlzoomcamp.com/)
+- [12 free Data Science projects to practice Python and Pandas](https://www.datawars.io/articles/12-free-data-science-projects-to-practice-python-and-pandas)
+
+
+### Free Courses
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [Data Scientist with R](https://www.datacamp.com/tracks/data-scientist-with-r)
+- [Data Scientist with Python](https://www.datacamp.com/tracks/data-scientist-with-python)
+- [Genetic Algorithms OCW Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-1-introduction-and-scope/)
+- [AI Expert Roadmap](https://github.com/AMAI-GmbH/AI-Expert-Roadmap) - Roadmap to becoming an Artificial Intelligence Expert
+- [Convex Optimization](https://www.edx.org/course/convex-optimization) - Convex Optimization (basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory...)
+- [Skillcombo - Data Science](https://skillcombo.com/courses/development/data-science/free/) - 1000+ free online Data Science courses
+- [Learning from Data](https://home.work.caltech.edu/telecourse.html) - Introduction to machine learning covering basic theory, algorithms and applications
+- [Kaggle](https://www.kaggle.com/learn) - Learn about Data Science, Machine Learning, Python etc
+- [ML Observability Fundamentals](https://arize.com/ml-observability-fundamentals/) - Learn how to monitor and root-cause production ML issues.
+- [Weights & Biases Effective MLOps: Model Development](https://www.wandb.courses/courses/effective-mlops-model-development) - Free Course and Certification for building an end-to-end machine using W&B
+- [Python for Machine Learning](https://globalaihub.com/courses/introduction-to-python-the-road-to-machine-learning/) - Start your journey to machine learning with Python, one of the most powerful programming languages.
+- [Python for Data Science by Scaler](https://www.scaler.com/topics/course/python-for-data-science/) - This course is designed to empower beginners with the essential skills to excel in today's data-driven world. The comprehensive curriculum will give you a solid foundation in statistics, programming, data visualization, and machine learning.
+- [MLSys-NYU-2022](https://github.com/jacopotagliabue/MLSys-NYU-2022/tree/main) - Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022.
+- [Hands-on Train and Deploy ML](https://github.com/Paulescu/hands-on-train-and-deploy-ml) - A hands-on course to train and deploy a serverless API that predicts crypto prices.
+- [LLMOps: Building Real-World Applications With Large Language Models](https://www.comet.com/site/llm-course/) - Learn to build modern software with LLMs using the newest tools and techniques in the field.
+
+
+### MOOC's
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [Coursera Introduction to Data Science](https://www.coursera.org/specializations/data-science)
+- [Data Science - 9 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/jhu-data-science)
+- [Data Mining - 5 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/data-mining)
+- [Machine Learning – 5 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/machine-learning)
+- [CS 109 Data Science](https://cs109.github.io/2015/)
+- [OpenIntro](https://www.openintro.org/)
+- [CS 171 Visualization](https://www.cs171.org/#!index.md)
+- [Process Mining: Data science in Action](https://www.coursera.org/learn/process-mining)
+- [Oxford Deep Learning](https://www.cs.ox.ac.uk/projects/DeepLearn/)
+- [Oxford Deep Learning - video](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu)
+- [Oxford Machine Learning](https://www.cs.ox.ac.uk/research/ai_ml/index.html)
+- [UBC Machine Learning - video](https://www.cs.ubc.ca/~nando/540-2013/lectures.html)
+- [Data Science Specialization](https://github.com/DataScienceSpecialization/courses)
+- [Coursera Big Data Specialization](https://www.coursera.org/specializations/big-data)
+- [Statistical Thinking for Data Science and Analytics by Edx](https://www.edx.org/course/statistical-thinking-for-data-science-and-analytic)
+- [Cognitive Class AI by IBM](https://cognitiveclass.ai/)
+- [Udacity - Deep Learning](https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187)
+- [Keras in Motion](https://www.manning.com/livevideo/keras-in-motion)
+- [Microsoft Professional Program for Data Science](https://academy.microsoft.com/en-us/professional-program/tracks/data-science/)
+- [COMP3222/COMP6246 - Machine Learning Technologies](https://tdgunes.com/COMP6246-2019Fall/)
+- [CS 231 - Convolutional Neural Networks for Visual Recognition](https://cs231n.github.io/)
+- [Coursera Tensorflow in practice](https://www.coursera.org/professional-certificates/tensorflow-in-practice)
+- [Coursera Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning)
+- [365 Data Science Course](https://365datascience.com/)
+- [Coursera Natural Language Processing Specialization](https://www.coursera.org/specializations/natural-language-processing)
+- [Coursera GAN Specialization](https://www.coursera.org/specializations/generative-adversarial-networks-gans)
+- [Codecademy's Data Science](https://www.codecademy.com/learn/paths/data-science)
+- [Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/) - Linear Algebra course by Gilbert Strang
+- [A 2020 Vision of Linear Algebra (G. Strang)](https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-linear-algebra-spring-2020/)
+- [Python for Data Science Foundation Course](https://intellipaat.com/academy/course/python-for-data-science-free-training/)
+- [Data Science: Statistics & Machine Learning](https://www.coursera.org/specializations/data-science-statistics-machine-learning)
+- [Machine Learning Engineering for Production (MLOps)](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops)
+- [Recommender Systems Specialization from University of Minnesota](https://www.coursera.org/specializations/recommender-systems) is an intermediate/advanced level specialization focused on Recommender System on the Coursera platform.
+- [Stanford Artificial Intelligence Professional Program](https://online.stanford.edu/programs/artificial-intelligence-professional-program)
+- [Data Scientist with Python](https://app.datacamp.com/learn/career-tracks/data-scientist-with-python)
+- [Programming with Julia](https://www.udemy.com/course/programming-with-julia/)
+- [Scaler Data Science & Machine Learning Program](https://www.scaler.com/data-science-course/)
+
+
+
+### Intensive Programs
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [S2DS](https://www.s2ds.org/)
+
+
+### Colleges
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [A list of colleges and universities offering degrees in data science.](https://github.com/ryanswanstrom/awesome-datascience-colleges)
+- [Data Science Degree @ Berkeley](https://ischoolonline.berkeley.edu/data-science/)
+- [Data Science Degree @ UVA](https://datascience.virginia.edu/)
+- [Data Science Degree @ Wisconsin](https://datasciencedegree.wisconsin.edu/)
+- [BS in Data Science & Applications](https://study.iitm.ac.in/ds/)
+- [MS in Computer Information Systems @ Boston University](https://www.bu.edu/online/programs/graduate-programs/computer-information-systems-masters-degree/)
+- [MS in Business Analytics @ ASU Online](https://asuonline.asu.edu/online-degree-programs/graduate/master-science-business-analytics/)
+- [MS in Applied Data Science @ Syracuse](https://ischool.syr.edu/academics/applied-data-science-masters-degree/)
+- [M.S. Management & Data Science @ Leuphana](https://www.leuphana.de/en/graduate-school/masters-programmes/management-data-science.html)
+- [Master of Data Science @ Melbourne University](https://study.unimelb.edu.au/find/courses/graduate/master-of-data-science/#overview)
+- [Msc in Data Science @ The University of Edinburgh](https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=902)
+- [Master of Management Analytics @ Queen's University](https://smith.queensu.ca/grad_studies/mma/index.php)
+- [Master of Data Science @ Illinois Institute of Technology](https://www.iit.edu/academics/programs/data-science-mas)
+- [Master of Applied Data Science @ The University of Michigan](https://www.si.umich.edu/programs/master-applied-data-science-online)
+- [Master Data Science and Artificial Intelligence @ Eindhoven University of Technology](https://www.tue.nl/en/education/graduate-school/master-data-science-and-artificial-intelligence/)
+- [Master's Degree in Data Science and Computer Engineering @ University of Granada](https://masteres.ugr.es/datcom/)
+
+## The Data Science Toolbox
+**[`^ back to top ^`](#awesome-data-science)**
+
+This section is a collection of packages, tools, algorithms, and other useful items in the data science world.
+
+### Algorithms
+**[`^ back to top ^`](#awesome-data-science)**
+
+These are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it.
+
+#### Three kinds of Machine Learning Systems
+
+- Based on training with human supervision
+- Based on learning incrementally on fly
+- Based on data points comparison and pattern detection
+
+#### Supervised Learning
+
+- [Regression](https://en.wikipedia.org/wiki/Regression)
+- [Linear Regression](https://en.wikipedia.org/wiki/Linear_regression)
+- [Ordinary Least Squares](https://en.wikipedia.org/wiki/Ordinary_least_squares)
+- [Logistic Regression](https://en.wikipedia.org/wiki/Logistic_regression)
+- [Stepwise Regression](https://en.wikipedia.org/wiki/Stepwise_regression)
+- [Multivariate Adaptive Regression Splines](https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_spline)
+- [Softmax Regression](https://d2l.ai/chapter_linear-classification/softmax-regression.html)
+- [Locally Estimated Scatterplot Smoothing](https://en.wikipedia.org/wiki/Local_regression)
+- Classification
+ - [k-nearest neighbor](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)
+ - [Support Vector Machines](https://en.wikipedia.org/wiki/Support_vector_machine)
+ - [Decision Trees](https://en.wikipedia.org/wiki/Decision_tree)
+ - [ID3 algorithm](https://en.wikipedia.org/wiki/ID3_algorithm)
+ - [C4.5 algorithm](https://en.wikipedia.org/wiki/C4.5_algorithm)
+- [Ensemble Learning](https://scikit-learn.org/stable/modules/ensemble.html)
+ - [Boosting](https://en.wikipedia.org/wiki/Boosting_(machine_learning))
+ - [Stacking](https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python)
+ - [Bagging](https://en.wikipedia.org/wiki/Bootstrap_aggregating)
+ - [Random Forest](https://en.wikipedia.org/wiki/Random_forest)
+ - [AdaBoost](https://en.wikipedia.org/wiki/AdaBoost)
+
+#### Unsupervised Learning
+- [Clustering](https://scikit-learn.org/stable/modules/clustering.html#clustering)
+ - [Hierchical clustering](https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering)
+ - [k-means](https://scikit-learn.org/stable/modules/clustering.html#k-means)
+ - [Density-based clustering](https://scikit-learn.org/stable/modules/clustering.html#dbscan)
+ - [Fuzzy clustering](https://en.wikipedia.org/wiki/Fuzzy_clustering)
+ - [Mixture models](https://en.wikipedia.org/wiki/Mixture_model)
+- [Dimension Reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction)
+ - [Principal Component Analysis (PCA)](https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca)
+ - [t-SNE; t-distributed Stochastic Neighbor Embedding](https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca)
+ - [Factor Analysis](https://scikit-learn.org/stable/modules/decomposition.html#factor-analysis)
+ - [Latent Dirichlet Allocation (LDA)](https://scikit-learn.org/stable/modules/decomposition.html#latent-dirichlet-allocation-lda)
+- [Neural Networks](https://en.wikipedia.org/wiki/Neural_network)
+- [Self-organizing map](https://en.wikipedia.org/wiki/Self-organizing_map)
+- [Adaptive resonance theory](https://en.wikipedia.org/wiki/Adaptive_resonance_theory)
+- [Hidden Markov Models (HMM)](https://en.wikipedia.org/wiki/Hidden_Markov_model)
+
+#### Semi-Supervised Learning
+
+- S3VM
+- [Clustering](https://en.wikipedia.org/wiki/Weak_supervision#Cluster_assumption)
+- [Generative models](https://en.wikipedia.org/wiki/Weak_supervision#Generative_models)
+- [Low-density separation](https://en.wikipedia.org/wiki/Weak_supervision#Low-density_separation)
+- [Laplacian regularization](https://en.wikipedia.org/wiki/Weak_supervision#Laplacian_regularization)
+- [Heuristic approaches](https://en.wikipedia.org/wiki/Weak_supervision#Heuristic_approaches)
+
+#### Reinforcement Learning
+
+- [Q Learning](https://en.wikipedia.org/wiki/Q-learning)
+- [SARSA (State-Action-Reward-State-Action) algorithm](https://en.wikipedia.org/wiki/State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action)
+- [Temporal difference learning](https://en.wikipedia.org/wiki/Temporal_difference_learning#:~:text=Temporal%20difference%20(TD)%20learning%20refers,estimate%20of%20the%20value%20function.)
+
+#### Data Mining Algorithms
+
+- [C4.5](https://en.wikipedia.org/wiki/C4.5_algorithm)
+- [k-Means](https://en.wikipedia.org/wiki/K-means_clustering)
+- [SVM (Support Vector Machine)](https://en.wikipedia.org/wiki/Support_vector_machine)
+- [Apriori](https://en.wikipedia.org/wiki/Apriori_algorithm)
+- [EM (Expectation-Maximization)](https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
+- [PageRank](https://en.wikipedia.org/wiki/PageRank)
+- [AdaBoost](https://en.wikipedia.org/wiki/AdaBoost)
+- [KNN (K-Nearest Neighbors)](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)
+- [Naive Bayes](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
+- [CART (Classification and Regression Trees)](https://en.wikipedia.org/wiki/Decision_tree_learning)
+
+
+
+#### Deep Learning architectures
+
+- [Multilayer Perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron)
+- [Convolutional Neural Network (CNN)](https://en.wikipedia.org/wiki/Convolutional_neural_network)
+- [Recurrent Neural Network (RNN)](https://en.wikipedia.org/wiki/Recurrent_neural_network)
+- [Boltzmann Machines](https://en.wikipedia.org/wiki/Boltzmann_machine)
+- [Autoencoder](https://www.tensorflow.org/tutorials/generative/autoencoder)
+- [Generative Adversarial Network (GAN)](https://developers.google.com/machine-learning/gan/gan_structure)
+- [Self-Organized Maps](https://en.wikipedia.org/wiki/Self-organizing_map)
+- [Transformer](https://www.tensorflow.org/text/tutorials/transformer)
+- [Conditional Random Field (CRF)](https://towardsdatascience.com/conditional-random-fields-explained-e5b8256da776)
+
+### General Machine Learning Packages
+**[`^ back to top ^`](#awesome-data-science)**
+
+* [scikit-learn](https://scikit-learn.org/)
+* [scikit-multilearn](https://github.com/scikit-multilearn/scikit-multilearn)
+* [sklearn-expertsys](https://github.com/tmadl/sklearn-expertsys)
+* [scikit-feature](https://github.com/jundongl/scikit-feature)
+* [scikit-rebate](https://github.com/EpistasisLab/scikit-rebate)
+* [seqlearn](https://github.com/larsmans/seqlearn)
+* [sklearn-bayes](https://github.com/AmazaspShumik/sklearn-bayes)
+* [sklearn-crfsuite](https://github.com/TeamHG-Memex/sklearn-crfsuite)
+* [sklearn-deap](https://github.com/rsteca/sklearn-deap)
+* [sigopt_sklearn](https://github.com/sigopt/sigopt-sklearn)
+* [sklearn-evaluation](https://github.com/edublancas/sklearn-evaluation)
+* [scikit-image](https://github.com/scikit-image/scikit-image)
+* [scikit-opt](https://github.com/guofei9987/scikit-opt)
+* [scikit-posthocs](https://github.com/maximtrp/scikit-posthocs)
+* [pystruct](https://github.com/pystruct/pystruct)
+* [Shogun](https://www.shogun-toolbox.org/)
+* [xLearn](https://github.com/aksnzhy/xlearn)
+* [cuML](https://github.com/rapidsai/cuml)
+* [causalml](https://github.com/uber/causalml)
+* [mlpack](https://github.com/mlpack/mlpack)
+* [MLxtend](https://github.com/rasbt/mlxtend)
+* [modAL](https://github.com/modAL-python/modAL)
+* [Sparkit-learn](https://github.com/lensacom/sparkit-learn)
+* [hyperlearn](https://github.com/danielhanchen/hyperlearn)
+* [dlib](https://github.com/davisking/dlib)
+* [imodels](https://github.com/csinva/imodels)
+* [RuleFit](https://github.com/christophM/rulefit)
+* [pyGAM](https://github.com/dswah/pyGAM)
+* [Deepchecks](https://github.com/deepchecks/deepchecks)
+* [scikit-survival](https://scikit-survival.readthedocs.io/en/stable)
+
+### Deep Learning Packages
+
+#### PyTorch Ecosystem
+* [PyTorch](https://github.com/pytorch/pytorch)
+* [torchvision](https://github.com/pytorch/vision)
+* [torchtext](https://github.com/pytorch/text)
+* [torchaudio](https://github.com/pytorch/audio)
+* [ignite](https://github.com/pytorch/ignite)
+* [PyTorchNet](https://github.com/pytorch/tnt)
+* [PyToune](https://github.com/GRAAL-Research/poutyne)
+* [skorch](https://github.com/skorch-dev/skorch)
+* [PyVarInf](https://github.com/ctallec/pyvarinf)
+* [pytorch_geometric](https://github.com/pyg-team/pytorch_geometric)
+* [GPyTorch](https://github.com/cornellius-gp/gpytorch)
+* [pyro](https://github.com/pyro-ppl/pyro)
+* [Catalyst](https://github.com/catalyst-team/catalyst)
+* [pytorch_tabular](https://github.com/manujosephv/pytorch_tabular)
+* [Yolov3](https://github.com/ultralytics/yolov3)
+* [Yolov5](https://github.com/ultralytics/yolov5)
+* [Yolov8](https://github.com/ultralytics/ultralytics)
+
+#### TensorFlow Ecosystem
+* [TensorFlow](https://github.com/tensorflow/tensorflow)
+* [TensorLayer](https://github.com/tensorlayer/TensorLayer)
+* [TFLearn](https://github.com/tflearn/tflearn)
+* [Sonnet](https://github.com/deepmind/sonnet)
+* [tensorpack](https://github.com/tensorpack/tensorpack)
+* [TRFL](https://github.com/deepmind/trfl)
+* [Polyaxon](https://github.com/polyaxon/polyaxon)
+* [NeuPy](https://github.com/itdxer/neupy)
+* [tfdeploy](https://github.com/riga/tfdeploy)
+* [tensorflow-upstream](https://github.com/ROCmSoftwarePlatform/tensorflow-upstream)
+* [TensorFlow Fold](https://github.com/tensorflow/fold)
+* [tensorlm](https://github.com/batzner/tensorlm)
+* [TensorLight](https://github.com/bsautermeister/tensorlight)
+* [Mesh TensorFlow](https://github.com/tensorflow/mesh)
+* [Ludwig](https://github.com/ludwig-ai/ludwig)
+* [TF-Agents](https://github.com/tensorflow/agents)
+* [TensorForce](https://github.com/tensorforce/tensorforce)
+
+#### Keras Ecosystem
+
+* [Keras](https://keras.io)
+* [keras-contrib](https://github.com/keras-team/keras-contrib)
+* [Hyperas](https://github.com/maxpumperla/hyperas)
+* [Elephas](https://github.com/maxpumperla/elephas)
+* [Hera](https://github.com/keplr-io/hera)
+* [Spektral](https://github.com/danielegrattarola/spektral)
+* [qkeras](https://github.com/google/qkeras)
+* [keras-rl](https://github.com/keras-rl/keras-rl)
+* [Talos](https://github.com/autonomio/talos)
+
+#### Visualization Tools
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [altair](https://altair-viz.github.io/)
+- [addepar](https://opensource.addepar.com/ember-charts/#/overview)
+- [amcharts](https://www.amcharts.com/)
+- [anychart](https://www.anychart.com/)
+- [bokeh](https://bokeh.org/)
+- [Comet](https://www.comet.com/site/products/ml-experiment-tracking/?utm_source=awesome-datascience)
+- [slemma](https://slemma.com/)
+- [cartodb](https://cartodb.github.io/odyssey.js/)
+- [Cube](https://square.github.io/cube/)
+- [d3plus](https://d3plus.org/)
+- [Data-Driven Documents(D3js)](https://d3js.org/)
+- [dygraphs](https://dygraphs.com/)
+- [ECharts](https://echarts.baidu.com/index-en.html)
+- [exhibit](https://www.simile-widgets.org/exhibit/)
+- [gephi](https://gephi.org/)
+- [ggplot2](https://ggplot2.tidyverse.org/)
+- [Glue](http://docs.glueviz.org/en/latest/index.html)
+- [Google Chart Gallery](https://developers.google.com/chart/interactive/docs/gallery)
+- [highcarts](https://www.highcharts.com/)
+- [import.io](https://www.import.io/)
+- [jqplot](https://www.jqplot.com/)
+- [Matplotlib](https://matplotlib.org/)
+- [nvd3](https://nvd3.org/)
+- [Netron](https://github.com/lutzroeder/netron)
+- [Openrefine](https://openrefine.org/)
+- [plot.ly](https://plot.ly/)
+- [raw](https://rawgraphs.io)
+- [Resseract Lite](https://github.com/abistarun/resseract-lite)
+- [Seaborn](https://seaborn.pydata.org/)
+- [techanjs](https://techanjs.org/)
+- [Timeline](https://timeline.knightlab.com/)
+- [variancecharts](https://variancecharts.com/index.html)
+- [vida](https://vida.io/)
+- [vizzu](https://github.com/vizzuhq/vizzu-lib)
+- [Wrangler](http://vis.stanford.edu/wrangler/)
+- [r2d3](https://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
+- [NetworkX](https://networkx.org/)
+- [Redash](https://redash.io/)
+- [C3](https://c3js.org/)
+- [TensorWatch](https://github.com/microsoft/tensorwatch)
+- [geomap](https://pypi.org/project/geomap/)
+
+### Miscellaneous Tools
+**[`^ back to top ^`](#awesome-data-science)**
+
+| Link | Description |
+| --- | --- |
+| [The Data Science Lifecycle Process](https://github.com/dslp/dslp) | The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo |
+| [Data Science Lifecycle Template Repo](https://github.com/dslp/dslp-repo-template) | Template repository for data science lifecycle project |
+| [RexMex](https://github.com/AstraZeneca/rexmex) | A general purpose recommender metrics library for fair evaluation. |
+| [ChemicalX](https://github.com/AstraZeneca/chemicalx) | A PyTorch based deep learning library for drug pair scoring. |
+| [PyTorch Geometric Temporal](https://github.com/benedekrozemberczki/pytorch_geometric_temporal) | Representation learning on dynamic graphs. |
+| [Little Ball of Fur](https://github.com/benedekrozemberczki/littleballoffur) | A graph sampling library for NetworkX with a Scikit-Learn like API. |
+| [Karate Club](https://github.com/benedekrozemberczki/karateclub) | An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API. |
+| [ML Workspace](https://github.com/ml-tooling/ml-workspace) | All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a Docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code) |
+| [Neptune.ai](https://neptune.ai) | Community-friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility. |
+| [steppy](https://github.com/minerva-ml/steppy) | Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces very simple interface that enables clean machine learning pipeline design. |
+| [steppy-toolkit](https://github.com/minerva-ml/steppy-toolkit) | Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective. |
+| [Datalab from Google](https://cloud.google.com/datalab/docs/) | easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively. |
+| [Hortonworks Sandbox](https://www.cloudera.com/downloads/hortonworks-sandbox.html) | is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials. |
+| [R](https://www.r-project.org/) | is a free software environment for statistical computing and graphics. |
+| [Tidyverse](https://www.tidyverse.org/) | is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. |
+| [RStudio](https://www.rstudio.com) | IDE – powerful user interface for R. It’s free and open source, and works on Windows, Mac, and Linux. |
+| [Python - Pandas - Anaconda](https://www.anaconda.com) | Completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing |
+| [Pandas GUI](https://github.com/adrotog/PandasGUI) | Pandas GUI |
+| [Scikit-Learn](https://scikit-learn.org/stable/) | Machine Learning in Python |
+| [NumPy](https://numpy.org/) | NumPy is fundamental for scientific computing with Python. It supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays. |
+| [Vaex](https://vaex.io/) | Vaex is a Python library that allows you to visualize large datasets and calculate statistics at high speeds. |
+| [SciPy](https://scipy.org/) | SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization. |
+| [Data Science Toolbox](https://www.coursera.org/learn/data-scientists-tools) | Coursera Course |
+| [Data Science Toolbox](https://datasciencetoolbox.org/) | Blog |
+| [Wolfram Data Science Platform](https://www.wolfram.com/data-science-platform/) | Take numerical, textual, image, GIS or other data and give it the Wolfram treatment, carrying out a full spectrum of data science analysis and visualization and automatically generate rich interactive reports—all powered by the revolutionary knowledge-based Wolfram Language. |
+| [Datadog](https://www.datadoghq.com/) | Solutions, code, and devops for high-scale data science. |
+| [Variance](https://variancecharts.com/) | Build powerful data visualizations for the web without writing JavaScript |
+| [Kite Development Kit](https://kitesdk.org/docs/current/index.html) | The Kite Software Development Kit (Apache License, Version 2.0), or Kite for short, is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem. |
+| [Domino Data Labs](https://www.dominodatalab.com) | Run, scale, share, and deploy your models — without any infrastructure or setup. |
+| [Apache Flink](https://flink.apache.org/) | A platform for efficient, distributed, general-purpose data processing. |
+| [Apache Hama](https://hama.apache.org/) | Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce. |
+| [Weka](https://www.cs.waikato.ac.nz/ml/weka/) | Weka is a collection of machine learning algorithms for data mining tasks. |
+| [Octave](https://www.gnu.org/software/octave/) | GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab) |
+| [Apache Spark](https://spark.apache.org/) | Lightning-fast cluster computing |
+| [Hydrosphere Mist](https://github.com/Hydrospheredata/mist) | a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services. |
+| [Data Mechanics](https://www.datamechanics.co) | A data science and engineering platform making Apache Spark more developer-friendly and cost-effective. |
+| [Caffe](https://caffe.berkeleyvision.org/) | Deep Learning Framework |
+| [Torch](https://torch.ch/) | A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT |
+| [Nervana's python based Deep Learning Framework](https://github.com/NervanaSystems/neon) | Intel® Nervana™ reference deep learning framework committed to best performance on all hardware. |
+| [Skale](https://github.com/skale-me/skale) | High performance distributed data processing in NodeJS |
+| [Aerosolve](https://airbnb.io/aerosolve/) | A machine learning package built for humans. |
+| [Intel framework](https://github.com/intel/idlf) | Intel® Deep Learning Framework |
+| [Datawrapper](https://www.datawrapper.de/) | An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at [github.com](https://github.com/datawrapper/datawrapper) |
+| [Tensor Flow](https://www.tensorflow.org/) | TensorFlow is an Open Source Software Library for Machine Intelligence |
+| [Natural Language Toolkit](https://www.nltk.org/) | An introductory yet powerful toolkit for natural language processing and classification |
+| [Annotation Lab](https://www.johnsnowlabs.com/annotation-lab/) | Free End-to-End No-Code platform for text annotation and DL model training/tuning. Out-of-the-box support for Named Entity Recognition, Classification, Relation extraction and Assertion Status Spark NLP models. Unlimited support for users, teams, projects, documents. |
+| [nlp-toolkit for node.js](https://www.npmjs.com/package/nlp-toolkit) | This module covers some basic nlp principles and implementations. The main focus is performance. When we deal with sample or training data in nlp, we quickly run out of memory. Therefore every implementation in this module is written as stream to only hold that data in memory that is currently processed at any step. |
+| [Julia](https://julialang.org) | high-level, high-performance dynamic programming language for technical computing |
+| [IJulia](https://github.com/JuliaLang/IJulia.jl) | a Julia-language backend combined with the Jupyter interactive environment |
+| [Apache Zeppelin](https://zeppelin.apache.org/) | Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more |
+| [Featuretools](https://github.com/alteryx/featuretools) | An open source framework for automated feature engineering written in python |
+| [Optimus](https://github.com/hi-primus/optimus) | Cleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend. |
+| [Albumentations](https://github.com/albumentations-team/albumentations) | А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, and detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops. |
+| [DVC](https://github.com/iterative/dvc) | An open-source data science version control system. It helps track, organize and make data science projects reproducible. In its very basic scenario it helps version control and share large data and model files. |
+| [Lambdo](https://github.com/asavinov/lambdo) | is a workflow engine that significantly simplifies data analysis by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation. |
+| [Feast](https://github.com/feast-dev/feast) | A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving. |
+| [Polyaxon](https://github.com/polyaxon/polyaxon) | A platform for reproducible and scalable machine learning and deep learning. |
+| [LightTag](https://www.lighttag.io/) | Text Annotation Tool for teams |
+| [UBIAI](https://ubiai.tools) | Easy-to-use text annotation tool for teams with most comprehensive auto-annotation features. Supports NER, relations and document classification as well as OCR annotation for invoice labeling |
+| [Trains](https://github.com/allegroai/clearml) | Auto-Magical Experiment Manager, Version Control & DevOps for AI |
+| [Hopsworks](https://github.com/logicalclocks/hopsworks) | Open-source data-intensive machine learning platform with a feature store. Ingest and manage features for both online (MySQL Cluster) and offline (Apache Hive) access, train and serve models at scale. |
+| [MindsDB](https://github.com/mindsdb/mindsdb) | MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code. |
+| [Lightwood](https://github.com/mindsdb/lightwood) | A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with an objective to build predictive models with one line of code. |
+| [AWS Data Wrangler](https://github.com/awslabs/aws-data-wrangler) | An open-source Python package that extends the power of Pandas library to AWS connecting DataFrames and AWS data related services (Amazon Redshift, AWS Glue, Amazon Athena, Amazon EMR, etc). |
+| [Amazon Rekognition](https://aws.amazon.com/rekognition/) | AWS Rekognition is a service that lets developers working with Amazon Web Services add image analysis to their applications. Catalog assets, automate workflows, and extract meaning from your media and applications.|
+| [Amazon Textract](https://aws.amazon.com/textract/) | Automatically extract printed text, handwriting, and data from any document. |
+| [Amazon Lookout for Vision](https://aws.amazon.com/lookout-for-vision/) | Spot product defects using computer vision to automate quality inspection. Identify missing product components, vehicle and structure damage, and irregularities for comprehensive quality control.|
+| [Amazon CodeGuru](https://aws.amazon.com/codeguru/) | Automate code reviews and optimize application performance with ML-powered recommendations.|
+| [CML](https://github.com/iterative/cml) | An open source toolkit for using continuous integration in data science projects. Automatically train and test models in production-like environments with GitHub Actions & GitLab CI, and autogenerate visual reports on pull/merge requests. |
+| [Dask](https://dask.org/) | An open source Python library to painlessly transition your analytics code to distributed computing systems (Big Data) |
+| [Statsmodels](https://www.statsmodels.org/stable/index.html) | A Python-based inferential statistics, hypothesis testing and regression framework |
+| [Gensim](https://radimrehurek.com/gensim/) | An open-source library for topic modeling of natural language text |
+| [spaCy](https://spacy.io/) | A performant natural language processing toolkit |
+| [Grid Studio](https://github.com/ricklamers/gridstudio) | Grid studio is a web-based spreadsheet application with full integration of the Python programming language. |
+|[Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook)|Python Data Science Handbook: full text in Jupyter Notebooks|
+| [Shapley](https://github.com/benedekrozemberczki/shapley) | A data-driven framework to quantify the value of classifiers in a machine learning ensemble. |
+| [DAGsHub](https://dagshub.com) | A platform built on open source tools for data, model and pipeline management. |
+| [Deepnote](https://deepnote.com) | A new kind of data science notebook. Jupyter-compatible, with real-time collaboration and running in the cloud. |
+| [Valohai](https://valohai.com) | An MLOps platform that handles machine orchestration, automatic reproducibility and deployment. |
+| [PyMC3](https://docs.pymc.io/) | A Python Library for Probabalistic Programming (Bayesian Inference and Machine Learning) |
+| [PyStan](https://pypi.org/project/pystan/) | Python interface to Stan (Bayesian inference and modeling) |
+| [hmmlearn](https://pypi.org/project/hmmlearn/) | Unsupervised learning and inference of Hidden Markov Models |
+| [Chaos Genius](https://github.com/chaos-genius/chaos_genius/) | ML powered analytics engine for outlier/anomaly detection and root cause analysis |
+| [Nimblebox](https://nimblebox.ai/) | A full-stack MLOps platform designed to help data scientists and machine learning practitioners around the world discover, create, and launch multi-cloud apps from their web browser. |
+| [Towhee](https://github.com/towhee-io/towhee) | A Python library that helps you encode your unstructured data into embeddings. |
+| [LineaPy](https://github.com/LineaLabs/lineapy) | Ever been frustrated with cleaning up long, messy Jupyter notebooks? With LineaPy, an open source Python library, it takes as little as two lines of code to transform messy development code into production pipelines. |
+| [envd](https://github.com/tensorchord/envd) | 🏕️ machine learning development environment for data science and AI/ML engineering teams |
+| [Explore Data Science Libraries](https://kandi.openweaver.com/explore/data-science) | A search engine 🔎 tool to discover & find a curated list of popular & new libraries, top authors, trending project kits, discussions, tutorials & learning resources |
+| [MLEM](https://github.com/iterative/mlem) | 🐶 Version and deploy your ML models following GitOps principles |
+| [MLflow](https://mlflow.org/) | MLOps framework for managing ML models across their full lifecycle |
+| [cleanlab](https://github.com/cleanlab/cleanlab) | Python library for data-centric AI and automatically detecting various issues in ML datasets |
+| [AutoGluon](https://github.com/awslabs/autogluon) | AutoML to easily produce accurate predictions for image, text, tabular, time-series, and multi-modal data |
+| [Arize AI](https://arize.com/) | Arize AI community tier observability tool for monitoring machine learning models in production and root-causing issues such as data quality and performance drift. |
+| [Aureo.io](https://aureo.io) | Aureo.io is a low-code platform that focuses on building artificial intelligence. It provides users with the capability to create pipelines, automations and integrate them with artificial intelligence models – all with their basic data. |
+| [ERD Lab](https://www.erdlab.io/) | Free cloud based entity relationship diagram (ERD) tool made for developers.
+| [Arize-Phoenix](https://docs.arize.com/phoenix) | MLOps in a notebook - uncover insights, surface problems, monitor, and fine tune your models. |
+| [Comet](https://github.com/comet-ml/comet-examples) | An MLOps platform with experiment tracking, model production management, a model registry, and full data lineage to support your ML workflow from training straight through to production. |
+| [CometLLM](https://github.com/comet-ml/comet-llm) | Log, track, visualize, and search your LLM prompts and chains in one easy-to-use, 100% open-source tool. |
+| [Synthical](https://synthical.com) | AI-powered collaborative environment for research. Find relevant papers, create collections to manage bibliography, and summarize content — all in one place |
+| [teeplot](https://github.com/mmore500/teeplot) | Workflow tool to automatically organize data visualization output |
+
+
+## Literature and Media
+**[`^ back to top ^`](#awesome-data-science)**
+
+This section includes some additional reading material, channels to watch, and talks to listen to.
+
+### Books
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [Data Science From Scratch: First Principles with Python](https://www.amazon.com/Data-Science-Scratch-Principles-Python-dp-1492041130/dp/1492041130/ref=dp_ob_title_bk)
+- [Artificial Intelligence with Python - Tutorialspoint](https://www.tutorialspoint.com/artificial_intelligence_with_python/artificial_intelligence_with_python_tutorial.pdf)
+- [Machine Learning from Scratch](https://dafriedman97.github.io/mlbook/content/introduction.html)
+- [Probabilistic Machine Learning: An Introduction](https://probml.github.io/pml-book/book1.html)
+- [A Comprehensive Guide to Machine Learning](https://www.eecs189.org/static/resources/comprehensive-guide.pdf)
+- [How to Lead in Data Science](https://www.manning.com/books/how-to-lead-in-data-science) - Early Access
+- [Fighting Churn With Data](https://www.manning.com/books/fighting-churn-with-data)
+- [Data Science at Scale with Python and Dask](https://www.manning.com/books/data-science-with-python-and-dask)
+- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)
+- [The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists](https://www.thedatasciencehandbook.com/)
+- [Think Like a Data Scientist](https://www.manning.com/books/think-like-a-data-scientist)
+- [Introducing Data Science](https://www.manning.com/books/introducing-data-science)
+- [Practical Data Science with R](https://www.manning.com/books/practical-data-science-with-r)
+- [Everyday Data Science](https://www.amazon.com/dp/B08TZ1MT3W/ref=cm_sw_r_cp_apa_fabc_a0ceGbWECF9A8) & [(cheaper PDF version)](https://gum.co/everydaydata)
+- [Exploring Data Science](https://www.manning.com/books/exploring-data-science) - free eBook sampler
+- [Exploring the Data Jungle](https://www.manning.com/books/exploring-the-data-jungle) - free eBook sampler
+- [Classic Computer Science Problems in Python](https://www.manning.com/books/classic-computer-science-problems-in-python)
+- [Math for Programmers](https://www.manning.com/books/math-for-programmers) Early access
+- [R in Action, Third Edition](https://www.manning.com/books/r-in-action-third-edition) Early Access
+- [Data Science Bookcamp](https://www.manning.com/books/data-science-bookcamp) Early access
+- [Data Science Thinking: The Next Scientific, Technological and Economic Revolution](https://www.springer.com/gp/book/9783319950914)
+- [Applied Data Science: Lessons Learned for the Data-Driven Business](https://www.springer.com/gp/book/9783030118204)
+- [The Data Science Handbook](https://www.amazon.com/Data-Science-Handbook-Field-Cady/dp/1119092949)
+- [Essential Natural Language Processing](https://www.manning.com/books/getting-started-with-natural-language-processing) - Early access
+- [Mining Massive Datasets](https://www.mmds.org/) - free e-book comprehended by an online course
+- [Pandas in Action](https://www.manning.com/books/pandas-in-action) - Early access
+- [Genetic Algorithms and Genetic Programming](https://www.taylorfrancis.com/books/9780429141973)
+- [Advances in Evolutionary Algorithms](https://www.intechopen.com/books/advances_in_evolutionary_algorithms) - Free Download
+- [Genetic Programming: New Approaches and Successful Applications](https://www.intechopen.com/books/genetic-programming-new-approaches-and-successful-applications) - Free Download
+- [Evolutionary Algorithms](https://www.intechopen.com/books/evolutionary-algorithms) - Free Download
+- [Advances in Genetic Programming, Vol. 3](https://www.cs.bham.ac.uk/~wbl/aigp3/) - Free Download
+- [Global Optimization Algorithms: Theory and Application](https://www.it-weise.de/projects/book.pdf) - Free Download
+- [Genetic Algorithms and Evolutionary Computation](https://www.talkorigins.org/faqs/genalg/genalg.html) - Free Download
+- [Convex Optimization](https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf) - Convex Optimization book by Stephen Boyd - Free Download
+- [Data Analysis with Python and PySpark](https://www.manning.com/books/data-analysis-with-python-and-pyspark) - Early Access
+- [R for Data Science](https://r4ds.had.co.nz/)
+- [Build a Career in Data Science](https://www.manning.com/books/build-a-career-in-data-science)
+- [Machine Learning Bookcamp](https://mlbookcamp.com/) - Early access
+- [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)
+- [Effective Data Science Infrastructure](https://www.manning.com/books/effective-data-science-infrastructure)
+- [Practical MLOps: How to Get Ready for Production Models](https://valohai.com/mlops-ebook/)
+- [Data Analysis with Python and PySpark](https://www.manning.com/books/data-analysis-with-python-and-pyspark)
+- [Regression, a Friendly guide](https://www.manning.com/books/regression-a-friendly-guide) - Early Access
+- [Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing](https://www.oreilly.com/library/view/streaming-systems/9781491983867/)
+- [Data Science at the Command Line: Facing the Future with Time-Tested Tools](https://www.oreilly.com/library/view/data-science-at/9781491947845/)
+- [Machine Learning - CIn UFPE](https://www.cin.ufpe.br/~cavmj/Machine%20-%20Learning%20-%20Tom%20Mitchell.pdf)
+- [Machine Learning with Python - Tutorialspoint](https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_tutorial.pdf)
+- [Deep Learning](https://www.deeplearningbook.org/)
+- [Designing Cloud Data Platforms](https://www.manning.com/books/designing-cloud-data-platforms) - Early Access
+- [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)
+- [The Elements of Statistical Learning: Data Mining, Inference, and Prediction](https://hastie.su.domains/ElemStatLearn/)
+- [Deep Learning with PyTorch](https://www.simonandschuster.com/books/Deep-Learning-with-PyTorch/Eli-Stevens/9781617295263)
+- [Neural Networks and Deep Learning](https://neuralnetworksanddeeplearning.com)
+- [Deep Learning Cookbook](https://www.oreilly.com/library/view/deep-learning-cookbook/9781491995839/)
+- [Introduction to Machine Learning with Python](https://www.oreilly.com/library/view/introduction-to-machine/9781449369880/)
+- [Artificial Intelligence: Foundations of Computational Agents, 2nd Edition](https://artint.info/index.html) - Free HTML version
+- [The Quest for Artificial Intelligence: A History of Ideas and Achievements](https://ai.stanford.edu/~nilsson/QAI/qai.pdf) - Free Download
+- [Graph Algorithms for Data Science](https://www.manning.com/books/graph-algorithms-for-data-science) - Early Access
+- [Data Mesh in Action](https://www.manning.com/books/data-mesh-in-action) - Early Access
+- [Julia for Data Analysis](https://www.manning.com/books/julia-for-data-analysis) - Early Access
+- [Casual Inference for Data Science](https://www.manning.com/books/julia-for-data-analysis) - Early Access
+- [Regular Expression Puzzles and AI Coding Assistants](https://www.manning.com/books/regular-expression-puzzles-and-ai-coding-assistants) by David Mertz
+- [Dive into Deep Learning](https://d2l.ai/)
+- [Data for All](https://www.manning.com/books/data-for-all)
+- [Interpretable Machine Learning: A Guide for Making Black Box Models Explainable](https://christophm.github.io/interpretable-ml-book/) - Free GitHub version
+- [Foundations of Data Science](https://www.cs.cornell.edu/jeh/book.pdf) Free Download
+- [Comet for DataScience: Enhance your ability to manage and optimize the life cycle of your data science project](https://www.amazon.com/Comet-Data-Science-Enhance-optimize/dp/1801814430)
+- [Software Engineering for Data Scientists](https://www.manning.com/books/software-engineering-for-data-scientists) - Early Access
+- [Julia for Data Science](https://www.manning.com/books/julia-for-data-science) - Early Access
+- [An Introduction to Statistical Learning](https://www.statlearning.com/) - Download Page
+- [Machine Learning For Absolute Beginners](https://www.amazon.in/Machine-Learning-Absolute-Beginners-Introduction-ebook/dp/B07335JNW1)
+
+#### Book Deals (Affiliated) 🛍
+
+- [eBook sale - Save up to 45% on eBooks!](https://www.manning.com/?utm_source=mikrobusiness&utm_medium=affiliate&utm_campaign=ebook_sale_8_8_22)
+
+- [Causal Machine Learning](https://www.manning.com/books/causal-machine-learning?utm_source=mikrobusiness&utm_medium=affiliate&utm_campaign=book_ness_causal_7_26_22&a_aid=mikrobusiness&a_bid=43a2198b
+)
+- [Managing ML Projects](https://www.manning.com/books/managing-machine-learning-projects?utm_source=mikrobusiness&utm_medium=affiliate&utm_campaign=book_thompson_managing_6_14_22)
+- [Causal Inference for Data Science](https://www.manning.com/books/causal-inference-for-data-science?utm_source=mikrobusiness&utm_medium=affiliate&utm_campaign=book_ruizdevilla_causal_6_6_22)
+- [Data for All](https://www.manning.com/books/data-for-all?utm_source=mikrobusiness&utm_medium=affiliate)
+
+### Journals, Publications and Magazines
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [ICML](https://icml.cc/2015/) - International Conference on Machine Learning
+- [GECCO](https://gecco-2019.sigevo.org/index.html/HomePage) - The Genetic and Evolutionary Computation Conference (GECCO)
+- [epjdatascience](https://epjdatascience.springeropen.com/)
+- [Journal of Data Science](https://jds-online.org/journal/JDS) - an international journal devoted to applications of statistical methods at large
+- [Big Data Research](https://www.journals.elsevier.com/big-data-research)
+- [Journal of Big Data](https://journalofbigdata.springeropen.com/)
+- [Big Data & Society](https://journals.sagepub.com/home/bds)
+- [Data Science Journal](https://www.jstage.jst.go.jp/browse/dsj)
+- [datatau.com/news](https://www.datatau.com/news) - Like Hacker News, but for data
+- [Data Science Trello Board](https://trello.com/b/rbpEfMld/data-science)
+- [Medium Data Science Topic](https://medium.com/tag/data-science) - Data Science related publications on medium
+- [Towards Data Science Genetic Algorithm Topic](https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3#:~:text=A%20genetic%20algorithm%20is%20a,offspring%20of%20the%20next%20generation.) -Genetic Algorithm related Publications towards Data Science
+- [all AI news](https://allainews.com/) - The AI/ML/Big Data news aggregator platform
+
+### Newsletters
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [AI Digest](https://aidigest.net/). A weekly newsletter to keep up to date with AI, machine learning, and data science. [Archive](https://aidigest.net/digests).
+- [DataTalks.Club](https://datatalks.club). A weekly newsletter about data-related things. [Archive](https://us19.campaign-archive.com/home/?u=0d7822ab98152f5afc118c176&id=97178021aa).
+- [The Analytics Engineering Roundup](https://roundup.getdbt.com/about). A newsletter about data science. [Archive](https://roundup.getdbt.com/archive).
+
+### Bloggers
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [Wes McKinney](https://wesmckinney.com/archives.html) - Wes McKinney Archives.
+- [Matthew Russell](https://miningthesocialweb.com/) - Mining The Social Web.
+- [Greg Reda](https://www.gregreda.com/) - Greg Reda Personal Blog
+- [Kevin Davenport](https://kldavenport.com/) - Kevin Davenport Personal Blog
+- [Julia Evans](https://jvns.ca/) - Recurse Center alumna
+- [Hakan Kardas](https://www.cse.unr.edu/~hkardes/) - Personal Web Page
+- [Sean J. Taylor](https://seanjtaylor.com/) - Personal Web Page
+- [Drew Conway](https://drewconway.com/) - Personal Web Page
+- [Hilary Mason](https://hilarymason.com/) - Personal Web Page
+- [Noah Iliinsky](https://complexdiagrams.com/) - Personal Blog
+- [Matt Harrison](https://hairysun.com/) - Personal Blog
+- [Vamshi Ambati](https://allthingsds.wordpress.com/) - AllThings Data Sciene
+- [Prash Chan](https://www.mdmgeek.com/) - Tech Blog on Master Data Management And Every Buzz Surrounding It
+- [Clare Corthell](https://datasciencemasters.org/) - The Open Source Data Science Masters
+- [Paul Miller](https://cloudofdata.com/) Based in the UK and working globally, Cloud of Data's consultancy services help clients understand the implications of taking data and more to the Cloud.
+- [Data Science London](https://datasciencelondon.org/) Data Science London is a non-profit organization dedicated to the free, open, dissemination of data science.
+ We are the largest data science community in Europe.
+ We are more than 3,190 data scientists and data geeks in our community.
+- [Datawrangling](http://www.datawrangling.org) by Peter Skomoroch. MACHINE LEARNING, DATA MINING, AND MORE
+- [Quora Data Science](https://www.quora.com/topic/Data-Science) - Data Science Questions and Answers from experts
+- [Siah](https://openresearch.wordpress.com/) a PhD student at Berkeley
+- [Louis Dorard](https://www.ownml.co/blog/) a technology guy with a penchant for the web and for data, big and small
+- [Machine Learning Mastery](https://machinelearningmastery.com/) about helping professional programmers confidently apply machine learning algorithms to address complex problems.
+- [Daniel Forsyth](https://www.danielforsyth.me/) - Personal Blog
+- [Data Science Weekly](https://www.datascienceweekly.org/) - Weekly News Blog
+- [Revolution Analytics](https://blog.revolutionanalytics.com/) - Data Science Blog
+- [R Bloggers](https://www.r-bloggers.com/) - R Bloggers
+- [The Practical Quant](https://practicalquant.blogspot.com/) Big data
+- [Yet Another Data Blog](https://yet-another-data-blog.blogspot.com/) Yet Another Data Blog
+- [Spenczar](https://spenczar.com/) a data scientist at _Twitch_. I handle the whole data pipeline, from tracking to model-building to reporting.
+- [KD Nuggets](https://www.kdnuggets.com/) Data Mining, Analytics, Big Data, Data, Science not a blog a portal
+- [Meta Brown](https://www.metabrown.com/blog/) - Personal Blog
+- [Data Scientist](https://datascientists.net/) is building the data scientist culture.
+- [WhatSTheBigData](https://whatsthebigdata.com/) is some of, all of, or much more than the above and this blog explores its impact on information technology, the business world, government agencies, and our lives.
+- [Tevfik Kosar](https://magnus-notitia.blogspot.com/) - Magnus Notitia
+- [New Data Scientist](https://newdatascientist.blogspot.com/) How a Social Scientist Jumps into the World of Big Data
+- [Harvard Data Science](https://harvarddatascience.com/) - Thoughts on Statistical Computing and Visualization
+- [Data Science 101](https://ryanswanstrom.com/datascience101/) - Learning To Be A Data Scientist
+- [Kaggle Past Solutions](https://www.chioka.in/kaggle-competition-solutions/)
+- [DataScientistJourney](https://datascientistjourney.wordpress.com/category/data-science/)
+- [NYC Taxi Visualization Blog](https://chriswhong.github.io/nyctaxi/)
+- [Learning Lover](https://learninglover.com/blog/)
+- [Dataists](https://www.dataists.com/)
+- [Data-Mania](https://www.data-mania.com/)
+- [Data-Magnum](https://data-magnum.com/)
+- [P-value](https://www.p-value.info/) - Musings on data science, machine learning, and stats.
+- [datascopeanalytics](https://datascopeanalytics.com/blog/)
+- [Digital transformation](https://tarrysingh.com/)
+- [datascientistjourney](https://datascientistjourney.wordpress.com/category/data-science/)
+- [Data Mania Blog](https://www.data-mania.com/blog/) - [The File Drawer](https://chris-said.io/) - Chris Said's science blog
+- [Emilio Ferrara's web page](https://www.emilio.ferrara.name/)
+- [DataNews](https://datanews.tumblr.com/)
+- [Reddit TextMining](https://www.reddit.com/r/textdatamining/)
+- [Periscopic](https://periscopic.com/#!/news)
+- [Hilary Parker](https://hilaryparker.com/)
+- [Data Stories](https://datastori.es/)
+- [Data Science Lab](https://datasciencelab.wordpress.com/)
+- [Meaning of](https://www.kennybastani.com/)
+- [Adventures in Data Land](https://blog.smola.org)
+- [DATA MINERS BLOG](https://blog.data-miners.com/)
+- [Dataclysm](https://theblog.okcupid.com/)
+- [FlowingData](https://flowingdata.com/) - Visualization and Statistics
+- [Calculated Risk](https://www.calculatedriskblog.com/)
+- [O'reilly Learning Blog](https://www.oreilly.com/content/topics/oreilly-learning/)
+- [Dominodatalab](https://blog.dominodatalab.com/)
+- [i am trask](https://iamtrask.github.io/) - A Machine Learning Craftsmanship Blog
+- [Vademecum of Practical Data Science](https://datasciencevademecum.wordpress.com/) - Handbook and recipes for data-driven solutions of real-world problems
+- [Dataconomy](https://dataconomy.com/) - A blog on the newly emerging data economy
+- [Springboard](https://www.springboard.com/blog/) - A blog with resources for data science learners
+- [Analytics Vidhya](https://www.analyticsvidhya.com/) - A full-fledged website about data science and analytics study material.
+- [Occam's Razor](https://www.kaushik.net/avinash/) - Focused on Web Analytics.
+- [Data School](https://www.dataschool.io/) - Data science tutorials for beginners!
+- [Colah's Blog](https://colah.github.io) - Blog for understanding Neural Networks!
+- [Sebastian's Blog](https://ruder.io/#open) - Blog for NLP and transfer learning!
+- [Distill](https://distill.pub) - Dedicated to clear explanations of machine learning!
+- [Chris Albon's Website](https://chrisalbon.com/) - Data Science and AI notes
+- [Andrew Carr](https://andrewnc.github.io/blog/blog.html) - Data Science with Esoteric programming languages
+- [floydhub](https://blog.floydhub.com/introduction-to-genetic-algorithms/) - Blog for Evolutionary Algorithms
+- [Jingles](https://jinglescode.github.io/) - Review and extract key concepts from academic papers
+- [nbshare](https://www.nbshare.io/notebooks/data-science/) - Data Science notebooks
+- [Deep and Shallow](https://deep-and-shallow.com/) - All things Deep and Shallow in Data Science
+- [Loic Tetrel](https://ltetrel.github.io/) - Data science blog
+- [Chip Huyen's Blog](https://huyenchip.com/blog/) - ML Engineering, MLOps, and the use of ML in startups
+- [Maria Khalusova](https://www.mariakhalusova.com/) - Data science blog
+- [Aditi Rastogi](https://medium.com/@aditi2507rastogi) - ML,DL,Data Science blog
+- [Santiago Basulto](https://medium.com/@santiagobasulto) - Data Science with Python
+- [Akhil Soni](https://medium.com/@akhil0435) - ML, DL and Data Science
+- [Akhil Soni](https://akhilworld.hashnode.dev/) - ML, DL and Data Science
+
+### Presentations
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [How to Become a Data Scientist](https://www.slideshare.net/ryanorban/how-to-become-a-data-scientist)
+- [Introduction to Data Science](https://www.slideshare.net/NikoVuokko/introduction-to-data-science-25391618)
+- [Intro to Data Science for Enterprise Big Data](https://www.slideshare.net/pacoid/intro-to-data-science-for-enterprise-big-data)
+- [How to Interview a Data Scientist](https://www.slideshare.net/dtunkelang/how-to-interview-a-data-scientist)
+- [How to Share Data with a Statistician](https://github.com/jtleek/datasharing)
+- [The Science of a Great Career in Data Science](https://www.slideshare.net/katemats/the-science-of-a-great-career-in-data-science)
+- [What Does a Data Scientist Do?](https://www.slideshare.net/datasciencelondon/big-data-sorry-data-science-what-does-a-data-scientist-do)
+- [Building Data Start-Ups: Fast, Big, and Focused](https://www.slideshare.net/medriscoll/driscoll-strata-buildingdatastartups25may2011clean)
+- [How to win data science competitions with Deep Learning](https://www.slideshare.net/0xdata/how-to-win-data-science-competitions-with-deep-learning)
+- [Full-Stack Data Scientist](https://www.slideshare.net/AlexeyGrigorev/fullstack-data-scientist)
+
+### Podcasts
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [AI at Home](https://podcasts.apple.com/us/podcast/data-science-at-home/id1069871378)
+- [AI Today](https://www.cognilytica.com/aitoday/)
+- [Adversarial Learning](https://adversariallearning.com/)
+- [Becoming a Data Scientist](https://www.becomingadatascientist.com/category/podcast/)
+- [Chai time Data Science](https://www.youtube.com/playlist?list=PLLvvXm0q8zUbiNdoIazGzlENMXvZ9bd3x)
+- [Data Crunch](https://datacrunchcorp.com/data-crunch-podcast/)
+- [Data Engineering Podcast](https://www.dataengineeringpodcast.com/)
+- [Data Science at Home](https://datascienceathome.com/)
+- [Data Science Mixer](https://community.alteryx.com/t5/Data-Science-Mixer/bg-p/mixer)
+- [Data Skeptic](https://dataskeptic.com/)
+- [Data Stories](https://datastori.es/)
+- [Datacast](https://jameskle.com/writes/category/Datacast)
+- [DataFramed](https://www.datacamp.com/community/podcast)
+- [DataTalks.Club](https://anchor.fm/datatalksclub)
+- [Gradient Dissent](https://wandb.ai/fully-connected/gradient-dissent)
+- [Learning Machines 101](https://www.learningmachines101.com/)
+- [Let's Data (Brazil)](https://www.youtube.com/playlist?list=PLn_z5E4dh_Lj5eogejMxfOiNX3nOhmhmM)
+- [Linear Digressions](https://lineardigressions.com/)
+- [Not So Standard Deviations](https://nssdeviations.com/)
+- [O'Reilly Data Show Podcast](https://www.oreilly.com/radar/topics/oreilly-data-show-podcast/)
+- [Partially Derivative](https://partiallyderivative.com/)
+- [Superdatascience](https://www.superdatascience.com/podcast/)
+- [The Data Engineering Show](https://www.dataengineeringshow.com/)
+- [The Radical AI Podcast](https://www.radicalai.org/)
+- [The Robot Brains Podcast](https://www.therobotbrains.ai/)
+- [What's The Point](https://fivethirtyeight.com/tag/whats-the-point/)
+- [How AI Built This](https://how-ai-built-this.captivate.fm/)
+
+### YouTube Videos & Channels
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [What is machine learning?](https://www.youtube.com/watch?v=WXHM_i-fgGo)
+- [Andrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24)
+- [Data36 - Data Science for Beginners by Tomi Mester](https://www.youtube.com/c/TomiMesterData36comDataScienceForBeginners)
+- [Deep Learning: Intelligence from Big Data](https://www.youtube.com/watch?v=czLI3oLDe8M)
+- [Interview with Google's AI and Deep Learning 'Godfather' Geoffrey Hinton](https://www.youtube.com/watch?v=1Wp3IIpssEc)
+- [Introduction to Deep Learning with Python](https://www.youtube.com/watch?v=S75EdAcXHKk)
+- [What is machine learning, and how does it work?](https://www.youtube.com/watch?v=elojMnjn4kk)
+- [Data School](https://www.youtube.com/channel/UCnVzApLJE2ljPZSeQylSEyg) - Data Science Education
+- [Neural Nets for Newbies by Melanie Warrick (May 2015)](https://www.youtube.com/watch?v=Cu6A96TUy_o)
+- [Neural Networks video series by Hugo Larochelle](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)
+- [Google DeepMind co-founder Shane Legg - Machine Super Intelligence](https://www.youtube.com/watch?v=evNCyRL3DOU)
+- [Data Science Primer](https://www.youtube.com/watch?v=cHzvYxBN9Ls&list=PLPqVjP3T4RIRsjaW07zoGzH-Z4dBACpxY)
+- [Data Science with Genetic Algorithms](https://www.youtube.com/watch?v=lpD38NxTOnk)
+- [Data Science for Beginners](https://www.youtube.com/playlist?list=PL2zq7klxX5ATMsmyRazei7ZXkP1GHt-vs)
+- [DataTalks.Club](https://www.youtube.com/channel/UCDvErgK0j5ur3aLgn6U-LqQ)
+- [Mildlyoverfitted - Tutorials on intermediate ML/DL topics](https://www.youtube.com/channel/UCYBSjwkGTK06NnDnFsOcR7g)
+- [mlops.community - Interviews of industry experts about production ML](https://www.youtube.com/channel/UCYBSjwkGTK06NnDnFsOcR7g)
+- [ML Street Talk - Unabashedly technical and non-commercial, so you will hear no annoying pitches.](https://www.youtube.com/c/machinelearningstreettalk)
+- [Neural networks by 3Blue1Brown ](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
+- [Neural networks from scratch by Sentdex](https://www.youtube.com/playlist?list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3)
+- [Manning Publications YouTube channel](https://www.youtube.com/c/ManningPublications/featured)
+- [Ask Dr Chong: How to Lead in Data Science - Part 1](https://youtu.be/JYuQZii5o58)
+- [Ask Dr Chong: How to Lead in Data Science - Part 2](https://youtu.be/SzqIXV-O-ko)
+- [Ask Dr Chong: How to Lead in Data Science - Part 3](https://youtu.be/Ogwm7k_smTA)
+- [Ask Dr Chong: How to Lead in Data Science - Part 4](https://youtu.be/a9usjdzTxTU)
+- [Ask Dr Chong: How to Lead in Data Science - Part 5](https://youtu.be/MYdQq-F3Ws0)
+- [Ask Dr Chong: How to Lead in Data Science - Part 6](https://youtu.be/LOOt4OVC3hY)
+- [Regression Models: Applying simple Poisson regression](https://www.youtube.com/watch?v=9Hk8K8jhiOo)
+- [Deep Learning Architectures](https://www.youtube.com/playlist?list=PLv8Cp2NvcY8DpVcsmOT71kymgMmcr59Mf)
+- [Time Series Modelling and Analysis](https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK)
+
+## Socialize
+**[`^ back to top ^`](#awesome-data-science)**
+
+Below are some Social Media links. Connect with other data scientists!
+
+- [Facebook Accounts](#facebook-accounts)
+- [Twitter Accounts](#twitter-accounts)
+- [Telegram Channels](#telegram-channels)
+- [Slack Communities](#slack-communities)
+- [GitHub Groups](#github-groups)
+- [Data Science Competitions](#data-science-competitions)
+
+
+### Facebook Accounts
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [Data](https://www.facebook.com/data)
+- [Big Data Scientist](https://www.facebook.com/Bigdatascientist)
+- [Data Science Day](https://www.facebook.com/datascienceday/)
+- [Data Science Academy](https://www.facebook.com/nycdatascience)
+- [Facebook Data Science Page](https://www.facebook.com/pages/Data-science/431299473579193?ref=br_rs)
+- [Data Science London](https://www.facebook.com/pages/Data-Science-London/226174337471513)
+- [Data Science Technology and Corporation](https://www.facebook.com/DataScienceTechnologyCorporation?ref=br_rs)
+- [Data Science - Closed Group](https://www.facebook.com/groups/1394010454157077/?ref=br_rs)
+- [Center for Data Science](https://www.facebook.com/centerdatasciences?ref=br_rs)
+- [Big data hadoop NOSQL Hive Hbase](https://www.facebook.com/groups/bigdatahadoop/)
+- [Analytics, Data Mining, Predictive Modeling, Artificial Intelligence](https://www.facebook.com/groups/data.analytics/)
+- [Big Data Analytics using R](https://www.facebook.com/groups/434352233255448/)
+- [Big Data Analytics with R and Hadoop](https://www.facebook.com/groups/rhadoop/)
+- [Big Data Learnings](https://www.facebook.com/groups/bigdatalearnings/)
+- [Big Data, Data Science, Data Mining & Statistics](https://www.facebook.com/groups/bigdatastatistics/)
+- [BigData/Hadoop Expert](https://www.facebook.com/groups/BigDataExpert/)
+- [Data Mining / Machine Learning / AI](https://www.facebook.com/groups/machinelearningforum/)
+- [Data Mining/Big Data - Social Network Ana](https://www.facebook.com/groups/dataminingsocialnetworks/)
+- [Vademecum of Practical Data Science](https://www.facebook.com/datasciencevademecum)
+- [Veri Bilimi Istanbul](https://www.facebook.com/groups/veribilimiistanbul/)
+- [The Data Science Blog](https://www.facebook.com/theDataScienceBlog/)
+
+
+### Twitter Accounts
+**[`^ back to top ^`](#awesome-data-science)**
+
+| Twitter | Description |
+| --- | --- |
+| [Big Data Combine](https://twitter.com/BigDataCombine) | Rapid-fire, live tryouts for data scientists seeking to monetize their models as trading strategies |
+| Big Data Mania | Data Viz Wiz, Data Journalist, Growth Hacker, Author of Data Science for Dummies (2015) |
+| [Big Data Science](https://twitter.com/analyticbridge) | Big Data, Data Science, Predictive Modeling, Business Analytics, Hadoop, Decision and Operations Research. |
+| Charlie Greenbacker | Director of Data Science at @ExploreAltamira |
+| [Chris Said](https://twitter.com/Chris_Said) | Data scientist at Twitter |
+| [Clare Corthell](https://twitter.com/clarecorthell) | Dev, Design, Data Science @mattermark #hackerei |
+| [DADI Charles-Abner](https://twitter.com/DadiCharles) | #datascientist @Ekimetrics. , #machinelearning #dataviz #DynamicCharts #Hadoop #R #Python #NLP #Bitcoin #dataenthousiast |
+| [Data Science Central](https://twitter.com/DataScienceCtrl) | Data Science Central is the industry's single resource for Big Data practitioners. |
+| [Data Science London](https://twitter.com/ds_ldn) | Data Science. Big Data. Data Hacks. Data Junkies. Data Startups. Open Data |
+| [Data Science Renee](https://twitter.com/BecomingDataSci) | Documenting my path from SQL Data Analyst pursuing an Engineering Master's Degree to Data Scientist |
+| [Data Science Report](https://twitter.com/TedOBrien93) | Mission is to help guide & advance careers in Data Science & Analytics |
+| [Data Science Tips](https://twitter.com/datasciencetips) | Tips and Tricks for Data Scientists around the world! #datascience #bigdata |
+| [Data Vizzard](https://twitter.com/DataVisualizati) | DataViz, Security, Military |
+| [DataScienceX](https://twitter.com/DataScienceX) | |
+| deeplearning4j | |
+| [DJ Patil](https://twitter.com/dpatil) | White House Data Chief, VP @ RelateIQ. |
+| [Domino Data Lab](https://twitter.com/DominoDataLab) | |
+| [Drew Conway](https://twitter.com/drewconway) | Data nerd, hacker, student of conflict. |
+| Emilio Ferrara | #Networks, #MachineLearning and #DataScience. I work on #Social Media. Postdoc at @IndianaUniv |
+| [Erin Bartolo](https://twitter.com/erinbartolo) | Running with #BigData--enjoying a love/hate relationship with its hype. @iSchoolSU #DataScience Program Mgr. |
+| [Greg Reda](https://twitter.com/gjreda) | Working @ _GrubHub_ about data and pandas |
+| [Gregory Piatetsky](https://twitter.com/kdnuggets) | KDnuggets President, Analytics/Big Data/Data Mining/Data Science expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, part-time philosopher. |
+| [Hadley Wickham](https://twitter.com/hadleywickham) | Chief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University. |
+| [Hakan Kardas](https://twitter.com/hakan_kardes) | Data Scientist |
+| [Hilary Mason](https://twitter.com/hmason) | Data Scientist in Residence at @accel. |
+| [Jeff Hammerbacher](https://twitter.com/hackingdata) | ReTweeting about data science |
+| [John Myles White](https://twitter.com/johnmyleswhite) | Scientist at Facebook and Julia developer. Author of Machine Learning for Hackers and Bandit Algorithms for Website Optimization. Tweets reflect my views only. |
+| [Juan Miguel Lavista](https://twitter.com/BDataScientist) | Principal Data Scientist @ Microsoft Data Science Team |
+| [Julia Evans](https://twitter.com/b0rk) | Hacker - Pandas - Data Analyze |
+| [Kenneth Cukier](https://twitter.com/kncukier) | The Economist's Data Editor and co-author of Big Data (http://www.big-data-book.com/). |
+| Kevin Davenport | Organizer of https://www.meetup.com/San-Diego-Data-Science-R-Users-Group/ |
+| [Kevin Markham](https://twitter.com/justmarkham) | Data science instructor, and founder of [Data School](https://www.dataschool.io/) |
+| [Kim Rees](https://twitter.com/krees) | Interactive data visualization and tools. Data flaneur. |
+| [Kirk Borne](https://twitter.com/KirkDBorne) | DataScientist, PhD Astrophysicist, Top #BigData Influencer. |
+| Linda Regber | Data storyteller, visualizations. |
+| [Luis Rei](https://twitter.com/lmrei) | PhD Student. Programming, Mobile, Web. Artificial Intelligence, Intelligent Robotics Machine Learning, Data Mining, Natural Language Processing, Data Science. |
+| Mark Stevenson | Data Analytics Recruitment Specialist at Salt (@SaltJobs) Analytics - Insight - Big Data - Data science |
+| [Matt Harrison](https://twitter.com/__mharrison__) | Opinions of full-stack Python guy, author, instructor, currently playing Data Scientist. Occasional fathering, husbanding, organic gardening. |
+| [Matthew Russell](https://twitter.com/ptwobrussell) | Mining the Social Web. |
+| [Mert Nuhoğlu](https://twitter.com/mertnuhoglu) | Data Scientist at BizQualify, Developer |
+| [Monica Rogati](https://twitter.com/mrogati) | Data @ Jawbone. Turned data into stories & products at LinkedIn. Text mining, applied machine learning, recommender systems. Ex-gamer, ex-machine coder; namer. |
+| [Noah Iliinsky](https://twitter.com/noahi) | Visualization & interaction designer. Practical cyclist. Author of vis books: https://www.oreilly.com/pub/au/4419 |
+| [Paul Miller](https://twitter.com/PaulMiller) | Cloud Computing/ Big Data/ Open Data Analyst & Consultant. Writer, Speaker & Moderator. Gigaom Research Analyst. |
+| [Peter Skomoroch](https://twitter.com/peteskomoroch) | Creating intelligent systems to automate tasks & improve decisions. Entrepreneur, ex-Principal Data Scientist @LinkedIn. Machine Learning, ProductRei, Networks |
+| [Prash Chan](https://twitter.com/MDMGeek) | Solution Architect @ IBM, Master Data Management, Data Quality & Data Governance Blogger. Data Science, Hadoop, Big Data & Cloud. |
+| [Quora Data Science](https://twitter.com/q_datascience) | Quora's data science topic |
+| [R-Bloggers](https://twitter.com/Rbloggers) | Tweet blog posts from the R blogosphere, data science conferences, and (!) open jobs for data scientists. |
+| [Rand Hindi](https://twitter.com/randhindi) | |
+| [Randy Olson](https://twitter.com/randal_olson) | Computer scientist researching artificial intelligence. Data tinkerer. Community leader for @DataIsBeautiful. #OpenScience advocate. |
+| [Recep Erol](https://twitter.com/EROLRecep) | Data Science geek @ UALR |
+| [Ryan Orban](https://twitter.com/ryanorban) | Data scientist, genetic origamist, hardware aficionado |
+| [Sean J. Taylor](https://twitter.com/seanjtaylor) | Social Scientist. Hacker. Facebook Data Science Team. Keywords: Experiments, Causal Inference, Statistics, Machine Learning, Economics. |
+| [Silvia K. Spiva](https://twitter.com/silviakspiva) | #DataScience at Cisco |
+| [Harsh B. Gupta](https://twitter.com/harshbg) | Data Scientist at BBVA Compass |
+| [Spencer Nelson](https://twitter.com/spenczar_n) | Data nerd |
+| [Talha Oz](https://twitter.com/tozCSS) | Enjoys ABM, SNA, DM, ML, NLP, HI, Python, Java. Top percentile Kaggler/data scientist |
+| [Tasos Skarlatidis](https://twitter.com/anskarl) | Complex Event Processing, Big Data, Artificial Intelligence and Machine Learning. Passionate about programming and open-source. |
+| [Terry Timko](https://twitter.com/Terry_Timko) | InfoGov; Bigdata; Data as a Service; Data Science; Open, Social & Business Data Convergence |
+| [Tony Baer](https://twitter.com/TonyBaer) | IT analyst with Ovum covering Big Data & data management with some systems engineering thrown in. |
+| [Tony Ojeda](https://twitter.com/tonyojeda3) | Data Scientist , Author , Entrepreneur. Co-founder @DataCommunityDC. Founder @DistrictDataLab. #DataScience #BigData #DataDC |
+| [Vamshi Ambati](https://twitter.com/vambati) | Data Science @ PayPal. #NLP, #machinelearning; PhD, Carnegie Mellon alumni (Blog: https://allthingsds.wordpress.com ) |
+| [Wes McKinney](https://twitter.com/wesmckinn) | Pandas (Python Data Analysis library). |
+| [WileyEd](https://twitter.com/WileyEd) | Senior Manager - @Seagate Big Data Analytics @McKinsey Alum #BigData + #Analytics Evangelist #Hadoop, #Cloud, #Digital, & #R Enthusiast |
+| [WNYC Data News Team](https://twitter.com/datanews) | The data news crew at @WNYC. Practicing data-driven journalism, making it visual, and showing our work. |
+| [Alexey Grigorev](https://twitter.com/Al_Grigor) | Data science author |
+| [İlker Arslan](https://twitter.com/ilkerarslan_35) | Data science author. Shares mostly about Julia programming |
+| [INEVITABLE](https://twitter.com/WeAreInevitable) | AI & Data Science Start-up Company based in England, UK |
+
+### Telegram Channels
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [Open Data Science](https://t.me/opendatascience) – First Telegram Data Science channel. Covering all technical and popular staff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former.
+- [Loss function porn](https://t.me/loss_function_porn) — Beautiful posts on DS/ML theme with video or graphic visualization.
+- [Machinelearning](https://t.me/ai_machinelearning_big_data) – Daily ML news.
+
+
+### Slack Communities
+[top](#awesome-data-science)
+
+- [DataTalks.Club](https://datatalks.club)
+- [Women Who Code - Data Science](https://www.womenwhocode.com/datascience)
+
+### GitHub Groups
+- [Berkeley Institute for Data Science](https://github.com/BIDS)
+
+### Data Science Competitions
+
+Some data mining competition platforms
+
+- [Kaggle](https://www.kaggle.com/)
+- [DrivenData](https://www.drivendata.org/)
+- [Analytics Vidhya](https://datahack.analyticsvidhya.com/)
+- [InnoCentive](https://www.innocentive.com/)
+- [Microprediction](https://www.microprediction.com/python-1)
+
+## Fun
+
+- [Infographic](#infographics)
+- [Datasets](#datasets)
+- [Comics](#comics)
+
+
+### Infographics
+**[`^ back to top ^`](#awesome-data-science)**
+
+| Preview | Description |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| [](https://i.imgur.com/0OoLaa5.png) | [Key differences of a data scientist vs. data engineer](https://searchbusinessanalytics.techtarget.com/feature/Key-differences-of-a-data-scientist-vs-data-engineer) |
+| [](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/DataScienceEightSteps_Full.png) | A visual guide to Becoming a Data Scientist in 8 Steps by [DataCamp](https://www.datacamp.com) [(img)](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/DataScienceEightSteps_Full.png) |
+| [](https://i.imgur.com/FxsL3b8.png) | Mindmap on required skills ([img](https://i.imgur.com/FxsL3b8.png)) |
+| [](https://nirvacana.com/thoughts/wp-content/uploads/2013/07/RoadToDataScientist1.png) | Swami Chandrasekaran made a [Curriculum via Metro map](http://nirvacana.com/thoughts/2013/07/08/becoming-a-data-scientist/). |
+| [](https://i.imgur.com/4ZBBvb0.png) | by [@kzawadz](https://twitter.com/kzawadz) via [twitter](https://twitter.com/MktngDistillery/status/538671811991715840) |
+| [](https://i.imgur.com/xLY3XZn.jpg) | By [Data Science Central](https://www.datasciencecentral.com/) |
+| [](https://i.imgur.com/0TydZ4M.png) | Data Science Wars: R vs Python |
+| [](https://i.imgur.com/HnRwlce.png) | How to select statistical or machine learning techniques |
+| [](https://scikit-learn.org/stable/_static/ml_map.png) | Choosing the Right Estimator |
+| [](https://i.imgur.com/uEqMwZa.png) | The Data Science Industry: Who Does What |
+| [](https://i.imgur.com/RsHqY84.png) | Data Science ~~Venn~~ Euler Diagram |
+| [](https://www.springboard.com/blog/wp-content/uploads/2016/03/20160324_springboard_vennDiagram.png) | Different Data Science Skills and Roles from [this article](https://www.springboard.com/blog/data-science-career-paths-different-roles-industry/) by Springboard |
+| [](https://data-literacy.geckoboard.com/poster/) | A simple and friendly way of teaching your non-data scientist/non-statistician colleagues [how to avoid mistakes with data](https://data-literacy.geckoboard.com/poster/). From Geckoboard's [Data Literacy Lessons](https://data-literacy.geckoboard.com/). |
+
+### Datasets
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [Academic Torrents](https://academictorrents.com/)
+- [ADS-B Exchange](https://www.adsbexchange.com/data-samples/) - Specific datasets for aircraft and Automatic Dependent Surveillance-Broadcast (ADS-B) sources.
+- [hadoopilluminated.com](https://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html)
+- [data.gov](https://catalog.data.gov/dataset) - The home of the U.S. Government's open data
+- [United States Census Bureau](https://www.census.gov/)
+- [usgovxml.com](https://usgovxml.com/)
+- [enigma.com](https://enigma.com/) - Navigate the world of public data - Quickly search and analyze billions of public records published by governments, companies and organizations.
+- [datahub.io](https://datahub.io/)
+- [aws.amazon.com/datasets](https://aws.amazon.com/datasets/)
+- [datacite.org](https://datacite.org/)
+- [The official portal for European data](https://data.europa.eu/en)
+- [NASDAQ:DATA](https://data.nasdaq.com/) - Nasdaq Data Link A premier source for financial, economic and alternative datasets.
+- [figshare.com](https://figshare.com/)
+- [GeoLite Legacy Downloadable Databases](https://dev.maxmind.com/geoip)
+- [Quora's Big Datasets Answer](https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public)
+- [Public Big Data Sets](https://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html)
+- [Kaggle Datasets](https://www.kaggle.com/datasets)
+- [A Deep Catalog of Human Genetic Variation](https://www.internationalgenome.org/data)
+- [A community-curated database of well-known people, places, and things](https://developers.google.com/freebase/)
+- [Google Public Data](https://www.google.com/publicdata/directory)
+- [World Bank Data](https://data.worldbank.org/)
+- [NYC Taxi data](https://chriswhong.github.io/nyctaxi/)
+- [Open Data Philly](https://www.opendataphilly.org/) Connecting people with data for Philadelphia
+- [grouplens.org](https://grouplens.org/datasets/) Sample movie (with ratings), book and wiki datasets
+- [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/) - contains data sets good for machine learning
+- [research-quality data sets](https://web.archive.org/web/20150320022752/https://bitly.com/bundles/hmason/1) by [Hilary Mason](https://web.archive.org/web/20150501033715/https://bitly.com/u/hmason/bundles)
+- [National Centers for Environmental Information](https://www.ncei.noaa.gov/)
+- [ClimateData.us](https://www.climatedata.us/) (related: [U.S. Climate Resilience Toolkit](https://toolkit.climate.gov/))
+- [r/datasets](https://www.reddit.com/r/datasets/)
+- [MapLight](https://www.maplight.org/data-series) - provides a variety of data free of charge for uses that are freely available to the general public. Click on a data set below to learn more
+- [GHDx](https://ghdx.healthdata.org/) - Institute for Health Metrics and Evaluation - a catalog of health and demographic datasets from around the world and including IHME results
+- [St. Louis Federal Reserve Economic Data - FRED](https://fred.stlouisfed.org/)
+- [New Zealand Institute of Economic Research – Data1850](https://data1850.nz/)
+- [Open Data Sources](https://github.com/datasciencemasters/data)
+- [UNICEF Data](https://data.unicef.org/)
+- [undata](https://data.un.org/)
+- [NASA SocioEconomic Data and Applications Center - SEDAC](https://sedac.ciesin.columbia.edu/)
+- [The GDELT Project](https://www.gdeltproject.org/)
+- [Sweden, Statistics](https://www.scb.se/en/)
+- [StackExchange Data Explorer](https://data.stackexchange.com) - an open source tool for running arbitrary queries against public data from the Stack Exchange network.
+- [SocialGrep](https://socialgrep.com/datasets) - a collection of open Reddit datasets.
+- [San Fransisco Government Open Data](https://datasf.org/opendata/)
+- [IBM Asset Dataset](https://developer.ibm.com/exchanges/data/)
+- [Open data Index](https://index.okfn.org/)
+- [Public Git Archive](https://github.com/src-d/datasets/tree/master/PublicGitArchive)
+- [GHTorrent](https://ghtorrent.org/)
+- [Microsoft Research Open Data](https://msropendata.com/)
+- [Open Government Data Platform India](https://data.gov.in/)
+- [Google Dataset Search (beta)](https://datasetsearch.research.google.com/)
+- [NAYN.CO Turkish News with categories](https://github.com/naynco/nayn.data)
+- [Covid-19](https://github.com/datasets/covid-19)
+- [Covid-19 Google](https://github.com/google-research/open-covid-19-data)
+- [Enron Email Dataset](https://www.cs.cmu.edu/~./enron/)
+- [5000 Images of Clothes](https://github.com/alexeygrigorev/clothing-dataset)
+- [IBB Open Portal](https://data.ibb.gov.tr/en/)
+- [The Humanitarian Data Exchange](https://data.humdata.org/)
+
+### Comics
+**[`^ back to top ^`](#awesome-data-science)**
+
+- [Comic compilation](https://medium.com/@nikhil_garg/a-compilation-of-comics-explaining-statistics-data-science-and-machine-learning-eeefbae91277)
+- [Cartoons](https://www.kdnuggets.com/websites/cartoons.html)
+
+## Other Awesome Lists
+
+- Other amazingly awesome lists can be found in the [awesome-awesomeness](https://github.com/bayandin/awesome-awesomeness)
+- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)
+- [lists](https://github.com/jnv/lists)
+- [awesome-dataviz](https://github.com/javierluraschi/awesome-dataviz)
+- [awesome-python](https://github.com/vinta/awesome-python)
+- [Data Science IPython Notebooks.](https://github.com/donnemartin/data-science-ipython-notebooks)
+- [awesome-r](https://github.com/qinwf/awesome-R)
+- [awesome-datasets](https://github.com/awesomedata/awesome-public-datasets)
+- [awesome-Machine Learning & Deep Learning Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md)
+- [Awesome Data Science Ideas](https://github.com/JosPolfliet/awesome-ai-usecases)
+- [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)
+- [Community Curated Data Science Resources](https://hackr.io/tutorials/learn-data-science)
+- [Awesome Machine Learning On Source Code](https://github.com/src-d/awesome-machine-learning-on-source-code)
+- [Awesome Community Detection](https://github.com/benedekrozemberczki/awesome-community-detection)
+- [Awesome Graph Classification](https://github.com/benedekrozemberczki/awesome-graph-classification)
+- [Awesome Decision Tree Papers](https://github.com/benedekrozemberczki/awesome-decision-tree-papers)
+- [Awesome Fraud Detection Papers](https://github.com/benedekrozemberczki/awesome-fraud-detection-papers)
+- [Awesome Gradient Boosting Papers](https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers)
+- [Awesome Computer Vision Models](https://github.com/nerox8664/awesome-computer-vision-models)
+- [Awesome Monte Carlo Tree Search](https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers)
+- [Glossary of common statistics and ML terms](https://www.analyticsvidhya.com/glossary-of-common-statistics-and-machine-learning-terms/)
+- [100 NLP Papers](https://github.com/mhagiwara/100-nlp-papers)
+- [Awesome Game Datasets](https://github.com/leomaurodesenv/game-datasets#readme)
+- [Data Science Interviews Questions](https://github.com/alexeygrigorev/data-science-interviews)
+- [Awesome Explainable Graph Reasoning](https://github.com/AstraZeneca/awesome-explainable-graph-reasoning)
+- [Top Data Science Interview Questions](https://www.interviewbit.com/data-science-interview-questions/)
+- [Awesome Drug Synergy, Interaction and Polypharmacy Prediction](https://github.com/AstraZeneca/awesome-drug-pair-scoring)
+- [Deep Learning Interview Questions](https://www.adaface.com/blog/deep-learning-interview-questions/)
+- [Top Future Trends in Data Science in 2023](https://medium.com/the-modern-scientist/top-future-trends-in-data-science-in-2023-3e616c8998b8)
+- [How Generative AI Is Changing Creative Work](https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work)
+- [What is generative AI?](https://www.techtarget.com/searchenterpriseai/definition/generative-AI)
+
+### Hobby
+- [Awesome Music Production](https://github.com/ad-si/awesome-music-production)
+
+
+
+
diff --git a/readmes/index.md b/readmes/index.md
index 5a21d56..5b1341f 100644
--- a/readmes/index.md
+++ b/readmes/index.md
@@ -1,5 +1,5 @@
-
+
```
+====================================================================================================+
| ___ ___ | |
@@ -21,7 +21,7 @@
| |
+ ------------------------------------------ THANKS ------------------------------------------------ +
| |
-| List of awesome pages collected from awesome-awesome-awesome <1> awesome page. |
+| List of awesome pages collected from awesome-awesome-awesome ^1 awesome page. |
| |
| Big shoutout to @t3chnoboy and @sindresorhus for their meta meta (meta) awesome pages! |
| Also to @bradoyler, @emirjp, @erichs, @oyvindrobertsen, @bayandin, @jnv and @scooperma for their |
@@ -29,12 +29,12 @@
| |
| And of course to all the people curating such awesome link lists! You are awesome :) |
| |
-| Highly inspired by cheat.sh <2>. Give it a try. It's awesome too! |
+| Highly inspired by cheat.sh ^2. Give it a try. It's awesome too! |
| |
+ ------------------------------------------ LINKS ------------------------------------------------- +
| |
-| <1> https://github.com/t3chnoboy/awesome-awesome-awesome |
-| <2> https://cheat.sh |
+| ^1 https://github.com/t3chnoboy/awesome-awesome-awesome |
+| ^2 https://cheat.sh |
| |
+====================================================================================================+
```
diff --git a/terminal/datascience b/terminal/datascience
index cb187ef..b81a5e1 100644
--- a/terminal/datascience
+++ b/terminal/datascience
@@ -1,1189 +1,10 @@
+[38;5;12mawesome-data-science[39m
+[38;5;12m====================[39m
+[38;5;12mA curated list of amazingly awesome open source data science resources.[39m
-[38;5;12m [39m[38;2;255;187;0m[1m[4mAWESOME DATA SCIENCE[0m
+[38;5;12mData Visualization[39m
-[38;5;14m[1m![0m[38;5;12mAwesome[39m[38;5;14m[1m (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)[0m[38;5;12m (https://github.com/sindresorhus/awesome) [39m
-
-[38;5;14m[1mAn open-source Data Science repository to learn and apply towards solving real world problems.[0m
-
-[38;5;12mThis is a shortcut path to start studying [39m[38;5;14m[1mData Science[0m[38;5;12m. Just follow the steps to answer the questions, "What is Data Science and what should I study to learn Data Science?"[39m
-
-[38;2;255;187;0m[4mSponsors[0m
-
-[38;5;239m│[39m[38;5;12mSponsor[39m[38;5;239m│[39m[38;5;12m [39m[38;5;12mPitch[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m├[39m[38;5;239m───────[39m[38;5;239m┼[39m[38;5;239m───────────────────────────────────────────[39m[38;5;239m┤[39m
-[38;5;239m│[39m[38;5;12m---[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mBe the first to sponsor! [39m[48;5;235m[38;5;249mgithub@academic.io[49m[39m[38;5;239m│[39m
-
-
-
-
-
-[38;2;255;187;0m[4mTable of Contents[0m
-
-[38;5;12m- [39m[38;5;14m[1mWhat is Data Science?[0m[38;5;12m (#what-is-data-science)[39m
-[38;5;12m- [39m[38;5;14m[1mWhere do I Start?[0m[38;5;12m (#where-do-i-start)[39m
-[38;5;12m- [39m[38;5;14m[1mTraining Resources[0m[38;5;12m (#training-resources)[39m
-[38;5;12m - [39m[38;5;14m[1mTutorials[0m[38;5;12m (#tutorials)[39m
-[38;5;12m - [39m[38;5;14m[1mFree Courses[0m[38;5;12m (#free-courses)[39m
-[38;5;12m - [39m[38;5;14m[1mMassively Open Online Courses[0m[38;5;12m (#moocs)[39m
-[38;5;12m - [39m[38;5;14m[1mIntensive Programs[0m[38;5;12m (#intensive-programs)[39m
-[38;5;12m - [39m[38;5;14m[1mColleges[0m[38;5;12m (#colleges)[39m
-[38;5;12m- [39m[38;5;14m[1mThe Data Science Toolbox[0m[38;5;12m (#the-data-science-toolbox)[39m
-[38;5;12m - [39m[38;5;14m[1mAlgorithms[0m[38;5;12m (#algorithms)[39m
-[48;5;235m[38;5;249m- **Supervised Learning** (#supervised-learning)[49m[39m[48;5;235m[38;5;249m [49m[39m
-[48;5;235m[38;5;249m- **Unsupervised Learning** (#unsupervised-learning)[49m[39m[48;5;235m[38;5;249m [49m[39m
-[48;5;235m[38;5;249m- **Semi-Supervised Learning** (#semi-supervised-learning)[49m[39m[48;5;235m[38;5;249m [49m[39m
-[48;5;235m[38;5;249m- **Reinforcement Learning** (#reinforcement-learning)[49m[39m[48;5;235m[38;5;249m [49m[39m
-[48;5;235m[38;5;249m- **Data Mining Algorithms** (#data-mining-algorithms)[49m[39m[48;5;235m[38;5;249m [49m[39m
-[48;5;235m[38;5;249m- **Deep Learning Architectures** (#deep-learning-architectures)[49m[39m
-[38;5;12m - [39m[38;5;14m[1mGeneral Machine Learning Packages[0m[38;5;12m (#general-machine-learning-packages)[39m
-[38;5;12m - [39m[38;5;14m[1mDeep Learning Packages[0m[38;5;12m (#deep-learning-packages)[39m
-[48;5;235m[38;5;249m- **PyTorch Ecosystem** (#pytorch-ecosystem)[49m[39m[48;5;235m[38;5;249m [49m[39m
-[48;5;235m[38;5;249m- **TensorFlow Ecosystem** (#tensorflow-ecosystem)[49m[39m
-[48;5;235m[38;5;249m- **Keras Ecosystem** (#keras-ecosystem)[49m[39m[48;5;235m[38;5;249m [49m[39m
-[38;5;12m - [39m[38;5;14m[1mVisualization Tools[0m[38;5;12m (#visualization-tools)[39m
-[38;5;12m - [39m[38;5;14m[1mMiscellaneous Tools[0m[38;5;12m (#miscellaneous-tools)[39m
-[38;5;12m- [39m[38;5;14m[1mLiterature and Media[0m[38;5;12m (#literature-and-media)[39m
-[38;5;12m - [39m[38;5;14m[1mBooks[0m[38;5;12m (#books)[39m
-[48;5;235m[38;5;249m- **Book Deals (Affiliated)** (#book-deals-affiliated-)[49m[39m
-[38;5;12m - [39m[38;5;14m[1mJournals, Publications, and Magazines[0m[38;5;12m (#journals-publications-and-magazines)[39m
-[38;5;12m - [39m[38;5;14m[1mNewsletters[0m[38;5;12m (#newsletters)[39m
-[38;5;12m - [39m[38;5;14m[1mBloggers[0m[38;5;12m (#bloggers)[39m
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-[38;5;12m- [39m[38;5;14m[1mProgramming with Julia[0m[38;5;12m (https://www.udemy.com/course/programming-with-julia/)[39m
-[38;5;12m- [39m[38;5;14m[1mScaler Data Science & Machine Learning Program[0m[38;5;12m (https://www.scaler.com/data-science-course/)[39m
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-[38;2;255;187;0m[4mIntensive Programs[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;12m- [39m[38;5;14m[1mS2DS[0m[38;5;12m (https://www.s2ds.org/)[39m
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-[38;2;255;187;0m[4mColleges[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;12m- [39m[38;5;14m[1mA list of colleges and universities offering degrees in data science.[0m[38;5;12m (https://github.com/ryanswanstrom/awesome-datascience-colleges)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science Degree @ Berkeley[0m[38;5;12m (https://ischoolonline.berkeley.edu/data-science/)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science Degree @ UVA[0m[38;5;12m (https://datascience.virginia.edu/)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science Degree @ Wisconsin[0m[38;5;12m (https://datasciencedegree.wisconsin.edu/)[39m
-[38;5;12m- [39m[38;5;14m[1mBS in Data Science & Applications[0m[38;5;12m (https://study.iitm.ac.in/ds/)[39m
-[38;5;12m- [39m[38;5;14m[1mMS in Computer Information Systems @ Boston University[0m[38;5;12m (https://www.bu.edu/online/programs/graduate-programs/computer-information-systems-masters-degree/)[39m
-[38;5;12m- [39m[38;5;14m[1mMS in Business Analytics @ ASU Online[0m[38;5;12m (https://asuonline.asu.edu/online-degree-programs/graduate/master-science-business-analytics/)[39m
-[38;5;12m- [39m[38;5;14m[1mMS in Applied Data Science @ Syracuse[0m[38;5;12m (https://ischool.syr.edu/academics/applied-data-science-masters-degree/)[39m
-[38;5;12m- [39m[38;5;14m[1mM.S. Management & Data Science @ Leuphana[0m[38;5;12m (https://www.leuphana.de/en/graduate-school/masters-programmes/management-data-science.html)[39m
-[38;5;12m- [39m[38;5;14m[1mMaster of Data Science @ Melbourne University[0m[38;5;12m (https://study.unimelb.edu.au/find/courses/graduate/master-of-data-science/#overview)[39m
-[38;5;12m- [39m[38;5;14m[1mMsc in Data Science @ The University of Edinburgh[0m[38;5;12m (https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=902)[39m
-[38;5;12m- [39m[38;5;14m[1mMaster of Management Analytics @ Queen's University[0m[38;5;12m (https://smith.queensu.ca/grad_studies/mma/index.php)[39m
-[38;5;12m- [39m[38;5;14m[1mMaster of Data Science @ Illinois Institute of Technology[0m[38;5;12m (https://www.iit.edu/academics/programs/data-science-mas)[39m
-[38;5;12m- [39m[38;5;14m[1mMaster of Applied Data Science @ The University of Michigan[0m[38;5;12m (https://www.si.umich.edu/programs/master-applied-data-science-online)[39m
-[38;5;12m- [39m[38;5;14m[1mMaster Data Science and Artificial Intelligence @ Eindhoven University of Technology[0m[38;5;12m (https://www.tue.nl/en/education/graduate-school/master-data-science-and-artificial-intelligence/)[39m
-[38;5;12m- [39m[38;5;14m[1mMaster's Degree in Data Science and Computer Engineering @ University of Granada[0m[38;5;12m (https://masteres.ugr.es/datcom/)[39m
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-[38;2;255;187;0m[4mThe Data Science Toolbox[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;12mThis section is a collection of packages, tools, algorithms, and other useful items in the data science world.[39m
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-[38;2;255;187;0m[4mAlgorithms[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;12mThese are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it.[39m
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-[38;2;255;187;0m[4mThree kinds of Machine Learning Systems[0m
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-[38;5;12m- Based on training with human supervision[39m
-[38;5;12m- Based on learning incrementally on fly[39m
-[38;5;12m- Based on data points comparison and pattern detection[39m
-[38;5;12m [39m
-[38;2;255;187;0m[4mSupervised Learning[0m
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-[38;5;12m- [39m[38;5;14m[1mRegression[0m[38;5;12m (https://en.wikipedia.org/wiki/Regression)[39m
-[38;5;12m- [39m[38;5;14m[1mLinear Regression[0m[38;5;12m (https://en.wikipedia.org/wiki/Linear_regression)[39m
-[38;5;12m- [39m[38;5;14m[1mOrdinary Least Squares[0m[38;5;12m (https://en.wikipedia.org/wiki/Ordinary_least_squares)[39m
-[38;5;12m- [39m[38;5;14m[1mLogistic Regression[0m[38;5;12m (https://en.wikipedia.org/wiki/Logistic_regression)[39m
-[38;5;12m- [39m[38;5;14m[1mStepwise Regression[0m[38;5;12m (https://en.wikipedia.org/wiki/Stepwise_regression)[39m
-[38;5;12m- [39m[38;5;14m[1mMultivariate Adaptive Regression Splines[0m[38;5;12m (https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_spline)[39m
-[38;5;12m- [39m[38;5;14m[1mSoftmax Regression[0m[38;5;12m (https://d2l.ai/chapter_linear-classification/softmax-regression.html)[39m
-[38;5;12m- [39m[38;5;14m[1mLocally Estimated Scatterplot Smoothing[0m[38;5;12m (https://en.wikipedia.org/wiki/Local_regression)[39m
-[38;5;12m- Classification[39m
-[38;5;12m - [39m[38;5;14m[1mk-nearest neighbor[0m[38;5;12m (https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)[39m
-[38;5;12m - [39m[38;5;14m[1mSupport Vector Machines[0m[38;5;12m (https://en.wikipedia.org/wiki/Support_vector_machine)[39m
-[38;5;12m - [39m[38;5;14m[1mDecision Trees[0m[38;5;12m (https://en.wikipedia.org/wiki/Decision_tree)[39m
-[38;5;12m - [39m[38;5;14m[1mID3 algorithm[0m[38;5;12m (https://en.wikipedia.org/wiki/ID3_algorithm)[39m
-[38;5;12m - [39m[38;5;14m[1mC4.5 algorithm[0m[38;5;12m (https://en.wikipedia.org/wiki/C4.5_algorithm)[39m
-[38;5;12m- [39m[38;5;14m[1mEnsemble Learning[0m[38;5;12m (https://scikit-learn.org/stable/modules/ensemble.html)[39m
-[38;5;12m - [39m[38;5;14m[1mBoosting[0m[38;5;12m (https://en.wikipedia.org/wiki/Boosting_(machine_learning))[39m
-[38;5;12m - [39m[38;5;14m[1mStacking[0m[38;5;12m (https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python)[39m
-[38;5;12m - [39m[38;5;14m[1mBagging[0m[38;5;12m (https://en.wikipedia.org/wiki/Bootstrap_aggregating)[39m
-[38;5;12m - [39m[38;5;14m[1mRandom Forest[0m[38;5;12m (https://en.wikipedia.org/wiki/Random_forest)[39m
-[38;5;12m - [39m[38;5;14m[1mAdaBoost[0m[38;5;12m (https://en.wikipedia.org/wiki/AdaBoost)[39m
-
-[38;2;255;187;0m[4mUnsupervised Learning[0m
-[38;5;12m- [39m[38;5;14m[1mClustering[0m[38;5;12m (https://scikit-learn.org/stable/modules/clustering.html#clustering)[39m
-[38;5;12m - [39m[38;5;14m[1mHierchical clustering[0m[38;5;12m (https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering)[39m
-[38;5;12m - [39m[38;5;14m[1mk-means[0m[38;5;12m (https://scikit-learn.org/stable/modules/clustering.html#k-means)[39m
-[38;5;12m - [39m[38;5;14m[1mDensity-based clustering[0m[38;5;12m (https://scikit-learn.org/stable/modules/clustering.html#dbscan)[39m
-[38;5;12m - [39m[38;5;14m[1mFuzzy clustering[0m[38;5;12m (https://en.wikipedia.org/wiki/Fuzzy_clustering)[39m
-[38;5;12m - [39m[38;5;14m[1mMixture models[0m[38;5;12m (https://en.wikipedia.org/wiki/Mixture_model)[39m
-[38;5;12m- [39m[38;5;14m[1mDimension Reduction[0m[38;5;12m (https://en.wikipedia.org/wiki/Dimensionality_reduction)[39m
-[38;5;12m - [39m[38;5;14m[1mPrincipal Component Analysis (PCA)[0m[38;5;12m (https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca)[39m
-[38;5;12m - [39m[38;5;14m[1mt-SNE; t-distributed Stochastic Neighbor Embedding[0m[38;5;12m (https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca)[39m
-[38;5;12m - [39m[38;5;14m[1mFactor Analysis[0m[38;5;12m (https://scikit-learn.org/stable/modules/decomposition.html#factor-analysis)[39m
-[38;5;12m - [39m[38;5;14m[1mLatent Dirichlet Allocation (LDA)[0m[38;5;12m (https://scikit-learn.org/stable/modules/decomposition.html#latent-dirichlet-allocation-lda)[39m
-[38;5;12m- [39m[38;5;14m[1mNeural Networks[0m[38;5;12m (https://en.wikipedia.org/wiki/Neural_network)[39m
-[38;5;12m- [39m[38;5;14m[1mSelf-organizing map[0m[38;5;12m (https://en.wikipedia.org/wiki/Self-organizing_map)[39m
-[38;5;12m- [39m[38;5;14m[1mAdaptive resonance theory[0m[38;5;12m (https://en.wikipedia.org/wiki/Adaptive_resonance_theory)[39m
-[38;5;12m- [39m[38;5;14m[1mHidden Markov Models (HMM)[0m[38;5;12m (https://en.wikipedia.org/wiki/Hidden_Markov_model)[39m
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-[38;2;255;187;0m[4mSemi-Supervised Learning[0m
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-[38;5;12m- S3VM[39m
-[38;5;12m- [39m[38;5;14m[1mClustering[0m[38;5;12m (https://en.wikipedia.org/wiki/Weak_supervision#Cluster_assumption)[39m
-[38;5;12m- [39m[38;5;14m[1mGenerative models[0m[38;5;12m (https://en.wikipedia.org/wiki/Weak_supervision#Generative_models)[39m
-[38;5;12m- [39m[38;5;14m[1mLow-density separation[0m[38;5;12m (https://en.wikipedia.org/wiki/Weak_supervision#Low-density_separation)[39m
-[38;5;12m- [39m[38;5;14m[1mLaplacian regularization[0m[38;5;12m (https://en.wikipedia.org/wiki/Weak_supervision#Laplacian_regularization)[39m
-[38;5;12m- [39m[38;5;14m[1mHeuristic approaches[0m[38;5;12m (https://en.wikipedia.org/wiki/Weak_supervision#Heuristic_approaches)[39m
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-[38;2;255;187;0m[4mReinforcement Learning[0m
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-[38;5;12m- [39m[38;5;14m[1mQ Learning[0m[38;5;12m (https://en.wikipedia.org/wiki/Q-learning)[39m
-[38;5;12m- [39m[38;5;14m[1mSARSA (State-Action-Reward-State-Action) algorithm[0m[38;5;12m (https://en.wikipedia.org/wiki/State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action)[39m
-[38;5;12m- [39m[38;5;14m[1mTemporal difference learning[0m[38;5;12m (https://en.wikipedia.org/wiki/Temporal_difference_learning#:~:text=Temporal%20difference%20(TD)%20learning%20refers,estimate%20of%20the%20value%20function.)[39m
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-[38;2;255;187;0m[4mData Mining Algorithms[0m
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-[38;5;12m- [39m[38;5;14m[1mC4.5[0m[38;5;12m (https://en.wikipedia.org/wiki/C4.5_algorithm)[39m
-[38;5;12m- [39m[38;5;14m[1mk-Means[0m[38;5;12m (https://en.wikipedia.org/wiki/K-means_clustering)[39m
-[38;5;12m- [39m[38;5;14m[1mSVM (Support Vector Machine)[0m[38;5;12m (https://en.wikipedia.org/wiki/Support_vector_machine)[39m
-[38;5;12m- [39m[38;5;14m[1mApriori[0m[38;5;12m (https://en.wikipedia.org/wiki/Apriori_algorithm)[39m
-[38;5;12m- [39m[38;5;14m[1mEM (Expectation-Maximization)[0m[38;5;12m (https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)[39m
-[38;5;12m- [39m[38;5;14m[1mPageRank[0m[38;5;12m (https://en.wikipedia.org/wiki/PageRank)[39m
-[38;5;12m- [39m[38;5;14m[1mAdaBoost[0m[38;5;12m (https://en.wikipedia.org/wiki/AdaBoost)[39m
-[38;5;12m- [39m[38;5;14m[1mKNN (K-Nearest Neighbors)[0m[38;5;12m (https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)[39m
-[38;5;12m- [39m[38;5;14m[1mNaive Bayes[0m[38;5;12m (https://en.wikipedia.org/wiki/Naive_Bayes_classifier)[39m
-[38;5;12m- [39m[38;5;14m[1mCART (Classification and Regression Trees)[0m[38;5;12m (https://en.wikipedia.org/wiki/Decision_tree_learning)[39m
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-[38;2;255;187;0m[4mDeep Learning architectures[0m
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-[38;5;12m- [39m[38;5;14m[1mMultilayer Perceptron[0m[38;5;12m (https://en.wikipedia.org/wiki/Multilayer_perceptron)[39m
-[38;5;12m- [39m[38;5;14m[1mConvolutional Neural Network (CNN)[0m[38;5;12m (https://en.wikipedia.org/wiki/Convolutional_neural_network)[39m
-[38;5;12m- [39m[38;5;14m[1mRecurrent Neural Network (RNN)[0m[38;5;12m (https://en.wikipedia.org/wiki/Recurrent_neural_network)[39m
-[38;5;12m- [39m[38;5;14m[1mBoltzmann Machines[0m[38;5;12m (https://en.wikipedia.org/wiki/Boltzmann_machine)[39m
-[38;5;12m- [39m[38;5;14m[1mAutoencoder[0m[38;5;12m (https://www.tensorflow.org/tutorials/generative/autoencoder)[39m
-[38;5;12m- [39m[38;5;14m[1mGenerative Adversarial Network (GAN)[0m[38;5;12m (https://developers.google.com/machine-learning/gan/gan_structure)[39m
-[38;5;12m- [39m[38;5;14m[1mSelf-Organized Maps[0m[38;5;12m (https://en.wikipedia.org/wiki/Self-organizing_map)[39m
-[38;5;12m- [39m[38;5;14m[1mTransformer[0m[38;5;12m (https://www.tensorflow.org/text/tutorials/transformer)[39m
-[38;5;12m- [39m[38;5;14m[1mConditional Random Field (CRF)[0m[38;5;12m (https://towardsdatascience.com/conditional-random-fields-explained-e5b8256da776)[39m
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-[38;2;255;187;0m[4mGeneral Machine Learning Packages[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-learn[0m[38;5;12m (https://scikit-learn.org/)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-multilearn[0m[38;5;12m (https://github.com/scikit-multilearn/scikit-multilearn)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-expertsys[0m[38;5;12m (https://github.com/tmadl/sklearn-expertsys)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-feature[0m[38;5;12m (https://github.com/jundongl/scikit-feature)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-rebate[0m[38;5;12m (https://github.com/EpistasisLab/scikit-rebate)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mseqlearn[0m[38;5;12m (https://github.com/larsmans/seqlearn)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-bayes[0m[38;5;12m (https://github.com/AmazaspShumik/sklearn-bayes)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-crfsuite[0m[38;5;12m (https://github.com/TeamHG-Memex/sklearn-crfsuite)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-deap[0m[38;5;12m (https://github.com/rsteca/sklearn-deap)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msigopt_sklearn[0m[38;5;12m (https://github.com/sigopt/sigopt-sklearn)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-evaluation[0m[38;5;12m (https://github.com/edublancas/sklearn-evaluation)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-image[0m[38;5;12m (https://github.com/scikit-image/scikit-image)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-opt[0m[38;5;12m (https://github.com/guofei9987/scikit-opt)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-posthocs[0m[38;5;12m (https://github.com/maximtrp/scikit-posthocs)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpystruct[0m[38;5;12m (https://github.com/pystruct/pystruct)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mShogun[0m[38;5;12m (https://www.shogun-toolbox.org/)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mxLearn[0m[38;5;12m (https://github.com/aksnzhy/xlearn)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mcuML[0m[38;5;12m (https://github.com/rapidsai/cuml)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mcausalml[0m[38;5;12m (https://github.com/uber/causalml)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmlpack[0m[38;5;12m (https://github.com/mlpack/mlpack)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMLxtend[0m[38;5;12m (https://github.com/rasbt/mlxtend)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmodAL[0m[38;5;12m (https://github.com/modAL-python/modAL)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSparkit-learn[0m[38;5;12m (https://github.com/lensacom/sparkit-learn)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mhyperlearn[0m[38;5;12m (https://github.com/danielhanchen/hyperlearn)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mdlib[0m[38;5;12m (https://github.com/davisking/dlib)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mimodels[0m[38;5;12m (https://github.com/csinva/imodels)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRuleFit[0m[38;5;12m (https://github.com/christophM/rulefit)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpyGAM[0m[38;5;12m (https://github.com/dswah/pyGAM)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDeepchecks[0m[38;5;12m (https://github.com/deepchecks/deepchecks)[39m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-survival[0m[38;5;12m (https://scikit-survival.readthedocs.io/en/stable)[39m
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-[38;2;255;187;0m[4mDeep Learning Packages[0m
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-[38;2;255;187;0m[4mPyTorch Ecosystem[0m
-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPyTorch[0m[38;5;12m (https://github.com/pytorch/pytorch)[39m
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-[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTensorLayer[0m[38;5;12m (https://github.com/tensorlayer/TensorLayer)[39m
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-[38;2;255;187;0m[4mKeras Ecosystem[0m
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-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;239m│[39m[38;5;14m[1mNeptune.ai[0m[38;5;12m (https://neptune.ai)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mCommunity-friendly[39m[38;5;12m [39m[38;5;12mplatform[39m[38;5;12m [39m[38;5;12msupporting[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mscientists[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mcreating[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12msharing[39m[38;5;12m [39m[38;5;12mmachine[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mmodels.[39m[38;5;12m [39m[38;5;12mNeptune[39m[38;5;12m [39m[38;5;12mfacilitates[39m[38;5;12m [39m[38;5;12mteamwork,[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12minfrastructure[39m[38;5;12m [39m[38;5;12mmanagement,[39m[38;5;12m [39m[38;5;12mmodels[39m[38;5;12m [39m[38;5;12mcomparison[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mreproducibility.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1msteppy[0m[38;5;12m (https://github.com/minerva-ml/steppy)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mLightweight,[39m[38;5;12m [39m[38;5;12mPython[39m[38;5;12m [39m[38;5;12mlibrary[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mfast[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mreproducible[39m[38;5;12m [39m[38;5;12mmachine[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mexperimentation.[39m[38;5;12m [39m[38;5;12mIntroduces[39m[38;5;12m [39m[38;5;12mvery[39m[38;5;12m [39m[38;5;12msimple[39m[38;5;12m [39m[38;5;12minterface[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12menables[39m[38;5;12m [39m[38;5;12mclean[39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mmachine[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mpipeline[39m[38;5;12m [39m[38;5;12mdesign.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1msteppy-toolkit[0m[38;5;12m (https://github.com/minerva-ml/steppy-toolkit)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mCurated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mDatalab from Google[0m[38;5;12m (https://cloud.google.com/datalab/docs/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12measily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mHortonworks Sandbox[0m[38;5;12m (https://www.cloudera.com/downloads/hortonworks-sandbox.html)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mis a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mR[0m[38;5;12m (https://www.r-project.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mis a free software environment for statistical computing and graphics.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mTidyverse[0m[38;5;12m (https://www.tidyverse.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mis[39m[38;5;12m [39m[38;5;12man[39m[38;5;12m [39m[38;5;12mopinionated[39m[38;5;12m [39m[38;5;12mcollection[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mR[39m[38;5;12m [39m[38;5;12mpackages[39m[38;5;12m [39m[38;5;12mdesigned[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mscience.[39m[38;5;12m [39m[38;5;12mAll[39m[38;5;12m [39m[38;5;12mpackages[39m[38;5;12m [39m[38;5;12mshare[39m[38;5;12m [39m[38;5;12man[39m[38;5;12m [39m[38;5;12munderlying[39m[38;5;12m [39m[38;5;12mdesign[39m[38;5;12m [39m[38;5;12mphilosophy,[39m[38;5;12m [39m[38;5;12mgrammar,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mstructures.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mRStudio[0m[38;5;12m (https://www.rstudio.com)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mIDE – powerful user interface for R. It’s free and open source, and works on Windows, Mac, and Linux.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mPython - Pandas - Anaconda[0m[38;5;12m (https://www.anaconda.com)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mCompletely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mPandas GUI[0m[38;5;12m (https://github.com/adrotog/PandasGUI)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mPandas GUI[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mScikit-Learn[0m[38;5;12m (https://scikit-learn.org/stable/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mMachine Learning in Python[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mNumPy[0m[38;5;12m (https://numpy.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mNumPy[39m[38;5;12m [39m[38;5;12mis[39m[38;5;12m [39m[38;5;12mfundamental[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mscientific[39m[38;5;12m [39m[38;5;12mcomputing[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mPython.[39m[38;5;12m [39m[38;5;12mIt[39m[38;5;12m [39m[38;5;12msupports[39m[38;5;12m [39m[38;5;12mlarge,[39m[38;5;12m [39m[38;5;12mmulti-dimensional[39m[38;5;12m [39m[38;5;12marrays[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mmatrices[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mincludes[39m[38;5;12m [39m[38;5;12man[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12massortment[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mhigh-level[39m[38;5;12m [39m[38;5;12mmathematical[39m[38;5;12m [39m[38;5;12mfunctions[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12moperate[39m[38;5;12m [39m[38;5;12mon[39m[38;5;12m [39m[38;5;12mthese[39m[38;5;12m [39m[38;5;12marrays.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mVaex[0m[38;5;12m (https://vaex.io/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mVaex is a Python library that allows you to visualize large datasets and calculate statistics at high speeds.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mSciPy[0m[38;5;12m (https://scipy.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mSciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mData Science Toolbox[0m[38;5;12m (https://www.coursera.org/learn/data-scientists-tools)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mCoursera Course[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mData Science Toolbox[0m[38;5;12m (https://datasciencetoolbox.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mBlog[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mWolfram Data Science Platform[0m[38;5;12m (https://www.wolfram.com/data-science-platform/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mTake[39m[38;5;12m [39m[38;5;12mnumerical,[39m[38;5;12m [39m[38;5;12mtextual,[39m[38;5;12m [39m[38;5;12mimage,[39m[38;5;12m [39m[38;5;12mGIS[39m[38;5;12m [39m[38;5;12mor[39m[38;5;12m [39m[38;5;12mother[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mgive[39m[38;5;12m [39m[38;5;12mit[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mWolfram[39m[38;5;12m [39m[38;5;12mtreatment,[39m[38;5;12m [39m[38;5;12mcarrying[39m[38;5;12m [39m[38;5;12mout[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mfull[39m[38;5;12m [39m[38;5;12mspectrum[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mscience[39m[38;5;12m [39m[38;5;12manalysis[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mvisualization[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mautomatically[39m[38;5;12m [39m[38;5;12mgenerate[39m[38;5;12m [39m[38;5;12mrich[39m[38;5;12m [39m[38;5;12minteractive[39m[38;5;12m [39m[38;5;12mreports—all[39m[38;5;12m [39m[38;5;12mpowered[39m[38;5;12m [39m[38;5;12mby[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mrevolutionary[39m[38;5;12m [39m[38;5;12mknowledge-based[39m[38;5;12m [39m[38;5;12mWolfram[39m[38;5;12m [39m[38;5;12mLanguage.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mDatadog[0m[38;5;12m (https://www.datadoghq.com/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mSolutions, code, and devops for high-scale data science.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mVariance[0m[38;5;12m (https://variancecharts.com/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mBuild powerful data visualizations for the web without writing JavaScript[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mKite Development Kit[0m[38;5;12m (https://kitesdk.org/docs/current/index.html)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mThe[39m[38;5;12m [39m[38;5;12mKite[39m[38;5;12m [39m[38;5;12mSoftware[39m[38;5;12m [39m[38;5;12mDevelopment[39m[38;5;12m [39m[38;5;12mKit[39m[38;5;12m [39m[38;5;12m(Apache[39m[38;5;12m [39m[38;5;12mLicense,[39m[38;5;12m [39m[38;5;12mVersion[39m[38;5;12m [39m[38;5;12m2.0),[39m[38;5;12m [39m[38;5;12mor[39m[38;5;12m [39m[38;5;12mKite[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mshort,[39m[38;5;12m [39m[38;5;12mis[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mset[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mlibraries,[39m[38;5;12m [39m[38;5;12mtools,[39m[38;5;12m [39m[38;5;12mexamples,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mdocumentation[39m[38;5;12m [39m[38;5;12mfocused[39m[38;5;12m [39m[38;5;12mon[39m[38;5;12m [39m[38;5;12mmaking[39m[38;5;12m [39m[38;5;12mit[39m[38;5;12m [39m[38;5;12measier[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mbuild[39m[38;5;12m [39m[38;5;12msystems[39m[38;5;12m [39m[38;5;12mon[39m[38;5;12m [39m[38;5;12mtop[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mHadoop[39m[38;5;12m [39m[38;5;12mecosystem.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mDomino Data Labs[0m[38;5;12m (https://www.dominodatalab.com)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mRun, scale, share, and deploy your models — without any infrastructure or setup.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mApache Flink[0m[38;5;12m (https://flink.apache.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA platform for efficient, distributed, general-purpose data processing.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mApache Hama[0m[38;5;12m (https://hama.apache.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mApache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mWeka[0m[38;5;12m (https://www.cs.waikato.ac.nz/ml/weka/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mWeka is a collection of machine learning algorithms for data mining tasks.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mOctave[0m[38;5;12m (https://www.gnu.org/software/octave/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mGNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab)[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mApache Spark[0m[38;5;12m (https://spark.apache.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mLightning-fast cluster computing[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mHydrosphere Mist[0m[38;5;12m (https://github.com/Hydrospheredata/mist)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12ma service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mData Mechanics[0m[38;5;12m (https://www.datamechanics.co)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA data science and engineering platform making Apache Spark more developer-friendly and cost-effective.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mCaffe[0m[38;5;12m (https://caffe.berkeleyvision.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mDeep Learning Framework[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mTorch[0m[38;5;12m (https://torch.ch/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mNervana's python based Deep Learning Framework[0m[38;5;12m (https://github.com/NervanaSystems/neon)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mIntel® Nervana™ reference deep learning framework committed to best performance on all hardware.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mSkale[0m[38;5;12m (https://github.com/skale-me/skale)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mHigh performance distributed data processing in NodeJS[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mAerosolve[0m[38;5;12m (https://airbnb.io/aerosolve/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA machine learning package built for humans.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mIntel framework[0m[38;5;12m (https://github.com/intel/idlf)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mIntel® Deep Learning Framework[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mDatawrapper[0m[38;5;12m (https://www.datawrapper.de/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAn[39m[38;5;12m [39m[38;5;12mopen[39m[38;5;12m [39m[38;5;12msource[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mvisualization[39m[38;5;12m [39m[38;5;12mplatform[39m[38;5;12m [39m[38;5;12mhelping[39m[38;5;12m [39m[38;5;12meveryone[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mcreate[39m[38;5;12m [39m[38;5;12msimple,[39m[38;5;12m [39m[38;5;12mcorrect[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12membeddable[39m[38;5;12m [39m[38;5;12mcharts.[39m[38;5;12m [39m[38;5;12mAlso[39m[38;5;12m [39m[38;5;12mat[39m[38;5;12m [39m[38;5;14m[1mgithub.com[0m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m(https://github.com/datawrapper/datawrapper)[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mTensor Flow[0m[38;5;12m (https://www.tensorflow.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mTensorFlow is an Open Source Software Library for Machine Intelligence[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mNatural Language Toolkit[0m[38;5;12m (https://www.nltk.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAn introductory yet powerful toolkit for natural language processing and classification[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mAnnotation Lab[0m[38;5;12m (https://www.johnsnowlabs.com/annotation-lab/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mFree[39m[38;5;12m [39m[38;5;12mEnd-to-End[39m[38;5;12m [39m[38;5;12mNo-Code[39m[38;5;12m [39m[38;5;12mplatform[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mtext[39m[38;5;12m [39m[38;5;12mannotation[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mDL[39m[38;5;12m [39m[38;5;12mmodel[39m[38;5;12m [39m[38;5;12mtraining/tuning.[39m[38;5;12m [39m[38;5;12mOut-of-the-box[39m[38;5;12m [39m[38;5;12msupport[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mNamed[39m[38;5;12m [39m[38;5;12mEntity[39m[38;5;12m [39m[38;5;12mRecognition,[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mClassification,[39m[38;5;12m [39m[38;5;12mRelation[39m[38;5;12m [39m[38;5;12mextraction[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mAssertion[39m[38;5;12m [39m[38;5;12mStatus[39m[38;5;12m [39m[38;5;12mSpark[39m[38;5;12m [39m[38;5;12mNLP[39m[38;5;12m [39m[38;5;12mmodels.[39m[38;5;12m [39m[38;5;12mUnlimited[39m[38;5;12m [39m[38;5;12msupport[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12musers,[39m[38;5;12m [39m[38;5;12mteams,[39m[38;5;12m [39m[38;5;12mprojects,[39m[38;5;12m [39m[38;5;12mdocuments.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mnlp-toolkit for node.js[0m[38;5;12m (https://www.npmjs.com/package/nlp-toolkit)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mThis[39m[38;5;12m [39m[38;5;12mmodule[39m[38;5;12m [39m[38;5;12mcovers[39m[38;5;12m [39m[38;5;12msome[39m[38;5;12m [39m[38;5;12mbasic[39m[38;5;12m [39m[38;5;12mnlp[39m[38;5;12m [39m[38;5;12mprinciples[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mimplementations.[39m[38;5;12m [39m[38;5;12mThe[39m[38;5;12m [39m[38;5;12mmain[39m[38;5;12m [39m[38;5;12mfocus[39m[38;5;12m [39m[38;5;12mis[39m[38;5;12m [39m[38;5;12mperformance.[39m[38;5;12m [39m[38;5;12mWhen[39m[38;5;12m [39m[38;5;12mwe[39m[38;5;12m [39m[38;5;12mdeal[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12msample[39m[38;5;12m [39m[38;5;12mor[39m[38;5;12m [39m[38;5;12mtraining[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12min[39m[38;5;12m [39m[38;5;12mnlp,[39m[38;5;12m [39m[38;5;12mwe[39m[38;5;12m [39m[38;5;12mquickly[39m[38;5;12m [39m[38;5;12mrun[39m[38;5;12m [39m[38;5;12mout[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mmemory.[39m[38;5;12m [39m[38;5;12mTherefore[39m[38;5;12m [39m[38;5;12mevery[39m[38;5;12m [39m[38;5;12mimplementation[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mthis[39m[38;5;12m [39m[38;5;12mmodule[39m[38;5;12m [39m[38;5;12mis[39m[38;5;12m [39m[38;5;12mwritten[39m[38;5;12m [39m[38;5;12mas[39m[38;5;12m [39m[38;5;12mstream[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12monly[39m[38;5;12m [39m[38;5;12mhold[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mmemory[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mis[39m[38;5;12m [39m[38;5;12mcurrently[39m[38;5;12m [39m[38;5;12mprocessed[39m[38;5;12m [39m[38;5;12mat[39m[38;5;12m [39m[38;5;12many[39m[38;5;12m [39m[38;5;12mstep.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mJulia[0m[38;5;12m (https://julialang.org)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mhigh-level, high-performance dynamic programming language for technical computing[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mIJulia[0m[38;5;12m (https://github.com/JuliaLang/IJulia.jl)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12ma Julia-language backend combined with the Jupyter interactive environment[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mApache Zeppelin[0m[38;5;12m (https://zeppelin.apache.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mWeb-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mFeaturetools[0m[38;5;12m (https://github.com/alteryx/featuretools)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAn open source framework for automated feature engineering written in python[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mOptimus[0m[38;5;12m (https://github.com/hi-primus/optimus)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mCleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mAlbumentations[0m[38;5;12m (https://github.com/albumentations-team/albumentations)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mА[39m[38;5;12m [39m[38;5;12mfast[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mframework[39m[38;5;12m [39m[38;5;12magnostic[39m[38;5;12m [39m[38;5;12mimage[39m[38;5;12m [39m[38;5;12maugmentation[39m[38;5;12m [39m[38;5;12mlibrary[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mimplements[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mdiverse[39m[38;5;12m [39m[38;5;12mset[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12maugmentation[39m[38;5;12m [39m[38;5;12mtechniques.[39m[38;5;12m [39m[38;5;12mSupports[39m[38;5;12m [39m[38;5;12mclassification,[39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12msegmentation,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mdetection[39m[38;5;12m [39m[38;5;12mout[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mbox.[39m[38;5;12m [39m[38;5;12mWas[39m[38;5;12m [39m[38;5;12mused[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mwin[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mnumber[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mDeep[39m[38;5;12m [39m[38;5;12mLearning[39m[38;5;12m [39m[38;5;12mcompetitions[39m[38;5;12m [39m[38;5;12mat[39m[38;5;12m [39m[38;5;12mKaggle,[39m[38;5;12m [39m[38;5;12mTopcoder[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mthose[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mwere[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mpart[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mCVPR[39m[38;5;12m [39m[38;5;12mworkshops.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mDVC[0m[38;5;12m (https://github.com/iterative/dvc)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAn[39m[38;5;12m [39m[38;5;12mopen-source[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mscience[39m[38;5;12m [39m[38;5;12mversion[39m[38;5;12m [39m[38;5;12mcontrol[39m[38;5;12m [39m[38;5;12msystem.[39m[38;5;12m [39m[38;5;12mIt[39m[38;5;12m [39m[38;5;12mhelps[39m[38;5;12m [39m[38;5;12mtrack,[39m[38;5;12m [39m[38;5;12morganize[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mmake[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mscience[39m[38;5;12m [39m[38;5;12mprojects[39m[38;5;12m [39m[38;5;12mreproducible.[39m[38;5;12m [39m[38;5;12mIn[39m[38;5;12m [39m[38;5;12mits[39m[38;5;12m [39m[38;5;12mvery[39m[38;5;12m [39m[38;5;12mbasic[39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mscenario[39m[38;5;12m [39m[38;5;12mit[39m[38;5;12m [39m[38;5;12mhelps[39m[38;5;12m [39m[38;5;12mversion[39m[38;5;12m [39m[38;5;12mcontrol[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mshare[39m[38;5;12m [39m[38;5;12mlarge[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mmodel[39m[38;5;12m [39m[38;5;12mfiles.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mLambdo[0m[38;5;12m (https://github.com/asavinov/lambdo)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mis[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mworkflow[39m[38;5;12m [39m[38;5;12mengine[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12msignificantly[39m[38;5;12m [39m[38;5;12msimplifies[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12manalysis[39m[38;5;12m [39m[38;5;12mby[39m[38;5;12m [39m[38;5;12mcombining[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mone[39m[38;5;12m [39m[38;5;12manalysis[39m[38;5;12m [39m[38;5;12mpipeline[39m[38;5;12m [39m[38;5;12m(i)[39m[38;5;12m [39m[38;5;12mfeature[39m[38;5;12m [39m[38;5;12mengineering[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mmachine[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12m(ii)[39m[38;5;12m [39m[38;5;12mmodel[39m[38;5;12m [39m[38;5;12mtraining[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mprediction[39m[38;5;12m [39m[38;5;12m(iii)[39m[38;5;12m [39m[38;5;12mtable[39m[38;5;12m [39m[38;5;12mpopulation[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mcolumn[39m[38;5;12m [39m[38;5;12mevaluation.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mFeast[0m[38;5;12m (https://github.com/feast-dev/feast)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA[39m[38;5;12m [39m[38;5;12mfeature[39m[38;5;12m [39m[38;5;12mstore[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mmanagement,[39m[38;5;12m [39m[38;5;12mdiscovery,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12maccess[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mmachine[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mfeatures.[39m[38;5;12m [39m[38;5;12mFeast[39m[38;5;12m [39m[38;5;12mprovides[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mconsistent[39m[38;5;12m [39m[38;5;12mview[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mfeature[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mboth[39m[38;5;12m [39m[38;5;12mmodel[39m[38;5;12m [39m[38;5;12mtraining[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mmodel[39m[38;5;12m [39m[38;5;12mserving.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mPolyaxon[0m[38;5;12m (https://github.com/polyaxon/polyaxon)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA platform for reproducible and scalable machine learning and deep learning.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mLightTag[0m[38;5;12m (https://www.lighttag.io/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mText Annotation Tool for teams[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mUBIAI[0m[38;5;12m (https://ubiai.tools)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mEasy-to-use[39m[38;5;12m [39m[38;5;12mtext[39m[38;5;12m [39m[38;5;12mannotation[39m[38;5;12m [39m[38;5;12mtool[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mteams[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mmost[39m[38;5;12m [39m[38;5;12mcomprehensive[39m[38;5;12m [39m[38;5;12mauto-annotation[39m[38;5;12m [39m[38;5;12mfeatures.[39m[38;5;12m [39m[38;5;12mSupports[39m[38;5;12m [39m[38;5;12mNER,[39m[38;5;12m [39m[38;5;12mrelations[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mdocument[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mclassification[39m[38;5;12m [39m[38;5;12mas[39m[38;5;12m [39m[38;5;12mwell[39m[38;5;12m [39m[38;5;12mas[39m[38;5;12m [39m[38;5;12mOCR[39m[38;5;12m [39m[38;5;12mannotation[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12minvoice[39m[38;5;12m [39m[38;5;12mlabeling[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mTrains[0m[38;5;12m (https://github.com/allegroai/clearml)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAuto-Magical Experiment Manager, Version Control & DevOps for AI[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mHopsworks[0m[38;5;12m (https://github.com/logicalclocks/hopsworks)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mOpen-source[39m[38;5;12m [39m[38;5;12mdata-intensive[39m[38;5;12m [39m[38;5;12mmachine[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mplatform[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mfeature[39m[38;5;12m [39m[38;5;12mstore.[39m[38;5;12m [39m[38;5;12mIngest[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mmanage[39m[38;5;12m [39m[38;5;12mfeatures[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mboth[39m[38;5;12m [39m[38;5;12monline[39m[38;5;12m [39m[38;5;12m(MySQL[39m[38;5;12m [39m[38;5;12mCluster)[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12moffline[39m[38;5;12m [39m[38;5;12m(Apache[39m[38;5;12m [39m[38;5;12mHive)[39m[38;5;12m [39m[38;5;12maccess,[39m[38;5;12m [39m[38;5;12mtrain[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mserve[39m[38;5;12m [39m[38;5;12mmodels[39m[38;5;12m [39m[38;5;12mat[39m[38;5;12m [39m[38;5;12mscale.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mMindsDB[0m[38;5;12m (https://github.com/mindsdb/mindsdb)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mMindsDB[39m[38;5;12m [39m[38;5;12mis[39m[38;5;12m [39m[38;5;12man[39m[38;5;12m [39m[38;5;12mExplainable[39m[38;5;12m [39m[38;5;12mAutoML[39m[38;5;12m [39m[38;5;12mframework[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mdevelopers.[39m[38;5;12m [39m[38;5;12mWith[39m[38;5;12m [39m[38;5;12mMindsDB[39m[38;5;12m [39m[38;5;12myou[39m[38;5;12m [39m[38;5;12mcan[39m[38;5;12m [39m[38;5;12mbuild,[39m[38;5;12m [39m[38;5;12mtrain[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12muse[39m[38;5;12m [39m[38;5;12mstate[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mart[39m[38;5;12m [39m[38;5;12mML[39m[38;5;12m [39m[38;5;12mmodels[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mas[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12msimple[39m[38;5;12m [39m[38;5;12mas[39m[38;5;12m [39m[38;5;12mone[39m[38;5;12m [39m[38;5;12mline[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mcode.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mLightwood[0m[38;5;12m (https://github.com/mindsdb/lightwood)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA[39m[38;5;12m [39m[38;5;12mPytorch[39m[38;5;12m [39m[38;5;12mbased[39m[38;5;12m [39m[38;5;12mframework[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mbreaks[39m[38;5;12m [39m[38;5;12mdown[39m[38;5;12m [39m[38;5;12mmachine[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mproblems[39m[38;5;12m [39m[38;5;12minto[39m[38;5;12m [39m[38;5;12msmaller[39m[38;5;12m [39m[38;5;12mblocks[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mcan[39m[38;5;12m [39m[38;5;12mbe[39m[38;5;12m [39m[38;5;12mglued[39m[38;5;12m [39m[38;5;12mtogether[39m[38;5;12m [39m[38;5;12mseamlessly[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12man[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mobjective[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mbuild[39m[38;5;12m [39m[38;5;12mpredictive[39m[38;5;12m [39m[38;5;12mmodels[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mone[39m[38;5;12m [39m[38;5;12mline[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mcode.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mAWS Data Wrangler[0m[38;5;12m (https://github.com/awslabs/aws-data-wrangler)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAn[39m[38;5;12m [39m[38;5;12mopen-source[39m[38;5;12m [39m[38;5;12mPython[39m[38;5;12m [39m[38;5;12mpackage[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mextends[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mpower[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mPandas[39m[38;5;12m [39m[38;5;12mlibrary[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mAWS[39m[38;5;12m [39m[38;5;12mconnecting[39m[38;5;12m [39m[38;5;12mDataFrames[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mAWS[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mrelated[39m[38;5;12m [39m[38;5;12mservices[39m[38;5;12m [39m[38;5;12m(Amazon[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mRedshift,[39m[38;5;12m [39m[38;5;12mAWS[39m[38;5;12m [39m[38;5;12mGlue,[39m[38;5;12m [39m[38;5;12mAmazon[39m[38;5;12m [39m[38;5;12mAthena,[39m[38;5;12m [39m[38;5;12mAmazon[39m[38;5;12m [39m[38;5;12mEMR,[39m[38;5;12m [39m[38;5;12metc).[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mAmazon Rekognition[0m[38;5;12m (https://aws.amazon.com/rekognition/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAWS[39m[38;5;12m [39m[38;5;12mRekognition[39m[38;5;12m [39m[38;5;12mis[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mservice[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mlets[39m[38;5;12m [39m[38;5;12mdevelopers[39m[38;5;12m [39m[38;5;12mworking[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mAmazon[39m[38;5;12m [39m[38;5;12mWeb[39m[38;5;12m [39m[38;5;12mServices[39m[38;5;12m [39m[38;5;12madd[39m[38;5;12m [39m[38;5;12mimage[39m[38;5;12m [39m[38;5;12manalysis[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mtheir[39m[38;5;12m [39m[38;5;12mapplications.[39m[38;5;12m [39m[38;5;12mCatalog[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12massets,[39m[38;5;12m [39m[38;5;12mautomate[39m[38;5;12m [39m[38;5;12mworkflows,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mextract[39m[38;5;12m [39m[38;5;12mmeaning[39m[38;5;12m [39m[38;5;12mfrom[39m[38;5;12m [39m[38;5;12myour[39m[38;5;12m [39m[38;5;12mmedia[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mapplications.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mAmazon Textract[0m[38;5;12m (https://aws.amazon.com/textract/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAutomatically extract printed text, handwriting, and data from any document.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mAmazon Lookout for Vision[0m[38;5;12m (https://aws.amazon.com/lookout-for-vision/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mSpot[39m[38;5;12m [39m[38;5;12mproduct[39m[38;5;12m [39m[38;5;12mdefects[39m[38;5;12m [39m[38;5;12musing[39m[38;5;12m [39m[38;5;12mcomputer[39m[38;5;12m [39m[38;5;12mvision[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mautomate[39m[38;5;12m [39m[38;5;12mquality[39m[38;5;12m [39m[38;5;12minspection.[39m[38;5;12m [39m[38;5;12mIdentify[39m[38;5;12m [39m[38;5;12mmissing[39m[38;5;12m [39m[38;5;12mproduct[39m[38;5;12m [39m[38;5;12mcomponents,[39m[38;5;12m [39m[38;5;12mvehicle[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mstructure[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mdamage,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mirregularities[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mcomprehensive[39m[38;5;12m [39m[38;5;12mquality[39m[38;5;12m [39m[38;5;12mcontrol.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mAmazon CodeGuru[0m[38;5;12m (https://aws.amazon.com/codeguru/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAutomate code reviews and optimize application performance with ML-powered recommendations.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mCML[0m[38;5;12m (https://github.com/iterative/cml)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAn[39m[38;5;12m [39m[38;5;12mopen[39m[38;5;12m [39m[38;5;12msource[39m[38;5;12m [39m[38;5;12mtoolkit[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12musing[39m[38;5;12m [39m[38;5;12mcontinuous[39m[38;5;12m [39m[38;5;12mintegration[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mscience[39m[38;5;12m [39m[38;5;12mprojects.[39m[38;5;12m [39m[38;5;12mAutomatically[39m[38;5;12m [39m[38;5;12mtrain[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mtest[39m[38;5;12m [39m[38;5;12mmodels[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mproduction-like[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12menvironments[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mGitHub[39m[38;5;12m [39m[38;5;12mActions[39m[38;5;12m [39m[38;5;12m&[39m[38;5;12m [39m[38;5;12mGitLab[39m[38;5;12m [39m[38;5;12mCI,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mautogenerate[39m[38;5;12m [39m[38;5;12mvisual[39m[38;5;12m [39m[38;5;12mreports[39m[38;5;12m [39m[38;5;12mon[39m[38;5;12m [39m[38;5;12mpull/merge[39m[38;5;12m [39m[38;5;12mrequests.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mDask[0m[38;5;12m (https://dask.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAn open source Python library to painlessly transition your analytics code to distributed computing systems (Big Data)[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mStatsmodels[0m[38;5;12m (https://www.statsmodels.org/stable/index.html)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA Python-based inferential statistics, hypothesis testing and regression framework[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mGensim[0m[38;5;12m (https://radimrehurek.com/gensim/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAn open-source library for topic modeling of natural language text[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mspaCy[0m[38;5;12m (https://spacy.io/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA performant natural language processing toolkit[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mGrid Studio[0m[38;5;12m (https://github.com/ricklamers/gridstudio)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mGrid studio is a web-based spreadsheet application with full integration of the Python programming language.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mPython Data Science Handbook[0m[38;5;12m (https://github.com/jakevdp/PythonDataScienceHandbook)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mPython Data Science Handbook: full text in Jupyter Notebooks[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mShapley[0m[38;5;12m (https://github.com/benedekrozemberczki/shapley)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA data-driven framework to quantify the value of classifiers in a machine learning ensemble.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mDAGsHub[0m[38;5;12m (https://dagshub.com)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA platform built on open source tools for data, model and pipeline management.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mDeepnote[0m[38;5;12m (https://deepnote.com)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA new kind of data science notebook. Jupyter-compatible, with real-time collaboration and running in the cloud.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mValohai[0m[38;5;12m (https://valohai.com)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAn MLOps platform that handles machine orchestration, automatic reproducibility and deployment.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mPyMC3[0m[38;5;12m (https://docs.pymc.io/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA Python Library for Probabalistic Programming (Bayesian Inference and Machine Learning)[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mPyStan[0m[38;5;12m (https://pypi.org/project/pystan/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mPython interface to Stan (Bayesian inference and modeling)[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mhmmlearn[0m[38;5;12m (https://pypi.org/project/hmmlearn/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mUnsupervised learning and inference of Hidden Markov Models[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mChaos Genius[0m[38;5;12m (https://github.com/chaos-genius/chaos_genius/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mML powered analytics engine for outlier/anomaly detection and root cause analysis[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mNimblebox[0m[38;5;12m (https://nimblebox.ai/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA[39m[38;5;12m [39m[38;5;12mfull-stack[39m[38;5;12m [39m[38;5;12mMLOps[39m[38;5;12m [39m[38;5;12mplatform[39m[38;5;12m [39m[38;5;12mdesigned[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mhelp[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mscientists[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mmachine[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mpractitioners[39m[38;5;12m [39m[38;5;12maround[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mworld[39m[38;5;12m [39m[38;5;12mdiscover,[39m[38;5;12m [39m[38;5;12mcreate,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mlaunch[39m[38;5;12m [39m[38;5;12mmulti-cloud[39m[38;5;12m [39m[38;5;12mapps[39m[38;5;12m [39m[38;5;12mfrom[39m[38;5;12m [39m[38;5;12mtheir[39m[38;5;12m [39m[38;5;12mweb[39m[38;5;12m [39m[38;5;12mbrowser.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mTowhee[0m[38;5;12m (https://github.com/towhee-io/towhee)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA Python library that helps you encode your unstructured data into embeddings.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mLineaPy[0m[38;5;12m (https://github.com/LineaLabs/lineapy)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mEver[39m[38;5;12m [39m[38;5;12mbeen[39m[38;5;12m [39m[38;5;12mfrustrated[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mcleaning[39m[38;5;12m [39m[38;5;12mup[39m[38;5;12m [39m[38;5;12mlong,[39m[38;5;12m [39m[38;5;12mmessy[39m[38;5;12m [39m[38;5;12mJupyter[39m[38;5;12m [39m[38;5;12mnotebooks?[39m[38;5;12m [39m[38;5;12mWith[39m[38;5;12m [39m[38;5;12mLineaPy,[39m[38;5;12m [39m[38;5;12man[39m[38;5;12m [39m[38;5;12mopen[39m[38;5;12m [39m[38;5;12msource[39m[38;5;12m [39m[38;5;12mPython[39m[38;5;12m [39m[38;5;12mlibrary,[39m[38;5;12m [39m[38;5;12mit[39m[38;5;12m [39m[38;5;12mtakes[39m[38;5;12m [39m[38;5;12mas[39m[38;5;12m [39m[38;5;12mlittle[39m[38;5;12m [39m[38;5;12mas[39m[38;5;12m [39m[38;5;12mtwo[39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mlines[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mcode[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mtransform[39m[38;5;12m [39m[38;5;12mmessy[39m[38;5;12m [39m[38;5;12mdevelopment[39m[38;5;12m [39m[38;5;12mcode[39m[38;5;12m [39m[38;5;12minto[39m[38;5;12m [39m[38;5;12mproduction[39m[38;5;12m [39m[38;5;12mpipelines.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1menvd[0m[38;5;12m (https://github.com/tensorchord/envd)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m🏕️ machine learning development environment for data science and AI/ML engineering teams[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mExplore Data Science Libraries[0m[38;5;12m (https://kandi.openweaver.com/explore/data-science)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA[39m[38;5;12m [39m[38;5;12msearch[39m[38;5;12m [39m[38;5;12mengine[39m[38;5;12m [39m[38;5;12m🔎[39m[38;5;12m [39m[38;5;12mtool[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mdiscover[39m[38;5;12m [39m[38;5;12m&[39m[38;5;12m [39m[38;5;12mfind[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mcurated[39m[38;5;12m [39m[38;5;12mlist[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mpopular[39m[38;5;12m [39m[38;5;12m&[39m[38;5;12m [39m[38;5;12mnew[39m[38;5;12m [39m[38;5;12mlibraries,[39m[38;5;12m [39m[38;5;12mtop[39m[38;5;12m [39m[38;5;12mauthors,[39m[38;5;12m [39m[38;5;12mtrending[39m[38;5;12m [39m[38;5;12mproject[39m[38;5;12m [39m[38;5;12mkits,[39m[38;5;12m [39m[38;5;12mdiscussions,[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mtutorials[39m[38;5;12m [39m[38;5;12m&[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mresources[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mMLEM[0m[38;5;12m (https://github.com/iterative/mlem)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m🐶 Version and deploy your ML models following GitOps principles[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mMLflow[0m[38;5;12m (https://mlflow.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mMLOps framework for managing ML models across their full lifecycle[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mcleanlab[0m[38;5;12m (https://github.com/cleanlab/cleanlab)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mPython library for data-centric AI and automatically detecting various issues in ML datasets[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mAutoGluon[0m[38;5;12m (https://github.com/awslabs/autogluon)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAutoML to easily produce accurate predictions for image, text, tabular, time-series, and multi-modal data[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mArize AI[0m[38;5;12m (https://arize.com/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mArize[39m[38;5;12m [39m[38;5;12mAI[39m[38;5;12m [39m[38;5;12mcommunity[39m[38;5;12m [39m[38;5;12mtier[39m[38;5;12m [39m[38;5;12mobservability[39m[38;5;12m [39m[38;5;12mtool[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mmonitoring[39m[38;5;12m [39m[38;5;12mmachine[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mmodels[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mproduction[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mroot-causing[39m[38;5;12m [39m[38;5;12missues[39m[38;5;12m [39m[38;5;12msuch[39m[38;5;12m [39m[38;5;12mas[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mquality[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mperformance[39m[38;5;12m [39m[38;5;12mdrift.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mAureo.io[0m[38;5;12m (https://aureo.io)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAureo.io[39m[38;5;12m [39m[38;5;12mis[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mlow-code[39m[38;5;12m [39m[38;5;12mplatform[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mfocuses[39m[38;5;12m [39m[38;5;12mon[39m[38;5;12m [39m[38;5;12mbuilding[39m[38;5;12m [39m[38;5;12martificial[39m[38;5;12m [39m[38;5;12mintelligence.[39m[38;5;12m [39m[38;5;12mIt[39m[38;5;12m [39m[38;5;12mprovides[39m[38;5;12m [39m[38;5;12musers[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mcapability[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mcreate[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mpipelines,[39m[38;5;12m [39m[38;5;12mautomations[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mintegrate[39m[38;5;12m [39m[38;5;12mthem[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12martificial[39m[38;5;12m [39m[38;5;12mintelligence[39m[38;5;12m [39m[38;5;12mmodels[39m[38;5;12m [39m[38;5;12m–[39m[38;5;12m [39m[38;5;12mall[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mtheir[39m[38;5;12m [39m[38;5;12mbasic[39m[38;5;12m [39m[38;5;12mdata.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mERD Lab[0m[38;5;12m (https://www.erdlab.io/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mFree cloud based entity relationship diagram (ERD) tool made for developers.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mArize-Phoenix[0m[38;5;12m (https://docs.arize.com/phoenix)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mMLOps in a notebook - uncover insights, surface problems, monitor, and fine tune your models.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mComet[0m[38;5;12m (https://github.com/comet-ml/comet-examples)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAn[39m[38;5;12m [39m[38;5;12mMLOps[39m[38;5;12m [39m[38;5;12mplatform[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mexperiment[39m[38;5;12m [39m[38;5;12mtracking,[39m[38;5;12m [39m[38;5;12mmodel[39m[38;5;12m [39m[38;5;12mproduction[39m[38;5;12m [39m[38;5;12mmanagement,[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mmodel[39m[38;5;12m [39m[38;5;12mregistry,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mfull[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mlineage[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12msupport[39m[38;5;12m [39m[38;5;12myour[39m[38;5;12m [39m[38;5;12mML[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mworkflow[39m[38;5;12m [39m[38;5;12mfrom[39m[38;5;12m [39m[38;5;12mtraining[39m[38;5;12m [39m[38;5;12mstraight[39m[38;5;12m [39m[38;5;12mthrough[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mproduction.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mCometLLM[0m[38;5;12m (https://github.com/comet-ml/comet-llm)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mLog, track, visualize, and search your LLM prompts and chains in one easy-to-use, 100% open-source tool.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mSynthical[0m[38;5;12m (https://synthical.com)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAI-powered[39m[38;5;12m [39m[38;5;12mcollaborative[39m[38;5;12m [39m[38;5;12menvironment[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mresearch.[39m[38;5;12m [39m[38;5;12mFind[39m[38;5;12m [39m[38;5;12mrelevant[39m[38;5;12m [39m[38;5;12mpapers,[39m[38;5;12m [39m[38;5;12mcreate[39m[38;5;12m [39m[38;5;12mcollections[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mmanage[39m[38;5;12m [39m[38;5;12mbibliography,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12msummarize[39m[38;5;12m [39m[38;5;12mcontent[39m[38;5;12m [39m[38;5;12m—[39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mall[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mone[39m[38;5;12m [39m[38;5;12mplace[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mteeplot[0m[38;5;12m (https://github.com/mmore500/teeplot)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mWorkflow tool to automatically organize data visualization output[39m[38;5;12m [39m[38;5;239m│[39m
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-[38;2;255;187;0m[4mLiterature and Media[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;12mThis section includes some additional reading material, channels to watch, and talks to listen to.[39m
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-[38;2;255;187;0m[4mBooks[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;12m- [39m[38;5;14m[1mData Science From Scratch: First Principles with Python[0m[38;5;12m (https://www.amazon.com/Data-Science-Scratch-Principles-Python-dp-1492041130/dp/1492041130/ref=dp_ob_title_bk)[39m
-[38;5;12m- [39m[38;5;14m[1mArtificial Intelligence with Python - Tutorialspoint[0m[38;5;12m (https://www.tutorialspoint.com/artificial_intelligence_with_python/artificial_intelligence_with_python_tutorial.pdf)[39m
-[38;5;12m- [39m[38;5;14m[1mMachine Learning from Scratch[0m[38;5;12m (https://dafriedman97.github.io/mlbook/content/introduction.html)[39m
-[38;5;12m- [39m[38;5;14m[1mProbabilistic Machine Learning: An Introduction[0m[38;5;12m (https://probml.github.io/pml-book/book1.html)[39m
-[38;5;12m- [39m[38;5;14m[1mA Comprehensive Guide to Machine Learning[0m[38;5;12m (https://www.eecs189.org/static/resources/comprehensive-guide.pdf)[39m
-[38;5;12m- [39m[38;5;14m[1mHow to Lead in Data Science[0m[38;5;12m (https://www.manning.com/books/how-to-lead-in-data-science) - Early Access[39m
-[38;5;12m- [39m[38;5;14m[1mFighting Churn With Data[0m[38;5;12m (https://www.manning.com/books/fighting-churn-with-data)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science at Scale with Python and Dask[0m[38;5;12m (https://www.manning.com/books/data-science-with-python-and-dask)[39m
-[38;5;12m- [39m[38;5;14m[1mPython Data Science Handbook[0m[38;5;12m (https://jakevdp.github.io/PythonDataScienceHandbook/)[39m
-[38;5;12m- [39m[38;5;14m[1mThe Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists[0m[38;5;12m (https://www.thedatasciencehandbook.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mThink Like a Data Scientist[0m[38;5;12m (https://www.manning.com/books/think-like-a-data-scientist)[39m
-[38;5;12m- [39m[38;5;14m[1mIntroducing Data Science[0m[38;5;12m (https://www.manning.com/books/introducing-data-science)[39m
-[38;5;12m- [39m[38;5;14m[1mPractical Data Science with R[0m[38;5;12m (https://www.manning.com/books/practical-data-science-with-r)[39m
-[38;5;12m- [39m[38;5;14m[1mEveryday Data Science[0m[38;5;12m (https://www.amazon.com/dp/B08TZ1MT3W/ref=cm_sw_r_cp_apa_fabc_a0ceGbWECF9A8) & [39m[38;5;14m[1m(cheaper PDF version)[0m[38;5;12m (https://gum.co/everydaydata)[39m
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-[38;5;12m- [39m[38;5;14m[1mData Science Bookcamp[0m[38;5;12m (https://www.manning.com/books/data-science-bookcamp) Early access[39m
-[38;5;12m- [39m[38;5;14m[1mData Science Thinking: The Next Scientific, Technological and Economic Revolution[0m[38;5;12m (https://www.springer.com/gp/book/9783319950914)[39m
-[38;5;12m- [39m[38;5;14m[1mApplied Data Science: Lessons Learned for the Data-Driven Business[0m[38;5;12m (https://www.springer.com/gp/book/9783030118204)[39m
-[38;5;12m- [39m[38;5;14m[1mThe Data Science Handbook[0m[38;5;12m (https://www.amazon.com/Data-Science-Handbook-Field-Cady/dp/1119092949)[39m
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-[38;5;12m- [39m[38;5;14m[1mPandas in Action[0m[38;5;12m (https://www.manning.com/books/pandas-in-action) - Early access[39m
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-[38;5;12m- [39m[38;5;14m[1mAdvances in Evolutionary Algorithms[0m[38;5;12m (https://www.intechopen.com/books/advances_in_evolutionary_algorithms) - Free Download[39m
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-[38;5;12m- [39m[38;5;14m[1mR for Data Science[0m[38;5;12m (https://r4ds.had.co.nz/)[39m
-[38;5;12m- [39m[38;5;14m[1mBuild a Career in Data Science[0m[38;5;12m (https://www.manning.com/books/build-a-career-in-data-science)[39m
-[38;5;12m- [39m[38;5;14m[1mMachine Learning Bookcamp[0m[38;5;12m (https://mlbookcamp.com/) - Early access[39m
-[38;5;12m- [39m[38;5;14m[1mHands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition[0m[38;5;12m (https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)[39m
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-[38;5;12m- [39m[38;5;14m[1mPractical MLOps: How to Get Ready for Production Models[0m[38;5;12m (https://valohai.com/mlops-ebook/)[39m
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-[38;5;12m- [39m[38;5;14m[1mRegression, a Friendly guide[0m[38;5;12m (https://www.manning.com/books/regression-a-friendly-guide) - Early Access[39m
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-[38;5;12m- [39m[38;5;14m[1mData Science at the Command Line: Facing the Future with Time-Tested Tools[0m[38;5;12m (https://www.oreilly.com/library/view/data-science-at/9781491947845/)[39m
-[38;5;12m- [39m[38;5;14m[1mMachine Learning - CIn UFPE[0m[38;5;12m (https://www.cin.ufpe.br/~cavmj/Machine%20-%20Learning%20-%20Tom%20Mitchell.pdf)[39m
-[38;5;12m- [39m[38;5;14m[1mMachine Learning with Python - Tutorialspoint[0m[38;5;12m (https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_tutorial.pdf)[39m
-[38;5;12m- [39m[38;5;14m[1mDeep Learning[0m[38;5;12m (https://www.deeplearningbook.org/)[39m
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-[38;5;12m- [39m[38;5;14m[1mAn Introduction to Statistical Learning with Applications in R[0m[38;5;12m (https://www.statlearning.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mThe Elements of Statistical Learning: Data Mining, Inference, and Prediction[0m[38;5;12m (https://hastie.su.domains/ElemStatLearn/)[39m
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-[38;5;12m- [39m[38;5;14m[1mIntroduction to Machine Learning with Python[0m[38;5;12m (https://www.oreilly.com/library/view/introduction-to-machine/9781449369880/)[39m
-[38;5;12m- [39m[38;5;14m[1mArtificial Intelligence: Foundations of Computational Agents, 2nd Edition[0m[38;5;12m (https://artint.info/index.html) - Free HTML version[39m
-[38;5;12m- [39m[38;5;14m[1mThe Quest for Artificial Intelligence: A History of Ideas and Achievements[0m[38;5;12m (https://ai.stanford.edu/~nilsson/QAI/qai.pdf) - Free Download[39m
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-[38;5;12m- [39m[38;5;14m[1mRegular Expression Puzzles and AI Coding Assistants[0m[38;5;12m (https://www.manning.com/books/regular-expression-puzzles-and-ai-coding-assistants) by David Mertz[39m
-[38;5;12m- [39m[38;5;14m[1mDive into Deep Learning[0m[38;5;12m (https://d2l.ai/)[39m
-[38;5;12m- [39m[38;5;14m[1mData for All[0m[38;5;12m (https://www.manning.com/books/data-for-all)[39m
-[38;5;12m- [39m[38;5;14m[1mInterpretable Machine Learning: A Guide for Making Black Box Models Explainable[0m[38;5;12m (https://christophm.github.io/interpretable-ml-book/) - Free GitHub version[39m
-[38;5;12m- [39m[38;5;14m[1mFoundations of Data Science[0m[38;5;12m (https://www.cs.cornell.edu/jeh/book.pdf) Free Download [39m
-[38;5;12m- [39m[38;5;14m[1mComet for DataScience: Enhance your ability to manage and optimize the life cycle of your data science project[0m[38;5;12m (https://www.amazon.com/Comet-Data-Science-Enhance-optimize/dp/1801814430) [39m
-[38;5;12m- [39m[38;5;14m[1mSoftware Engineering for Data Scientists[0m[38;5;12m (https://www.manning.com/books/software-engineering-for-data-scientists) - Early Access[39m
-[38;5;12m- [39m[38;5;14m[1mJulia for Data Science[0m[38;5;12m (https://www.manning.com/books/julia-for-data-science) - Early Access[39m
-[38;5;12m- [39m[38;5;14m[1mAn Introduction to Statistical Learning[0m[38;5;12m (https://www.statlearning.com/) - Download Page[39m
-[38;5;12m- [39m[38;5;14m[1mMachine Learning For Absolute Beginners[0m[38;5;12m (https://www.amazon.in/Machine-Learning-Absolute-Beginners-Introduction-ebook/dp/B07335JNW1)[39m
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-[38;5;12m- [39m[38;5;14m[1mJournal of Data Science[0m[38;5;12m (https://jds-online.org/journal/JDS) - an international journal devoted to applications of statistical methods at large[39m
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-[38;5;12m- [39m[38;5;14m[1mMatthew Russell[0m[38;5;12m (https://miningthesocialweb.com/) - Mining The Social Web.[39m
-[38;5;12m- [39m[38;5;14m[1mGreg Reda[0m[38;5;12m (https://www.gregreda.com/) - Greg Reda Personal Blog[39m
-[38;5;12m- [39m[38;5;14m[1mKevin Davenport[0m[38;5;12m (https://kldavenport.com/) - Kevin Davenport Personal Blog[39m
-[38;5;12m- [39m[38;5;14m[1mJulia Evans[0m[38;5;12m (https://jvns.ca/) - Recurse Center alumna[39m
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-[38;5;12m- [39m[38;5;14m[1mPrash Chan[0m[38;5;12m (https://www.mdmgeek.com/) - Tech Blog on Master Data Management And Every Buzz Surrounding It[39m
-[38;5;12m- [39m[38;5;14m[1mClare Corthell[0m[38;5;12m (https://datasciencemasters.org/) - The Open Source Data Science Masters[39m
-[38;5;12m- [39m[38;5;14m[1mPaul Miller[0m[38;5;12m (https://cloudofdata.com/) Based in the UK and working globally, Cloud of Data's consultancy services help clients understand the implications of taking data and more to the Cloud.[39m
-[38;5;12m- [39m[38;5;14m[1mData Science London[0m[38;5;12m (https://datasciencelondon.org/) Data Science London is a non-profit organization dedicated to the free, open, dissemination of data science.[39m
-[38;5;12m We are the largest data science community in Europe.[39m
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-[38;5;12m- [39m[38;5;14m[1mDatawrangling[0m[38;5;12m (http://www.datawrangling.org) by Peter Skomoroch. MACHINE LEARNING, DATA MINING, AND MORE[39m
-[38;5;12m- [39m[38;5;14m[1mQuora Data Science[0m[38;5;12m (https://www.quora.com/topic/Data-Science) - Data Science Questions and Answers from experts[39m
-[38;5;12m- [39m[38;5;14m[1mSiah[0m[38;5;12m (https://openresearch.wordpress.com/) a PhD student at Berkeley[39m
-[38;5;12m- [39m[38;5;14m[1mLouis Dorard[0m[38;5;12m (https://www.ownml.co/blog/) a technology guy with a penchant for the web and for data, big and small[39m
-[38;5;12m- [39m[38;5;14m[1mMachine Learning Mastery[0m[38;5;12m (https://machinelearningmastery.com/) about helping professional programmers confidently apply machine learning algorithms to address complex problems.[39m
-[38;5;12m- [39m[38;5;14m[1mDaniel Forsyth[0m[38;5;12m (https://www.danielforsyth.me/) - Personal Blog[39m
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-[38;5;12m- [39m[38;5;14m[1mThe Practical Quant[0m[38;5;12m (https://practicalquant.blogspot.com/) Big data[39m
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-[38;5;12m- [39m[38;5;14m[1mSpenczar[0m[38;5;12m (https://spenczar.com/) a data scientist at _Twitch_. I handle the whole data pipeline, from tracking to model-building to reporting.[39m
-[38;5;12m- [39m[38;5;14m[1mKD Nuggets[0m[38;5;12m (https://www.kdnuggets.com/) Data Mining, Analytics, Big Data, Data, Science not a blog a portal[39m
-[38;5;12m- [39m[38;5;14m[1mMeta Brown[0m[38;5;12m (https://www.metabrown.com/blog/) - Personal Blog[39m
-[38;5;12m- [39m[38;5;14m[1mData Scientist[0m[38;5;12m (https://datascientists.net/) is building the data scientist culture.[39m
-[38;5;12m- [39m[38;5;14m[1mWhatSTheBigData[0m[38;5;12m (https://whatsthebigdata.com/) is some of, all of, or much more than the above and this blog explores its impact on information technology, the business world, government agencies, and our lives.[39m
-[38;5;12m- [39m[38;5;14m[1mTevfik Kosar[0m[38;5;12m (https://magnus-notitia.blogspot.com/) - Magnus Notitia[39m
-[38;5;12m- [39m[38;5;14m[1mNew Data Scientist[0m[38;5;12m (https://newdatascientist.blogspot.com/) How a Social Scientist Jumps into the World of Big Data[39m
-[38;5;12m- [39m[38;5;14m[1mHarvard Data Science[0m[38;5;12m (https://harvarddatascience.com/) - Thoughts on Statistical Computing and Visualization[39m
-[38;5;12m- [39m[38;5;14m[1mData Science 101[0m[38;5;12m (https://ryanswanstrom.com/datascience101/) - Learning To Be A Data Scientist[39m
-[38;5;12m- [39m[38;5;14m[1mKaggle Past Solutions[0m[38;5;12m (https://www.chioka.in/kaggle-competition-solutions/)[39m
-[38;5;12m- [39m[38;5;14m[1mDataScientistJourney[0m[38;5;12m (https://datascientistjourney.wordpress.com/category/data-science/)[39m
-[38;5;12m- [39m[38;5;14m[1mNYC Taxi Visualization Blog[0m[38;5;12m (https://chriswhong.github.io/nyctaxi/)[39m
-[38;5;12m- [39m[38;5;14m[1mLearning Lover[0m[38;5;12m (https://learninglover.com/blog/)[39m
-[38;5;12m- [39m[38;5;14m[1mDataists[0m[38;5;12m (https://www.dataists.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mData-Mania[0m[38;5;12m (https://www.data-mania.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mData-Magnum[0m[38;5;12m (https://data-magnum.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mP-value[0m[38;5;12m (https://www.p-value.info/) - Musings on data science, machine learning, and stats.[39m
-[38;5;12m- [39m[38;5;14m[1mdatascopeanalytics[0m[38;5;12m (https://datascopeanalytics.com/blog/)[39m
-[38;5;12m- [39m[38;5;14m[1mDigital transformation[0m[38;5;12m (https://tarrysingh.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mdatascientistjourney[0m[38;5;12m (https://datascientistjourney.wordpress.com/category/data-science/)[39m
-[38;5;12m- [39m[38;5;14m[1mData Mania Blog[0m[38;5;12m (https://www.data-mania.com/blog/) - [39m[38;5;14m[1mThe File Drawer[0m[38;5;12m (https://chris-said.io/) - Chris Said's science blog[39m
-[38;5;12m- [39m[38;5;14m[1mEmilio Ferrara's web page[0m[38;5;12m (https://www.emilio.ferrara.name/)[39m
-[38;5;12m- [39m[38;5;14m[1mDataNews[0m[38;5;12m (https://datanews.tumblr.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mReddit TextMining[0m[38;5;12m (https://www.reddit.com/r/textdatamining/)[39m
-[38;5;12m- [39m[38;5;14m[1mPeriscopic[0m[38;5;12m (https://periscopic.com/#!/news)[39m
-[38;5;12m- [39m[38;5;14m[1mHilary Parker[0m[38;5;12m (https://hilaryparker.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mData Stories[0m[38;5;12m (https://datastori.es/)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science Lab[0m[38;5;12m (https://datasciencelab.wordpress.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mMeaning of[0m[38;5;12m (https://www.kennybastani.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mAdventures in Data Land[0m[38;5;12m (https://blog.smola.org)[39m
-[38;5;12m- [39m[38;5;14m[1mDATA MINERS BLOG[0m[38;5;12m (https://blog.data-miners.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mDataclysm[0m[38;5;12m (https://theblog.okcupid.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mFlowingData[0m[38;5;12m (https://flowingdata.com/) - Visualization and Statistics[39m
-[38;5;12m- [39m[38;5;14m[1mCalculated Risk[0m[38;5;12m (https://www.calculatedriskblog.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mO'reilly Learning Blog[0m[38;5;12m (https://www.oreilly.com/content/topics/oreilly-learning/)[39m
-[38;5;12m- [39m[38;5;14m[1mDominodatalab[0m[38;5;12m (https://blog.dominodatalab.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mi am trask[0m[38;5;12m (https://iamtrask.github.io/) - A Machine Learning Craftsmanship Blog[39m
-[38;5;12m- [39m[38;5;14m[1mVademecum of Practical Data Science[0m[38;5;12m (https://datasciencevademecum.wordpress.com/) - Handbook and recipes for data-driven solutions of real-world problems[39m
-[38;5;12m- [39m[38;5;14m[1mDataconomy[0m[38;5;12m (https://dataconomy.com/) - A blog on the newly emerging data economy[39m
-[38;5;12m- [39m[38;5;14m[1mSpringboard[0m[38;5;12m (https://www.springboard.com/blog/) - A blog with resources for data science learners[39m
-[38;5;12m- [39m[38;5;14m[1mAnalytics Vidhya[0m[38;5;12m (https://www.analyticsvidhya.com/) - A full-fledged website about data science and analytics study material.[39m
-[38;5;12m- [39m[38;5;14m[1mOccam's Razor[0m[38;5;12m (https://www.kaushik.net/avinash/) - Focused on Web Analytics.[39m
-[38;5;12m- [39m[38;5;14m[1mData School[0m[38;5;12m (https://www.dataschool.io/) - Data science tutorials for beginners![39m
-[38;5;12m- [39m[38;5;14m[1mColah's Blog[0m[38;5;12m (https://colah.github.io) - Blog for understanding Neural Networks![39m
-[38;5;12m- [39m[38;5;14m[1mSebastian's Blog[0m[38;5;12m (https://ruder.io/#open) - Blog for NLP and transfer learning![39m
-[38;5;12m- [39m[38;5;14m[1mDistill[0m[38;5;12m (https://distill.pub) - Dedicated to clear explanations of machine learning![39m
-[38;5;12m- [39m[38;5;14m[1mChris Albon's Website[0m[38;5;12m (https://chrisalbon.com/) - Data Science and AI notes[39m
-[38;5;12m- [39m[38;5;14m[1mAndrew Carr[0m[38;5;12m (https://andrewnc.github.io/blog/blog.html) - Data Science with Esoteric programming languages[39m
-[38;5;12m- [39m[38;5;14m[1mfloydhub[0m[38;5;12m (https://blog.floydhub.com/introduction-to-genetic-algorithms/) - Blog for Evolutionary Algorithms[39m
-[38;5;12m- [39m[38;5;14m[1mJingles[0m[38;5;12m (https://jinglescode.github.io/) - Review and extract key concepts from academic papers[39m
-[38;5;12m- [39m[38;5;14m[1mnbshare[0m[38;5;12m (https://www.nbshare.io/notebooks/data-science/) - Data Science notebooks[39m
-[38;5;12m- [39m[38;5;14m[1mDeep and Shallow[0m[38;5;12m (https://deep-and-shallow.com/) - All things Deep and Shallow in Data Science[39m
-[38;5;12m- [39m[38;5;14m[1mLoic Tetrel[0m[38;5;12m (https://ltetrel.github.io/) - Data science blog[39m
-[38;5;12m- [39m[38;5;14m[1mChip Huyen's Blog[0m[38;5;12m (https://huyenchip.com/blog/) - ML Engineering, MLOps, and the use of ML in startups[39m
-[38;5;12m- [39m[38;5;14m[1mMaria Khalusova[0m[38;5;12m (https://www.mariakhalusova.com/) - Data science blog[39m
-[38;5;12m- [39m[38;5;14m[1mAditi Rastogi[0m[38;5;12m (https://medium.com/@aditi2507rastogi) - ML,DL,Data Science blog[39m
-[38;5;12m- [39m[38;5;14m[1mSantiago Basulto[0m[38;5;12m (https://medium.com/@santiagobasulto) - Data Science with Python[39m
-[38;5;12m- [39m[38;5;14m[1mAkhil Soni[0m[38;5;12m (https://medium.com/@akhil0435) - ML, DL and Data Science[39m
-[38;5;12m- [39m[38;5;14m[1mAkhil Soni[0m[38;5;12m (https://akhilworld.hashnode.dev/) - ML, DL and Data Science [39m
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-[38;2;255;187;0m[4mPresentations[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;12m- [39m[38;5;14m[1mHow to Become a Data Scientist[0m[38;5;12m (https://www.slideshare.net/ryanorban/how-to-become-a-data-scientist)[39m
-[38;5;12m- [39m[38;5;14m[1mIntroduction to Data Science[0m[38;5;12m (https://www.slideshare.net/NikoVuokko/introduction-to-data-science-25391618)[39m
-[38;5;12m- [39m[38;5;14m[1mIntro to Data Science for Enterprise Big Data[0m[38;5;12m (https://www.slideshare.net/pacoid/intro-to-data-science-for-enterprise-big-data)[39m
-[38;5;12m- [39m[38;5;14m[1mHow to Interview a Data Scientist[0m[38;5;12m (https://www.slideshare.net/dtunkelang/how-to-interview-a-data-scientist)[39m
-[38;5;12m- [39m[38;5;14m[1mHow to Share Data with a Statistician[0m[38;5;12m (https://github.com/jtleek/datasharing)[39m
-[38;5;12m- [39m[38;5;14m[1mThe Science of a Great Career in Data Science[0m[38;5;12m (https://www.slideshare.net/katemats/the-science-of-a-great-career-in-data-science)[39m
-[38;5;12m- [39m[38;5;14m[1mWhat Does a Data Scientist Do?[0m[38;5;12m (https://www.slideshare.net/datasciencelondon/big-data-sorry-data-science-what-does-a-data-scientist-do)[39m
-[38;5;12m- [39m[38;5;14m[1mBuilding Data Start-Ups: Fast, Big, and Focused[0m[38;5;12m (https://www.slideshare.net/medriscoll/driscoll-strata-buildingdatastartups25may2011clean)[39m
-[38;5;12m- [39m[38;5;14m[1mHow to win data science competitions with Deep Learning[0m[38;5;12m (https://www.slideshare.net/0xdata/how-to-win-data-science-competitions-with-deep-learning)[39m
-[38;5;12m- [39m[38;5;14m[1mFull-Stack Data Scientist[0m[38;5;12m (https://www.slideshare.net/AlexeyGrigorev/fullstack-data-scientist)[39m
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-[38;2;255;187;0m[4mPodcasts[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;12m- [39m[38;5;14m[1mAI at Home[0m[38;5;12m (https://podcasts.apple.com/us/podcast/data-science-at-home/id1069871378)[39m
-[38;5;12m- [39m[38;5;14m[1mAI Today[0m[38;5;12m (https://www.cognilytica.com/aitoday/)[39m
-[38;5;12m- [39m[38;5;14m[1mAdversarial Learning[0m[38;5;12m (https://adversariallearning.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mBecoming a Data Scientist[0m[38;5;12m (https://www.becomingadatascientist.com/category/podcast/)[39m
-[38;5;12m- [39m[38;5;14m[1mChai time Data Science[0m[38;5;12m (https://www.youtube.com/playlist?list=PLLvvXm0q8zUbiNdoIazGzlENMXvZ9bd3x)[39m
-[38;5;12m- [39m[38;5;14m[1mData Crunch[0m[38;5;12m (https://datacrunchcorp.com/data-crunch-podcast/)[39m
-[38;5;12m- [39m[38;5;14m[1mData Engineering Podcast[0m[38;5;12m (https://www.dataengineeringpodcast.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science at Home[0m[38;5;12m (https://datascienceathome.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science Mixer[0m[38;5;12m (https://community.alteryx.com/t5/Data-Science-Mixer/bg-p/mixer)[39m
-[38;5;12m- [39m[38;5;14m[1mData Skeptic[0m[38;5;12m (https://dataskeptic.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mData Stories[0m[38;5;12m (https://datastori.es/)[39m
-[38;5;12m- [39m[38;5;14m[1mDatacast[0m[38;5;12m (https://jameskle.com/writes/category/Datacast)[39m
-[38;5;12m- [39m[38;5;14m[1mDataFramed[0m[38;5;12m (https://www.datacamp.com/community/podcast)[39m
-[38;5;12m- [39m[38;5;14m[1mDataTalks.Club[0m[38;5;12m (https://anchor.fm/datatalksclub)[39m
-[38;5;12m- [39m[38;5;14m[1mGradient Dissent[0m[38;5;12m (https://wandb.ai/fully-connected/gradient-dissent)[39m
-[38;5;12m- [39m[38;5;14m[1mLearning Machines 101[0m[38;5;12m (https://www.learningmachines101.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mLet's Data (Brazil)[0m[38;5;12m (https://www.youtube.com/playlist?list=PLn_z5E4dh_Lj5eogejMxfOiNX3nOhmhmM)[39m
-[38;5;12m- [39m[38;5;14m[1mLinear Digressions[0m[38;5;12m (https://lineardigressions.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mNot So Standard Deviations[0m[38;5;12m (https://nssdeviations.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mO'Reilly Data Show Podcast[0m[38;5;12m (https://www.oreilly.com/radar/topics/oreilly-data-show-podcast/)[39m
-[38;5;12m- [39m[38;5;14m[1mPartially Derivative[0m[38;5;12m (https://partiallyderivative.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mSuperdatascience[0m[38;5;12m (https://www.superdatascience.com/podcast/)[39m
-[38;5;12m- [39m[38;5;14m[1mThe Data Engineering Show[0m[38;5;12m (https://www.dataengineeringshow.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mThe Radical AI Podcast[0m[38;5;12m (https://www.radicalai.org/)[39m
-[38;5;12m- [39m[38;5;14m[1mThe Robot Brains Podcast[0m[38;5;12m (https://www.therobotbrains.ai/)[39m
-[38;5;12m- [39m[38;5;14m[1mWhat's The Point[0m[38;5;12m (https://fivethirtyeight.com/tag/whats-the-point/)[39m
-[38;5;12m- [39m[38;5;14m[1mHow AI Built This[0m[38;5;12m (https://how-ai-built-this.captivate.fm/)[39m
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-[38;2;255;187;0m[4mYouTube Videos & Channels[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;12m- [39m[38;5;14m[1mWhat is machine learning?[0m[38;5;12m (https://www.youtube.com/watch?v=WXHM_i-fgGo)[39m
-[38;5;12m- [39m[38;5;14m[1mAndrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning[0m[38;5;12m (https://www.youtube.com/watch?v=n1ViNeWhC24)[39m
-[38;5;12m- [39m[38;5;14m[1mData36 - Data Science for Beginners by Tomi Mester[0m[38;5;12m (https://www.youtube.com/c/TomiMesterData36comDataScienceForBeginners)[39m
-[38;5;12m- [39m[38;5;14m[1mDeep Learning: Intelligence from Big Data[0m[38;5;12m (https://www.youtube.com/watch?v=czLI3oLDe8M)[39m
-[38;5;12m- [39m[38;5;14m[1mInterview with Google's AI and Deep Learning 'Godfather' Geoffrey Hinton[0m[38;5;12m (https://www.youtube.com/watch?v=1Wp3IIpssEc)[39m
-[38;5;12m- [39m[38;5;14m[1mIntroduction to Deep Learning with Python[0m[38;5;12m (https://www.youtube.com/watch?v=S75EdAcXHKk)[39m
-[38;5;12m- [39m[38;5;14m[1mWhat is machine learning, and how does it work?[0m[38;5;12m (https://www.youtube.com/watch?v=elojMnjn4kk)[39m
-[38;5;12m- [39m[38;5;14m[1mData School[0m[38;5;12m (https://www.youtube.com/channel/UCnVzApLJE2ljPZSeQylSEyg) - Data Science Education[39m
-[38;5;12m- [39m[38;5;14m[1mNeural Nets for Newbies by Melanie Warrick (May 2015)[0m[38;5;12m (https://www.youtube.com/watch?v=Cu6A96TUy_o)[39m
-[38;5;12m- [39m[38;5;14m[1mNeural Networks video series by Hugo Larochelle[0m[38;5;12m (https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)[39m
-[38;5;12m- [39m[38;5;14m[1mGoogle DeepMind co-founder Shane Legg - Machine Super Intelligence[0m[38;5;12m (https://www.youtube.com/watch?v=evNCyRL3DOU)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science Primer[0m[38;5;12m (https://www.youtube.com/watch?v=cHzvYxBN9Ls&list=PLPqVjP3T4RIRsjaW07zoGzH-Z4dBACpxY)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science with Genetic Algorithms[0m[38;5;12m (https://www.youtube.com/watch?v=lpD38NxTOnk)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science for Beginners[0m[38;5;12m (https://www.youtube.com/playlist?list=PL2zq7klxX5ATMsmyRazei7ZXkP1GHt-vs)[39m
-[38;5;12m- [39m[38;5;14m[1mDataTalks.Club[0m[38;5;12m (https://www.youtube.com/channel/UCDvErgK0j5ur3aLgn6U-LqQ)[39m
-[38;5;12m- [39m[38;5;14m[1mMildlyoverfitted - Tutorials on intermediate ML/DL topics[0m[38;5;12m (https://www.youtube.com/channel/UCYBSjwkGTK06NnDnFsOcR7g)[39m
-[38;5;12m- [39m[38;5;14m[1mmlops.community - Interviews of industry experts about production ML[0m[38;5;12m (https://www.youtube.com/channel/UCYBSjwkGTK06NnDnFsOcR7g)[39m
-[38;5;12m- [39m[38;5;14m[1mML Street Talk - Unabashedly technical and non-commercial, so you will hear no annoying pitches.[0m[38;5;12m (https://www.youtube.com/c/machinelearningstreettalk)[39m
-[38;5;12m- [39m[38;5;14m[1mNeural networks by 3Blue1Brown [0m[38;5;12m (https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)[39m
-[38;5;12m- [39m[38;5;14m[1mNeural networks from scratch by Sentdex[0m[38;5;12m (https://www.youtube.com/playlist?list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3)[39m
-[38;5;12m- [39m[38;5;14m[1mManning Publications YouTube channel[0m[38;5;12m (https://www.youtube.com/c/ManningPublications/featured)[39m
-[38;5;12m- [39m[38;5;14m[1mAsk Dr Chong: How to Lead in Data Science - Part 1[0m[38;5;12m (https://youtu.be/JYuQZii5o58)[39m
-[38;5;12m- [39m[38;5;14m[1mAsk Dr Chong: How to Lead in Data Science - Part 2[0m[38;5;12m (https://youtu.be/SzqIXV-O-ko)[39m
-[38;5;12m- [39m[38;5;14m[1mAsk Dr Chong: How to Lead in Data Science - Part 3[0m[38;5;12m (https://youtu.be/Ogwm7k_smTA)[39m
-[38;5;12m- [39m[38;5;14m[1mAsk Dr Chong: How to Lead in Data Science - Part 4[0m[38;5;12m (https://youtu.be/a9usjdzTxTU)[39m
-[38;5;12m- [39m[38;5;14m[1mAsk Dr Chong: How to Lead in Data Science - Part 5[0m[38;5;12m (https://youtu.be/MYdQq-F3Ws0)[39m
-[38;5;12m- [39m[38;5;14m[1mAsk Dr Chong: How to Lead in Data Science - Part 6[0m[38;5;12m (https://youtu.be/LOOt4OVC3hY)[39m
-[38;5;12m- [39m[38;5;14m[1mRegression Models: Applying simple Poisson regression[0m[38;5;12m (https://www.youtube.com/watch?v=9Hk8K8jhiOo)[39m
-[38;5;12m- [39m[38;5;14m[1mDeep Learning Architectures[0m[38;5;12m (https://www.youtube.com/playlist?list=PLv8Cp2NvcY8DpVcsmOT71kymgMmcr59Mf)[39m
-[38;5;12m- [39m[38;5;14m[1mTime Series Modelling and Analysis[0m[38;5;12m (https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK)[39m
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-[38;2;255;187;0m[4mSocialize[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;12mBelow are some Social Media links. Connect with other data scientists![39m
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-[38;5;12m- [39m[38;5;14m[1mFacebook Accounts[0m[38;5;12m (#facebook-accounts)[39m
-[38;5;12m- [39m[38;5;14m[1mTwitter Accounts[0m[38;5;12m (#twitter-accounts)[39m
-[38;5;12m- [39m[38;5;14m[1mTelegram Channels[0m[38;5;12m (#telegram-channels)[39m
-[38;5;12m- [39m[38;5;14m[1mSlack Communities[0m[38;5;12m (#slack-communities)[39m
-[38;5;12m- [39m[38;5;14m[1mGitHub Groups[0m[38;5;12m (#github-groups)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science Competitions[0m[38;5;12m (#data-science-competitions)[39m
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-[38;2;255;187;0m[4mFacebook Accounts[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;12m- [39m[38;5;14m[1mData[0m[38;5;12m (https://www.facebook.com/data)[39m
-[38;5;12m- [39m[38;5;14m[1mBig Data Scientist[0m[38;5;12m (https://www.facebook.com/Bigdatascientist)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science Day[0m[38;5;12m (https://www.facebook.com/datascienceday/)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science Academy[0m[38;5;12m (https://www.facebook.com/nycdatascience)[39m
-[38;5;12m- [39m[38;5;14m[1mFacebook Data Science Page[0m[38;5;12m (https://www.facebook.com/pages/Data-science/431299473579193?ref=br_rs)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science London[0m[38;5;12m (https://www.facebook.com/pages/Data-Science-London/226174337471513)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science Technology and Corporation[0m[38;5;12m (https://www.facebook.com/DataScienceTechnologyCorporation?ref=br_rs)[39m
-[38;5;12m- [39m[38;5;14m[1mData Science - Closed Group[0m[38;5;12m (https://www.facebook.com/groups/1394010454157077/?ref=br_rs)[39m
-[38;5;12m- [39m[38;5;14m[1mCenter for Data Science[0m[38;5;12m (https://www.facebook.com/centerdatasciences?ref=br_rs)[39m
-[38;5;12m- [39m[38;5;14m[1mBig data hadoop NOSQL Hive Hbase[0m[38;5;12m (https://www.facebook.com/groups/bigdatahadoop/)[39m
-[38;5;12m- [39m[38;5;14m[1mAnalytics, Data Mining, Predictive Modeling, Artificial Intelligence[0m[38;5;12m (https://www.facebook.com/groups/data.analytics/)[39m
-[38;5;12m- [39m[38;5;14m[1mBig Data Analytics using R[0m[38;5;12m (https://www.facebook.com/groups/434352233255448/)[39m
-[38;5;12m- [39m[38;5;14m[1mBig Data Analytics with R and Hadoop[0m[38;5;12m (https://www.facebook.com/groups/rhadoop/)[39m
-[38;5;12m- [39m[38;5;14m[1mBig Data Learnings[0m[38;5;12m (https://www.facebook.com/groups/bigdatalearnings/)[39m
-[38;5;12m- [39m[38;5;14m[1mBig Data, Data Science, Data Mining & Statistics[0m[38;5;12m (https://www.facebook.com/groups/bigdatastatistics/)[39m
-[38;5;12m- [39m[38;5;14m[1mBigData/Hadoop Expert[0m[38;5;12m (https://www.facebook.com/groups/BigDataExpert/)[39m
-[38;5;12m- [39m[38;5;14m[1mData Mining / Machine Learning / AI[0m[38;5;12m (https://www.facebook.com/groups/machinelearningforum/)[39m
-[38;5;12m- [39m[38;5;14m[1mData Mining/Big Data - Social Network Ana[0m[38;5;12m (https://www.facebook.com/groups/dataminingsocialnetworks/)[39m
-[38;5;12m- [39m[38;5;14m[1mVademecum of Practical Data Science[0m[38;5;12m (https://www.facebook.com/datasciencevademecum)[39m
-[38;5;12m- [39m[38;5;14m[1mVeri Bilimi Istanbul[0m[38;5;12m (https://www.facebook.com/groups/veribilimiistanbul/)[39m
-[38;5;12m- [39m[38;5;14m[1mThe Data Science Blog[0m[38;5;12m (https://www.facebook.com/theDataScienceBlog/)[39m
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-[38;2;255;187;0m[4mTwitter Accounts[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;239m│[39m[38;5;12m [39m[38;5;12mTwitter[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m [39m[38;5;12mDescription[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m├[39m[38;5;239m──────────────────────────────────────────────────────────[39m[38;5;239m┼[39m[38;5;239m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────[39m[38;5;239m┤[39m
-[38;5;239m│[39m[38;5;14m[1mBig Data Combine[0m[38;5;12m (https://twitter.com/BigDataCombine)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mRapid-fire, live tryouts for data scientists seeking to monetize their models as trading strategies[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12mBig Data Mania[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Viz Wiz, Data Journalist, Growth Hacker, Author of Data Science for Dummies (2015)[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mBig Data Science[0m[38;5;12m (https://twitter.com/analyticbridge)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mBig Data, Data Science, Predictive Modeling, Business Analytics, Hadoop, Decision and Operations Research.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12mCharlie Greenbacker[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mDirector of Data Science at @ExploreAltamira[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mChris Said[0m[38;5;12m (https://twitter.com/Chris_Said)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData scientist at Twitter[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mClare Corthell[0m[38;5;12m (https://twitter.com/clarecorthell)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mDev, Design, Data Science @mattermark #hackerei[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mDADI Charles-Abner[0m[38;5;12m (https://twitter.com/DadiCharles)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m#datascientist @Ekimetrics. , #machinelearning #dataviz #DynamicCharts #Hadoop #R #Python #NLP #Bitcoin #dataenthousiast[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mData Science Central[0m[38;5;12m (https://twitter.com/DataScienceCtrl)[39m[38;5;239m│[39m[38;5;12mData Science Central is the industry's single resource for Big Data practitioners.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mData Science London[0m[38;5;12m (https://twitter.com/ds_ldn)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Science. Big Data. Data Hacks. Data Junkies. Data Startups. Open Data[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mData Science Renee[0m[38;5;12m (https://twitter.com/BecomingDataSci)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mDocumenting my path from SQL Data Analyst pursuing an Engineering Master's Degree to Data Scientist[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mData Science Report[0m[38;5;12m (https://twitter.com/TedOBrien93)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mMission is to help guide & advance careers in Data Science & Analytics[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mData Science Tips[0m[38;5;12m (https://twitter.com/datasciencetips)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mTips and Tricks for Data Scientists around the world! #datascience #bigdata[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mData Vizzard[0m[38;5;12m (https://twitter.com/DataVisualizati)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mDataViz, Security, Military[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mDataScienceX[0m[38;5;12m (https://twitter.com/DataScienceX)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12mdeeplearning4j[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mDJ Patil[0m[38;5;12m (https://twitter.com/dpatil)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mWhite House Data Chief, VP @ RelateIQ.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mDomino Data Lab[0m[38;5;12m (https://twitter.com/DominoDataLab)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mDrew Conway[0m[38;5;12m (https://twitter.com/drewconway)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData nerd, hacker, student of conflict.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12mEmilio Ferrara[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m#Networks, #MachineLearning and #DataScience. I work on #Social Media. Postdoc at @IndianaUniv[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mErin Bartolo[0m[38;5;12m (https://twitter.com/erinbartolo)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mRunning with #BigData--enjoying a love/hate relationship with its hype. @iSchoolSU #DataScience Program Mgr.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mGreg Reda[0m[38;5;12m (https://twitter.com/gjreda)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mWorking @ _GrubHub_ about data and pandas[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mGregory Piatetsky[0m[38;5;12m (https://twitter.com/kdnuggets)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mKDnuggets President, Analytics/Big Data/Data Mining/Data Science expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, part-time philosopher.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mHadley Wickham[0m[38;5;12m (https://twitter.com/hadleywickham)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mChief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mHakan Kardas[0m[38;5;12m (https://twitter.com/hakan_kardes)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Scientist[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mHilary Mason[0m[38;5;12m (https://twitter.com/hmason)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Scientist in Residence at @accel.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mJeff Hammerbacher[0m[38;5;12m (https://twitter.com/hackingdata)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mReTweeting about data science[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mJohn Myles White[0m[38;5;12m (https://twitter.com/johnmyleswhite)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mScientist at Facebook and Julia developer. Author of Machine Learning for Hackers and Bandit Algorithms for Website Optimization. Tweets reflect my views only.[39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mJuan Miguel Lavista[0m[38;5;12m (https://twitter.com/BDataScientist)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mPrincipal Data Scientist @ Microsoft Data Science Team[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mJulia Evans[0m[38;5;12m (https://twitter.com/b0rk)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mHacker - Pandas - Data Analyze[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mKenneth Cukier[0m[38;5;12m (https://twitter.com/kncukier)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mThe Economist's Data Editor and co-author of Big Data (http://www.big-data-book.com/).[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12mKevin Davenport[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mOrganizer of https://www.meetup.com/San-Diego-Data-Science-R-Users-Group/[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mKevin Markham[0m[38;5;12m (https://twitter.com/justmarkham)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData science instructor, and founder of [39m[38;5;14m[1mData School[0m[38;5;12m (https://www.dataschool.io/)[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mKim Rees[0m[38;5;12m (https://twitter.com/krees)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mInteractive data visualization and tools. Data flaneur.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mKirk Borne[0m[38;5;12m (https://twitter.com/KirkDBorne)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mDataScientist, PhD Astrophysicist, Top #BigData Influencer.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12mLinda Regber[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData storyteller, visualizations.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mLuis Rei[0m[38;5;12m (https://twitter.com/lmrei)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mPhD Student. Programming, Mobile, Web. Artificial Intelligence, Intelligent Robotics Machine Learning, Data Mining, Natural Language Processing, Data Science.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12mMark Stevenson[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Analytics Recruitment Specialist at Salt (@SaltJobs) Analytics - Insight - Big Data - Data science[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mMatt Harrison[0m[38;5;12m (https://twitter.com/__mharrison__)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mOpinions of full-stack Python guy, author, instructor, currently playing Data Scientist. Occasional fathering, husbanding, organic gardening.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mMatthew Russell[0m[38;5;12m (https://twitter.com/ptwobrussell)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mMining the Social Web.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mMert Nuhoğlu[0m[38;5;12m (https://twitter.com/mertnuhoglu)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Scientist at BizQualify, Developer[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mMonica Rogati[0m[38;5;12m (https://twitter.com/mrogati)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData @ Jawbone. Turned data into stories & products at LinkedIn. Text mining, applied machine learning, recommender systems. Ex-gamer, ex-machine coder; namer.[39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mNoah Iliinsky[0m[38;5;12m (https://twitter.com/noahi)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mVisualization & interaction designer. Practical cyclist. Author of vis books: https://www.oreilly.com/pub/au/4419[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mPaul Miller[0m[38;5;12m (https://twitter.com/PaulMiller)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mCloud Computing/ Big Data/ Open Data Analyst & Consultant. Writer, Speaker & Moderator. Gigaom Research Analyst.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mPeter Skomoroch[0m[38;5;12m (https://twitter.com/peteskomoroch)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mCreating intelligent systems to automate tasks & improve decisions. Entrepreneur, ex-Principal Data Scientist @LinkedIn. Machine Learning, ProductRei, Networks[39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mPrash Chan[0m[38;5;12m (https://twitter.com/MDMGeek)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mSolution Architect @ IBM, Master Data Management, Data Quality & Data Governance Blogger. Data Science, Hadoop, Big Data & Cloud.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mQuora Data Science[0m[38;5;12m (https://twitter.com/q_datascience)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mQuora's data science topic[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mR-Bloggers[0m[38;5;12m (https://twitter.com/Rbloggers)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mTweet blog posts from the R blogosphere, data science conferences, and (!) open jobs for data scientists.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mRand Hindi[0m[38;5;12m (https://twitter.com/randhindi)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mRandy Olson[0m[38;5;12m (https://twitter.com/randal_olson)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mComputer scientist researching artificial intelligence. Data tinkerer. Community leader for @DataIsBeautiful. #OpenScience advocate.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mRecep Erol[0m[38;5;12m (https://twitter.com/EROLRecep)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Science geek @ UALR[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mRyan Orban[0m[38;5;12m (https://twitter.com/ryanorban)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData scientist, genetic origamist, hardware aficionado[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mSean J. Taylor[0m[38;5;12m (https://twitter.com/seanjtaylor)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mSocial Scientist. Hacker. Facebook Data Science Team. Keywords: Experiments, Causal Inference, Statistics, Machine Learning, Economics.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mSilvia K. Spiva[0m[38;5;12m (https://twitter.com/silviakspiva)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m#DataScience at Cisco[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mHarsh B. Gupta[0m[38;5;12m (https://twitter.com/harshbg)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Scientist at BBVA Compass[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mSpencer Nelson[0m[38;5;12m (https://twitter.com/spenczar_n)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData nerd[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mTalha Oz[0m[38;5;12m (https://twitter.com/tozCSS)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mEnjoys ABM, SNA, DM, ML, NLP, HI, Python, Java. Top percentile Kaggler/data scientist[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mTasos Skarlatidis[0m[38;5;12m (https://twitter.com/anskarl)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mComplex Event Processing, Big Data, Artificial Intelligence and Machine Learning. Passionate about programming and open-source.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mTerry Timko[0m[38;5;12m (https://twitter.com/Terry_Timko)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mInfoGov; Bigdata; Data as a Service; Data Science; Open, Social & Business Data Convergence[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mTony Baer[0m[38;5;12m (https://twitter.com/TonyBaer)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mIT analyst with Ovum covering Big Data & data management with some systems engineering thrown in.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mTony Ojeda[0m[38;5;12m (https://twitter.com/tonyojeda3)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Scientist , Author , Entrepreneur. Co-founder @DataCommunityDC. Founder @DistrictDataLab. #DataScience #BigData #DataDC[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mVamshi Ambati[0m[38;5;12m (https://twitter.com/vambati)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Science @ PayPal. #NLP, #machinelearning; PhD, Carnegie Mellon alumni (Blog: https://allthingsds.wordpress.com )[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mWes McKinney[0m[38;5;12m (https://twitter.com/wesmckinn)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mPandas (Python Data Analysis library).[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mWileyEd[0m[38;5;12m (https://twitter.com/WileyEd)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mSenior Manager - @Seagate Big Data Analytics @McKinsey Alum #BigData + #Analytics Evangelist #Hadoop, #Cloud, #Digital, & #R Enthusiast[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mWNYC Data News Team[0m[38;5;12m (https://twitter.com/datanews)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mThe data news crew at @WNYC. Practicing data-driven journalism, making it visual, and showing our work.[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mAlexey Grigorev[0m[38;5;12m (https://twitter.com/Al_Grigor)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData science author[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mİlker Arslan[0m[38;5;12m (https://twitter.com/ilkerarslan_35)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData science author. Shares mostly about Julia programming[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;14m[1mINEVITABLE[0m[38;5;12m (https://twitter.com/WeAreInevitable)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAI & Data Science Start-up Company based in England, UK[39m[38;5;12m [39m[38;5;239m│[39m
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-[38;5;12mapplications[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mformer.[39m
-[38;5;12m- [39m[38;5;14m[1mLoss function porn[0m[38;5;12m (https://t.me/loss_function_porn) — Beautiful posts on DS/ML theme with video or graphic visualization.[39m
-[38;5;12m- [39m[38;5;14m[1mMachinelearning[0m[38;5;12m (https://t.me/ai_machinelearning_big_data) – Daily ML news.[39m
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-[38;5;12m- [39m[38;5;14m[1mWomen Who Code - Data Science[0m[38;5;12m (https://www.womenwhocode.com/datascience)[39m
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-[38;2;255;187;0m[4mGitHub Groups[0m
-[38;5;12m- [39m[38;5;14m[1mBerkeley Institute for Data Science[0m[38;5;12m (https://github.com/BIDS)[39m
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-[38;2;255;187;0m[4mData Science Competitions[0m
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-[38;5;12mSome data mining competition platforms[39m
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-[38;5;12m- [39m[38;5;14m[1mKaggle[0m[38;5;12m (https://www.kaggle.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mDrivenData[0m[38;5;12m (https://www.drivendata.org/)[39m
-[38;5;12m- [39m[38;5;14m[1mAnalytics Vidhya[0m[38;5;12m (https://datahack.analyticsvidhya.com/)[39m
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-[38;5;12m- [39m[38;5;14m[1mMicroprediction[0m[38;5;12m (https://www.microprediction.com/python-1)[39m
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-[38;5;12m- [39m[38;5;14m[1mInfographic[0m[38;5;12m (#infographics)[39m
-[38;5;12m- [39m[38;5;14m[1mDatasets[0m[38;5;12m (#datasets)[39m
-[38;5;12m- [39m[38;5;14m[1mComics[0m[38;5;12m (#comics)[39m
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-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;239m├[39m[38;5;239m──────────────────────────────────────────────────────────────────────────────────────────────────[39m[38;5;239m┼[39m[38;5;239m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────[39m[38;5;239m┤[39m
-[38;5;239m│[39m[38;5;12m(https://i.imgur.com/0OoLaa5.png)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;14m[1mKey differences of a data scientist vs. data engineer[0m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m (https://searchbusinessanalytics.techtarget.com/feature/Key-differences-of-a-data-scientist-vs-data-engineer)[39m[38;5;12m [39m[38;5;239m│[39m
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-[38;5;239m│[39m[38;5;12m(https://i.imgur.com/FxsL3b8.png)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mMindmap on required skills ([39m[38;5;14m[1mimg[0m[38;5;12m (https://i.imgur.com/FxsL3b8.png))[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m(https://nirvacana.com/thoughts/wp-content/uploads/2013/07/RoadToDataScientist1.png)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mSwami Chandrasekaran made a [39m[38;5;14m[1mCurriculum via Metro map[0m[38;5;12m (http://nirvacana.com/thoughts/2013/07/08/becoming-a-data-scientist/).[39m[38;5;12m [39m[38;5;239m│[39m
-[38;5;239m│[39m[38;5;12m(https://i.imgur.com/4ZBBvb0.png)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mby [39m[38;5;14m[1m@kzawadz[0m[38;5;12m (https://twitter.com/kzawadz) via [39m[38;5;14m[1mtwitter[0m[38;5;12m (https://twitter.com/MktngDistillery/status/538671811991715840)[39m[38;5;12m [39m[38;5;239m│[39m
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-[38;5;239m│[39m[38;5;12m(https://i.imgur.com/0TydZ4M.png)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Science Wars: R vs Python[39m[38;5;12m [39m[38;5;239m│[39m
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-[38;5;239m│[39m[38;5;12m(https://scikit-learn.org/stable/_static/ml_map.png)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mChoosing the Right Estimator[39m[38;5;12m [39m[38;5;239m│[39m
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-[38;5;239m│[39m[38;5;12m(https://i.imgur.com/RsHqY84.png)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Science [39m[38;5;12m[9mVenn[0m[38;5;12m Euler Diagram[39m[38;5;12m [39m[38;5;239m│[39m
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-[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m(https://www.springboard.com/blog/data-science-career-paths-different-roles-industry/)[39m[38;5;12m [39m[38;5;12mby[39m[38;5;12m [39m[38;5;12mSpringboard[39m[38;5;12m [39m[38;5;239m│[39m
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-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
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-[38;5;12m- [39m[38;5;14m[1mAcademic Torrents[0m[38;5;12m (https://academictorrents.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mADS-B Exchange[0m[38;5;12m (https://www.adsbexchange.com/data-samples/) - Specific datasets for aircraft and Automatic Dependent Surveillance-Broadcast (ADS-B) sources.[39m
-[38;5;12m- [39m[38;5;14m[1mhadoopilluminated.com[0m[38;5;12m (https://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html)[39m
-[38;5;12m- [39m[38;5;14m[1mdata.gov[0m[38;5;12m (https://catalog.data.gov/dataset) - The home of the U.S. Government's open data[39m
-[38;5;12m- [39m[38;5;14m[1mUnited States Census Bureau[0m[38;5;12m (https://www.census.gov/)[39m
-[38;5;12m- [39m[38;5;14m[1musgovxml.com[0m[38;5;12m (https://usgovxml.com/)[39m
-[38;5;12m- [39m[38;5;14m[1menigma.com[0m[38;5;12m (https://enigma.com/) - Navigate the world of public data - Quickly search and analyze billions of public records published by governments, companies and organizations.[39m
-[38;5;12m- [39m[38;5;14m[1mdatahub.io[0m[38;5;12m (https://datahub.io/)[39m
-[38;5;12m- [39m[38;5;14m[1maws.amazon.com/datasets[0m[38;5;12m (https://aws.amazon.com/datasets/)[39m
-[38;5;12m- [39m[38;5;14m[1mdatacite.org[0m[38;5;12m (https://datacite.org/)[39m
-[38;5;12m- [39m[38;5;14m[1mThe official portal for European data[0m[38;5;12m (https://data.europa.eu/en)[39m
-[38;5;12m- [39m[38;5;14m[1mNASDAQ:DATA[0m[38;5;12m (https://data.nasdaq.com/) - Nasdaq Data Link A premier source for financial, economic and alternative datasets.[39m
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-[38;5;12m- [39m[38;5;14m[1mPublic Big Data Sets[0m[38;5;12m (https://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html)[39m
-[38;5;12m- [39m[38;5;14m[1mKaggle Datasets[0m[38;5;12m (https://www.kaggle.com/datasets)[39m
-[38;5;12m- [39m[38;5;14m[1mA Deep Catalog of Human Genetic Variation[0m[38;5;12m (https://www.internationalgenome.org/data)[39m
-[38;5;12m- [39m[38;5;14m[1mA community-curated database of well-known people, places, and things[0m[38;5;12m (https://developers.google.com/freebase/)[39m
-[38;5;12m- [39m[38;5;14m[1mGoogle Public Data[0m[38;5;12m (https://www.google.com/publicdata/directory)[39m
-[38;5;12m- [39m[38;5;14m[1mWorld Bank Data[0m[38;5;12m (https://data.worldbank.org/)[39m
-[38;5;12m- [39m[38;5;14m[1mNYC Taxi data[0m[38;5;12m (https://chriswhong.github.io/nyctaxi/)[39m
-[38;5;12m- [39m[38;5;14m[1mOpen Data Philly[0m[38;5;12m (https://www.opendataphilly.org/) Connecting people with data for Philadelphia[39m
-[38;5;12m- [39m[38;5;14m[1mgrouplens.org[0m[38;5;12m (https://grouplens.org/datasets/) Sample movie (with ratings), book and wiki datasets[39m
-[38;5;12m- [39m[38;5;14m[1mUC Irvine Machine Learning Repository[0m[38;5;12m (https://archive.ics.uci.edu/ml/) - contains data sets good for machine learning[39m
-[38;5;12m- [39m[38;5;14m[1mresearch-quality data sets[0m[38;5;12m (https://web.archive.org/web/20150320022752/https://bitly.com/bundles/hmason/1) by [39m[38;5;14m[1mHilary Mason[0m[38;5;12m (https://web.archive.org/web/20150501033715/https://bitly.com/u/hmason/bundles)[39m
-[38;5;12m- [39m[38;5;14m[1mNational Centers for Environmental Information[0m[38;5;12m (https://www.ncei.noaa.gov/)[39m
-[38;5;12m- [39m[38;5;14m[1mClimateData.us[0m[38;5;12m (https://www.climatedata.us/) (related: [39m[38;5;14m[1mU.S. Climate Resilience Toolkit[0m[38;5;12m (https://toolkit.climate.gov/))[39m
-[38;5;12m- [39m[38;5;14m[1mr/datasets[0m[38;5;12m (https://www.reddit.com/r/datasets/)[39m
-[38;5;12m- [39m[38;5;14m[1mMapLight[0m[38;5;12m (https://www.maplight.org/data-series) - provides a variety of data free of charge for uses that are freely available to the general public. Click on a data set below to learn more[39m
-[38;5;12m- [39m[38;5;14m[1mGHDx[0m[38;5;12m (https://ghdx.healthdata.org/) - Institute for Health Metrics and Evaluation - a catalog of health and demographic datasets from around the world and including IHME results[39m
-[38;5;12m- [39m[38;5;14m[1mSt. Louis Federal Reserve Economic Data - FRED[0m[38;5;12m (https://fred.stlouisfed.org/)[39m
-[38;5;12m- [39m[38;5;14m[1mNew Zealand Institute of Economic Research – Data1850[0m[38;5;12m (https://data1850.nz/)[39m
-[38;5;12m- [39m[38;5;14m[1mOpen Data Sources[0m[38;5;12m (https://github.com/datasciencemasters/data)[39m
-[38;5;12m- [39m[38;5;14m[1mUNICEF Data[0m[38;5;12m (https://data.unicef.org/)[39m
-[38;5;12m- [39m[38;5;14m[1mundata[0m[38;5;12m (https://data.un.org/)[39m
-[38;5;12m- [39m[38;5;14m[1mNASA SocioEconomic Data and Applications Center - SEDAC[0m[38;5;12m (https://sedac.ciesin.columbia.edu/)[39m
-[38;5;12m- [39m[38;5;14m[1mThe GDELT Project[0m[38;5;12m (https://www.gdeltproject.org/)[39m
-[38;5;12m- [39m[38;5;14m[1mSweden, Statistics[0m[38;5;12m (https://www.scb.se/en/)[39m
-[38;5;12m- [39m[38;5;14m[1mStackExchange Data Explorer[0m[38;5;12m (https://data.stackexchange.com) - an open source tool for running arbitrary queries against public data from the Stack Exchange network.[39m
-[38;5;12m- [39m[38;5;14m[1mSocialGrep[0m[38;5;12m (https://socialgrep.com/datasets) - a collection of open Reddit datasets.[39m
-[38;5;12m- [39m[38;5;14m[1mSan Fransisco Government Open Data[0m[38;5;12m (https://datasf.org/opendata/)[39m
-[38;5;12m- [39m[38;5;14m[1mIBM Asset Dataset[0m[38;5;12m (https://developer.ibm.com/exchanges/data/)[39m
-[38;5;12m- [39m[38;5;14m[1mOpen data Index[0m[38;5;12m (https://index.okfn.org/)[39m
-[38;5;12m- [39m[38;5;14m[1mPublic Git Archive[0m[38;5;12m (https://github.com/src-d/datasets/tree/master/PublicGitArchive)[39m
-[38;5;12m- [39m[38;5;14m[1mGHTorrent[0m[38;5;12m (https://ghtorrent.org/)[39m
-[38;5;12m- [39m[38;5;14m[1mMicrosoft Research Open Data[0m[38;5;12m (https://msropendata.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mOpen Government Data Platform India[0m[38;5;12m (https://data.gov.in/)[39m
-[38;5;12m- [39m[38;5;14m[1mGoogle Dataset Search (beta)[0m[38;5;12m (https://datasetsearch.research.google.com/)[39m
-[38;5;12m- [39m[38;5;14m[1mNAYN.CO Turkish News with categories[0m[38;5;12m (https://github.com/naynco/nayn.data)[39m
-[38;5;12m- [39m[38;5;14m[1mCovid-19[0m[38;5;12m (https://github.com/datasets/covid-19)[39m
-[38;5;12m- [39m[38;5;14m[1mCovid-19 Google[0m[38;5;12m (https://github.com/google-research/open-covid-19-data)[39m
-[38;5;12m- [39m[38;5;14m[1mEnron Email Dataset[0m[38;5;12m (https://www.cs.cmu.edu/~./enron/)[39m
-[38;5;12m- [39m[38;5;14m[1m5000 Images of Clothes[0m[38;5;12m (https://github.com/alexeygrigorev/clothing-dataset)[39m
-[38;5;12m- [39m[38;5;14m[1mIBB Open Portal[0m[38;5;12m (https://data.ibb.gov.tr/en/)[39m
-[38;5;12m- [39m[38;5;14m[1mThe Humanitarian Data Exchange[0m[38;5;12m (https://data.humdata.org/)[39m
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-[38;2;255;187;0m[4mComics[0m
-[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
-
-[38;5;12m- [39m[38;5;14m[1mComic compilation[0m[38;5;12m (https://medium.com/@nikhil_garg/a-compilation-of-comics-explaining-statistics-data-science-and-machine-learning-eeefbae91277)[39m
-[38;5;12m- [39m[38;5;14m[1mCartoons[0m[38;5;12m (https://www.kdnuggets.com/websites/cartoons.html)[39m
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-[38;2;255;187;0m[4mOther Awesome Lists[0m
-
-[38;5;12m- Other amazingly awesome lists can be found in the [39m[38;5;14m[1mawesome-awesomeness[0m[38;5;12m (https://github.com/bayandin/awesome-awesomeness)[39m
-[38;5;12m- [39m[38;5;14m[1mAwesome Machine Learning[0m[38;5;12m (https://github.com/josephmisiti/awesome-machine-learning)[39m
-[38;5;12m- [39m[38;5;14m[1mlists[0m[38;5;12m (https://github.com/jnv/lists)[39m
-[38;5;12m- [39m[38;5;14m[1mawesome-dataviz[0m[38;5;12m (https://github.com/javierluraschi/awesome-dataviz)[39m
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-[38;5;12m- [39m[38;5;14m[1mData Science IPython Notebooks.[0m[38;5;12m (https://github.com/donnemartin/data-science-ipython-notebooks)[39m
-[38;5;12m- [39m[38;5;14m[1mawesome-r[0m[38;5;12m (https://github.com/qinwf/awesome-R)[39m
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-[38;5;12m- [39m[38;5;14m[1mawesome-Machine Learning & Deep Learning Tutorials[0m[38;5;12m (https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md)[39m
-[38;5;12m- [39m[38;5;14m[1mAwesome Data Science Ideas[0m[38;5;12m (https://github.com/JosPolfliet/awesome-ai-usecases)[39m
-[38;5;12m- [39m[38;5;14m[1mMachine Learning for Software Engineers[0m[38;5;12m (https://github.com/ZuzooVn/machine-learning-for-software-engineers)[39m
-[38;5;12m- [39m[38;5;14m[1mCommunity Curated Data Science Resources[0m[38;5;12m (https://hackr.io/tutorials/learn-data-science)[39m
-[38;5;12m- [39m[38;5;14m[1mAwesome Machine Learning On Source Code[0m[38;5;12m (https://github.com/src-d/awesome-machine-learning-on-source-code)[39m
-[38;5;12m- [39m[38;5;14m[1mAwesome Community Detection[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-community-detection)[39m
-[38;5;12m- [39m[38;5;14m[1mAwesome Graph Classification[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-graph-classification)[39m
-[38;5;12m- [39m[38;5;14m[1mAwesome Decision Tree Papers[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-decision-tree-papers)[39m
-[38;5;12m- [39m[38;5;14m[1mAwesome Fraud Detection Papers[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-fraud-detection-papers)[39m
-[38;5;12m- [39m[38;5;14m[1mAwesome Gradient Boosting Papers[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers)[39m
-[38;5;12m- [39m[38;5;14m[1mAwesome Computer Vision Models[0m[38;5;12m (https://github.com/nerox8664/awesome-computer-vision-models)[39m
-[38;5;12m- [39m[38;5;14m[1mAwesome Monte Carlo Tree Search[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers)[39m
-[38;5;12m- [39m[38;5;14m[1mGlossary of common statistics and ML terms[0m[38;5;12m (https://www.analyticsvidhya.com/glossary-of-common-statistics-and-machine-learning-terms/)[39m
-[38;5;12m- [39m[38;5;14m[1m100 NLP Papers[0m[38;5;12m (https://github.com/mhagiwara/100-nlp-papers)[39m
-[38;5;12m- [39m[38;5;14m[1mAwesome Game Datasets[0m[38;5;12m (https://github.com/leomaurodesenv/game-datasets#readme)[39m
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-[38;5;12m- [39m[38;5;14m[1mAwesome Explainable Graph Reasoning[0m[38;5;12m (https://github.com/AstraZeneca/awesome-explainable-graph-reasoning)[39m
-[38;5;12m- [39m[38;5;14m[1mTop Data Science Interview Questions[0m[38;5;12m (https://www.interviewbit.com/data-science-interview-questions/)[39m
-[38;5;12m- [39m[38;5;14m[1mAwesome Drug Synergy, Interaction and Polypharmacy Prediction[0m[38;5;12m (https://github.com/AstraZeneca/awesome-drug-pair-scoring)[39m
-[38;5;12m- [39m[38;5;14m[1mDeep Learning Interview Questions[0m[38;5;12m (https://www.adaface.com/blog/deep-learning-interview-questions/)[39m
-[38;5;12m- [39m[38;5;14m[1mTop Future Trends in Data Science in 2023[0m[38;5;12m (https://medium.com/the-modern-scientist/top-future-trends-in-data-science-in-2023-3e616c8998b8)[39m
-[38;5;12m- [39m[38;5;14m[1mHow Generative AI Is Changing Creative Work[0m[38;5;12m (https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work)[39m
-[38;5;12m- [39m[38;5;14m[1mWhat is generative AI?[0m[38;5;12m (https://www.techtarget.com/searchenterpriseai/definition/generative-AI)[39m
-
-[38;2;255;187;0m[4mHobby[0m
-[38;5;12m- [39m[38;5;14m[1mAwesome Music Production[0m[38;5;12m (https://github.com/ad-si/awesome-music-production)[39m
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-[38;5;12m window.dataLayer = window.dataLayer || [39m[38;5;12m ;[39m
-[38;5;12m function gtag(){dataLayer.push(arguments);}[39m
-[38;5;12m gtag('js', new Date());[39m
-
-[38;5;12m gtag('config', 'G-YL0RV0E5XZ');[39m
+[38;5;12mA JavaScript visualization library for HTML and SVG - http://d3js.org[39m
+[38;5;12mReal-time visualization library - https://github.com/fastly/epoch[39m
diff --git a/terminal/datascience2 b/terminal/datascience2
index b81a5e1..cb187ef 100644
--- a/terminal/datascience2
+++ b/terminal/datascience2
@@ -1,10 +1,1189 @@
-[38;5;12mawesome-data-science[39m
-[38;5;12m====================[39m
-[38;5;12mA curated list of amazingly awesome open source data science resources.[39m
-[38;5;12mData Visualization[39m
+[38;5;12m [39m[38;2;255;187;0m[1m[4mAWESOME DATA SCIENCE[0m
-[38;5;12mA JavaScript visualization library for HTML and SVG - http://d3js.org[39m
+[38;5;14m[1m![0m[38;5;12mAwesome[39m[38;5;14m[1m (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)[0m[38;5;12m (https://github.com/sindresorhus/awesome) [39m
+
+[38;5;14m[1mAn open-source Data Science repository to learn and apply towards solving real world problems.[0m
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+[38;5;12mThis is a shortcut path to start studying [39m[38;5;14m[1mData Science[0m[38;5;12m. Just follow the steps to answer the questions, "What is Data Science and what should I study to learn Data Science?"[39m
+
+[38;2;255;187;0m[4mSponsors[0m
+
+[38;5;239m│[39m[38;5;12mSponsor[39m[38;5;239m│[39m[38;5;12m [39m[38;5;12mPitch[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m├[39m[38;5;239m───────[39m[38;5;239m┼[39m[38;5;239m───────────────────────────────────────────[39m[38;5;239m┤[39m
+[38;5;239m│[39m[38;5;12m---[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mBe the first to sponsor! [39m[48;5;235m[38;5;249mgithub@academic.io[49m[39m[38;5;239m│[39m
+
+
+
+
+
+[38;2;255;187;0m[4mTable of Contents[0m
+
+[38;5;12m- [39m[38;5;14m[1mWhat is Data Science?[0m[38;5;12m (#what-is-data-science)[39m
+[38;5;12m- [39m[38;5;14m[1mWhere do I Start?[0m[38;5;12m (#where-do-i-start)[39m
+[38;5;12m- [39m[38;5;14m[1mTraining Resources[0m[38;5;12m (#training-resources)[39m
+[38;5;12m - [39m[38;5;14m[1mTutorials[0m[38;5;12m (#tutorials)[39m
+[38;5;12m - [39m[38;5;14m[1mFree Courses[0m[38;5;12m (#free-courses)[39m
+[38;5;12m - [39m[38;5;14m[1mMassively Open Online Courses[0m[38;5;12m (#moocs)[39m
+[38;5;12m - [39m[38;5;14m[1mIntensive Programs[0m[38;5;12m (#intensive-programs)[39m
+[38;5;12m - [39m[38;5;14m[1mColleges[0m[38;5;12m (#colleges)[39m
+[38;5;12m- [39m[38;5;14m[1mThe Data Science Toolbox[0m[38;5;12m (#the-data-science-toolbox)[39m
+[38;5;12m - [39m[38;5;14m[1mAlgorithms[0m[38;5;12m (#algorithms)[39m
+[48;5;235m[38;5;249m- **Supervised Learning** (#supervised-learning)[49m[39m[48;5;235m[38;5;249m [49m[39m
+[48;5;235m[38;5;249m- **Unsupervised Learning** (#unsupervised-learning)[49m[39m[48;5;235m[38;5;249m [49m[39m
+[48;5;235m[38;5;249m- **Semi-Supervised Learning** (#semi-supervised-learning)[49m[39m[48;5;235m[38;5;249m [49m[39m
+[48;5;235m[38;5;249m- **Reinforcement Learning** (#reinforcement-learning)[49m[39m[48;5;235m[38;5;249m [49m[39m
+[48;5;235m[38;5;249m- **Data Mining Algorithms** (#data-mining-algorithms)[49m[39m[48;5;235m[38;5;249m [49m[39m
+[48;5;235m[38;5;249m- **Deep Learning Architectures** (#deep-learning-architectures)[49m[39m
+[38;5;12m - [39m[38;5;14m[1mGeneral Machine Learning Packages[0m[38;5;12m (#general-machine-learning-packages)[39m
+[38;5;12m - [39m[38;5;14m[1mDeep Learning Packages[0m[38;5;12m (#deep-learning-packages)[39m
+[48;5;235m[38;5;249m- **PyTorch Ecosystem** (#pytorch-ecosystem)[49m[39m[48;5;235m[38;5;249m [49m[39m
+[48;5;235m[38;5;249m- **TensorFlow Ecosystem** (#tensorflow-ecosystem)[49m[39m
+[48;5;235m[38;5;249m- **Keras Ecosystem** (#keras-ecosystem)[49m[39m[48;5;235m[38;5;249m [49m[39m
+[38;5;12m - [39m[38;5;14m[1mVisualization Tools[0m[38;5;12m (#visualization-tools)[39m
+[38;5;12m - [39m[38;5;14m[1mMiscellaneous Tools[0m[38;5;12m (#miscellaneous-tools)[39m
+[38;5;12m- [39m[38;5;14m[1mLiterature and Media[0m[38;5;12m (#literature-and-media)[39m
+[38;5;12m - [39m[38;5;14m[1mBooks[0m[38;5;12m (#books)[39m
+[48;5;235m[38;5;249m- **Book Deals (Affiliated)** (#book-deals-affiliated-)[49m[39m
+[38;5;12m - [39m[38;5;14m[1mJournals, Publications, and Magazines[0m[38;5;12m (#journals-publications-and-magazines)[39m
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+[38;5;12m- [39m[38;5;14m[1mMS in Business Analytics @ ASU Online[0m[38;5;12m (https://asuonline.asu.edu/online-degree-programs/graduate/master-science-business-analytics/)[39m
+[38;5;12m- [39m[38;5;14m[1mMS in Applied Data Science @ Syracuse[0m[38;5;12m (https://ischool.syr.edu/academics/applied-data-science-masters-degree/)[39m
+[38;5;12m- [39m[38;5;14m[1mM.S. Management & Data Science @ Leuphana[0m[38;5;12m (https://www.leuphana.de/en/graduate-school/masters-programmes/management-data-science.html)[39m
+[38;5;12m- [39m[38;5;14m[1mMaster of Data Science @ Melbourne University[0m[38;5;12m (https://study.unimelb.edu.au/find/courses/graduate/master-of-data-science/#overview)[39m
+[38;5;12m- [39m[38;5;14m[1mMsc in Data Science @ The University of Edinburgh[0m[38;5;12m (https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=902)[39m
+[38;5;12m- [39m[38;5;14m[1mMaster of Management Analytics @ Queen's University[0m[38;5;12m (https://smith.queensu.ca/grad_studies/mma/index.php)[39m
+[38;5;12m- [39m[38;5;14m[1mMaster of Data Science @ Illinois Institute of Technology[0m[38;5;12m (https://www.iit.edu/academics/programs/data-science-mas)[39m
+[38;5;12m- [39m[38;5;14m[1mMaster of Applied Data Science @ The University of Michigan[0m[38;5;12m (https://www.si.umich.edu/programs/master-applied-data-science-online)[39m
+[38;5;12m- [39m[38;5;14m[1mMaster Data Science and Artificial Intelligence @ Eindhoven University of Technology[0m[38;5;12m (https://www.tue.nl/en/education/graduate-school/master-data-science-and-artificial-intelligence/)[39m
+[38;5;12m- [39m[38;5;14m[1mMaster's Degree in Data Science and Computer Engineering @ University of Granada[0m[38;5;12m (https://masteres.ugr.es/datcom/)[39m
+
+[38;2;255;187;0m[4mThe Data Science Toolbox[0m
+[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
+
+[38;5;12mThis section is a collection of packages, tools, algorithms, and other useful items in the data science world.[39m
+
+[38;2;255;187;0m[4mAlgorithms[0m
+[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
+
+[38;5;12mThese are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it.[39m
+
+[38;2;255;187;0m[4mThree kinds of Machine Learning Systems[0m
+
+[38;5;12m- Based on training with human supervision[39m
+[38;5;12m- Based on learning incrementally on fly[39m
+[38;5;12m- Based on data points comparison and pattern detection[39m
+[38;5;12m [39m
+[38;2;255;187;0m[4mSupervised Learning[0m
+
+[38;5;12m- [39m[38;5;14m[1mRegression[0m[38;5;12m (https://en.wikipedia.org/wiki/Regression)[39m
+[38;5;12m- [39m[38;5;14m[1mLinear Regression[0m[38;5;12m (https://en.wikipedia.org/wiki/Linear_regression)[39m
+[38;5;12m- [39m[38;5;14m[1mOrdinary Least Squares[0m[38;5;12m (https://en.wikipedia.org/wiki/Ordinary_least_squares)[39m
+[38;5;12m- [39m[38;5;14m[1mLogistic Regression[0m[38;5;12m (https://en.wikipedia.org/wiki/Logistic_regression)[39m
+[38;5;12m- [39m[38;5;14m[1mStepwise Regression[0m[38;5;12m (https://en.wikipedia.org/wiki/Stepwise_regression)[39m
+[38;5;12m- [39m[38;5;14m[1mMultivariate Adaptive Regression Splines[0m[38;5;12m (https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_spline)[39m
+[38;5;12m- [39m[38;5;14m[1mSoftmax Regression[0m[38;5;12m (https://d2l.ai/chapter_linear-classification/softmax-regression.html)[39m
+[38;5;12m- [39m[38;5;14m[1mLocally Estimated Scatterplot Smoothing[0m[38;5;12m (https://en.wikipedia.org/wiki/Local_regression)[39m
+[38;5;12m- Classification[39m
+[38;5;12m - [39m[38;5;14m[1mk-nearest neighbor[0m[38;5;12m (https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)[39m
+[38;5;12m - [39m[38;5;14m[1mSupport Vector Machines[0m[38;5;12m (https://en.wikipedia.org/wiki/Support_vector_machine)[39m
+[38;5;12m - [39m[38;5;14m[1mDecision Trees[0m[38;5;12m (https://en.wikipedia.org/wiki/Decision_tree)[39m
+[38;5;12m - [39m[38;5;14m[1mID3 algorithm[0m[38;5;12m (https://en.wikipedia.org/wiki/ID3_algorithm)[39m
+[38;5;12m - [39m[38;5;14m[1mC4.5 algorithm[0m[38;5;12m (https://en.wikipedia.org/wiki/C4.5_algorithm)[39m
+[38;5;12m- [39m[38;5;14m[1mEnsemble Learning[0m[38;5;12m (https://scikit-learn.org/stable/modules/ensemble.html)[39m
+[38;5;12m - [39m[38;5;14m[1mBoosting[0m[38;5;12m (https://en.wikipedia.org/wiki/Boosting_(machine_learning))[39m
+[38;5;12m - [39m[38;5;14m[1mStacking[0m[38;5;12m (https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python)[39m
+[38;5;12m - [39m[38;5;14m[1mBagging[0m[38;5;12m (https://en.wikipedia.org/wiki/Bootstrap_aggregating)[39m
+[38;5;12m - [39m[38;5;14m[1mRandom Forest[0m[38;5;12m (https://en.wikipedia.org/wiki/Random_forest)[39m
+[38;5;12m - [39m[38;5;14m[1mAdaBoost[0m[38;5;12m (https://en.wikipedia.org/wiki/AdaBoost)[39m
+
+[38;2;255;187;0m[4mUnsupervised Learning[0m
+[38;5;12m- [39m[38;5;14m[1mClustering[0m[38;5;12m (https://scikit-learn.org/stable/modules/clustering.html#clustering)[39m
+[38;5;12m - [39m[38;5;14m[1mHierchical clustering[0m[38;5;12m (https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering)[39m
+[38;5;12m - [39m[38;5;14m[1mk-means[0m[38;5;12m (https://scikit-learn.org/stable/modules/clustering.html#k-means)[39m
+[38;5;12m - [39m[38;5;14m[1mDensity-based clustering[0m[38;5;12m (https://scikit-learn.org/stable/modules/clustering.html#dbscan)[39m
+[38;5;12m - [39m[38;5;14m[1mFuzzy clustering[0m[38;5;12m (https://en.wikipedia.org/wiki/Fuzzy_clustering)[39m
+[38;5;12m - [39m[38;5;14m[1mMixture models[0m[38;5;12m (https://en.wikipedia.org/wiki/Mixture_model)[39m
+[38;5;12m- [39m[38;5;14m[1mDimension Reduction[0m[38;5;12m (https://en.wikipedia.org/wiki/Dimensionality_reduction)[39m
+[38;5;12m - [39m[38;5;14m[1mPrincipal Component Analysis (PCA)[0m[38;5;12m (https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca)[39m
+[38;5;12m - [39m[38;5;14m[1mt-SNE; t-distributed Stochastic Neighbor Embedding[0m[38;5;12m (https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca)[39m
+[38;5;12m - [39m[38;5;14m[1mFactor Analysis[0m[38;5;12m (https://scikit-learn.org/stable/modules/decomposition.html#factor-analysis)[39m
+[38;5;12m - [39m[38;5;14m[1mLatent Dirichlet Allocation (LDA)[0m[38;5;12m (https://scikit-learn.org/stable/modules/decomposition.html#latent-dirichlet-allocation-lda)[39m
+[38;5;12m- [39m[38;5;14m[1mNeural Networks[0m[38;5;12m (https://en.wikipedia.org/wiki/Neural_network)[39m
+[38;5;12m- [39m[38;5;14m[1mSelf-organizing map[0m[38;5;12m (https://en.wikipedia.org/wiki/Self-organizing_map)[39m
+[38;5;12m- [39m[38;5;14m[1mAdaptive resonance theory[0m[38;5;12m (https://en.wikipedia.org/wiki/Adaptive_resonance_theory)[39m
+[38;5;12m- [39m[38;5;14m[1mHidden Markov Models (HMM)[0m[38;5;12m (https://en.wikipedia.org/wiki/Hidden_Markov_model)[39m
+
+[38;2;255;187;0m[4mSemi-Supervised Learning[0m
+
+[38;5;12m- S3VM[39m
+[38;5;12m- [39m[38;5;14m[1mClustering[0m[38;5;12m (https://en.wikipedia.org/wiki/Weak_supervision#Cluster_assumption)[39m
+[38;5;12m- [39m[38;5;14m[1mGenerative models[0m[38;5;12m (https://en.wikipedia.org/wiki/Weak_supervision#Generative_models)[39m
+[38;5;12m- [39m[38;5;14m[1mLow-density separation[0m[38;5;12m (https://en.wikipedia.org/wiki/Weak_supervision#Low-density_separation)[39m
+[38;5;12m- [39m[38;5;14m[1mLaplacian regularization[0m[38;5;12m (https://en.wikipedia.org/wiki/Weak_supervision#Laplacian_regularization)[39m
+[38;5;12m- [39m[38;5;14m[1mHeuristic approaches[0m[38;5;12m (https://en.wikipedia.org/wiki/Weak_supervision#Heuristic_approaches)[39m
+
+[38;2;255;187;0m[4mReinforcement Learning[0m
+
+[38;5;12m- [39m[38;5;14m[1mQ Learning[0m[38;5;12m (https://en.wikipedia.org/wiki/Q-learning)[39m
+[38;5;12m- [39m[38;5;14m[1mSARSA (State-Action-Reward-State-Action) algorithm[0m[38;5;12m (https://en.wikipedia.org/wiki/State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action)[39m
+[38;5;12m- [39m[38;5;14m[1mTemporal difference learning[0m[38;5;12m (https://en.wikipedia.org/wiki/Temporal_difference_learning#:~:text=Temporal%20difference%20(TD)%20learning%20refers,estimate%20of%20the%20value%20function.)[39m
+
+[38;2;255;187;0m[4mData Mining Algorithms[0m
+
+[38;5;12m- [39m[38;5;14m[1mC4.5[0m[38;5;12m (https://en.wikipedia.org/wiki/C4.5_algorithm)[39m
+[38;5;12m- [39m[38;5;14m[1mk-Means[0m[38;5;12m (https://en.wikipedia.org/wiki/K-means_clustering)[39m
+[38;5;12m- [39m[38;5;14m[1mSVM (Support Vector Machine)[0m[38;5;12m (https://en.wikipedia.org/wiki/Support_vector_machine)[39m
+[38;5;12m- [39m[38;5;14m[1mApriori[0m[38;5;12m (https://en.wikipedia.org/wiki/Apriori_algorithm)[39m
+[38;5;12m- [39m[38;5;14m[1mEM (Expectation-Maximization)[0m[38;5;12m (https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)[39m
+[38;5;12m- [39m[38;5;14m[1mPageRank[0m[38;5;12m (https://en.wikipedia.org/wiki/PageRank)[39m
+[38;5;12m- [39m[38;5;14m[1mAdaBoost[0m[38;5;12m (https://en.wikipedia.org/wiki/AdaBoost)[39m
+[38;5;12m- [39m[38;5;14m[1mKNN (K-Nearest Neighbors)[0m[38;5;12m (https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)[39m
+[38;5;12m- [39m[38;5;14m[1mNaive Bayes[0m[38;5;12m (https://en.wikipedia.org/wiki/Naive_Bayes_classifier)[39m
+[38;5;12m- [39m[38;5;14m[1mCART (Classification and Regression Trees)[0m[38;5;12m (https://en.wikipedia.org/wiki/Decision_tree_learning)[39m
+
+
+
+[38;2;255;187;0m[4mDeep Learning architectures[0m
+
+[38;5;12m- [39m[38;5;14m[1mMultilayer Perceptron[0m[38;5;12m (https://en.wikipedia.org/wiki/Multilayer_perceptron)[39m
+[38;5;12m- [39m[38;5;14m[1mConvolutional Neural Network (CNN)[0m[38;5;12m (https://en.wikipedia.org/wiki/Convolutional_neural_network)[39m
+[38;5;12m- [39m[38;5;14m[1mRecurrent Neural Network (RNN)[0m[38;5;12m (https://en.wikipedia.org/wiki/Recurrent_neural_network)[39m
+[38;5;12m- [39m[38;5;14m[1mBoltzmann Machines[0m[38;5;12m (https://en.wikipedia.org/wiki/Boltzmann_machine)[39m
+[38;5;12m- [39m[38;5;14m[1mAutoencoder[0m[38;5;12m (https://www.tensorflow.org/tutorials/generative/autoencoder)[39m
+[38;5;12m- [39m[38;5;14m[1mGenerative Adversarial Network (GAN)[0m[38;5;12m (https://developers.google.com/machine-learning/gan/gan_structure)[39m
+[38;5;12m- [39m[38;5;14m[1mSelf-Organized Maps[0m[38;5;12m (https://en.wikipedia.org/wiki/Self-organizing_map)[39m
+[38;5;12m- [39m[38;5;14m[1mTransformer[0m[38;5;12m (https://www.tensorflow.org/text/tutorials/transformer)[39m
+[38;5;12m- [39m[38;5;14m[1mConditional Random Field (CRF)[0m[38;5;12m (https://towardsdatascience.com/conditional-random-fields-explained-e5b8256da776)[39m
+
+[38;2;255;187;0m[4mGeneral Machine Learning Packages[0m
+[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
+
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-learn[0m[38;5;12m (https://scikit-learn.org/)[39m
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-multilearn[0m[38;5;12m (https://github.com/scikit-multilearn/scikit-multilearn)[39m
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-expertsys[0m[38;5;12m (https://github.com/tmadl/sklearn-expertsys)[39m
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-feature[0m[38;5;12m (https://github.com/jundongl/scikit-feature)[39m
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-rebate[0m[38;5;12m (https://github.com/EpistasisLab/scikit-rebate)[39m
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mseqlearn[0m[38;5;12m (https://github.com/larsmans/seqlearn)[39m
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-bayes[0m[38;5;12m (https://github.com/AmazaspShumik/sklearn-bayes)[39m
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1msklearn-crfsuite[0m[38;5;12m (https://github.com/TeamHG-Memex/sklearn-crfsuite)[39m
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+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mscikit-posthocs[0m[38;5;12m (https://github.com/maximtrp/scikit-posthocs)[39m
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mpystruct[0m[38;5;12m (https://github.com/pystruct/pystruct)[39m
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mShogun[0m[38;5;12m (https://www.shogun-toolbox.org/)[39m
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mxLearn[0m[38;5;12m (https://github.com/aksnzhy/xlearn)[39m
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mcuML[0m[38;5;12m (https://github.com/rapidsai/cuml)[39m
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mcausalml[0m[38;5;12m (https://github.com/uber/causalml)[39m
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+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmodAL[0m[38;5;12m (https://github.com/modAL-python/modAL)[39m
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+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mimodels[0m[38;5;12m (https://github.com/csinva/imodels)[39m
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+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDeepchecks[0m[38;5;12m (https://github.com/deepchecks/deepchecks)[39m
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+
+[38;2;255;187;0m[4mDeep Learning Packages[0m
+
+[38;2;255;187;0m[4mPyTorch Ecosystem[0m
+[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPyTorch[0m[38;5;12m (https://github.com/pytorch/pytorch)[39m
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+[38;5;239m│[39m[38;5;14m[1mDask[0m[38;5;12m (https://dask.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAn open source Python library to painlessly transition your analytics code to distributed computing systems (Big Data)[39m[38;5;12m [39m[38;5;239m│[39m
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+[38;5;239m│[39m[38;5;14m[1mspaCy[0m[38;5;12m (https://spacy.io/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA performant natural language processing toolkit[39m[38;5;12m [39m[38;5;239m│[39m
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+[38;5;239m│[39m[38;5;14m[1mPython Data Science Handbook[0m[38;5;12m (https://github.com/jakevdp/PythonDataScienceHandbook)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mPython Data Science Handbook: full text in Jupyter Notebooks[39m[38;5;12m [39m[38;5;239m│[39m
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+[38;5;239m│[39m[38;5;14m[1mDeepnote[0m[38;5;12m (https://deepnote.com)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA new kind of data science notebook. Jupyter-compatible, with real-time collaboration and running in the cloud.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mValohai[0m[38;5;12m (https://valohai.com)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAn MLOps platform that handles machine orchestration, automatic reproducibility and deployment.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mPyMC3[0m[38;5;12m (https://docs.pymc.io/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA Python Library for Probabalistic Programming (Bayesian Inference and Machine Learning)[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mPyStan[0m[38;5;12m (https://pypi.org/project/pystan/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mPython interface to Stan (Bayesian inference and modeling)[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mhmmlearn[0m[38;5;12m (https://pypi.org/project/hmmlearn/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mUnsupervised learning and inference of Hidden Markov Models[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mChaos Genius[0m[38;5;12m (https://github.com/chaos-genius/chaos_genius/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mML powered analytics engine for outlier/anomaly detection and root cause analysis[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mNimblebox[0m[38;5;12m (https://nimblebox.ai/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA[39m[38;5;12m [39m[38;5;12mfull-stack[39m[38;5;12m [39m[38;5;12mMLOps[39m[38;5;12m [39m[38;5;12mplatform[39m[38;5;12m [39m[38;5;12mdesigned[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mhelp[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mscientists[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mmachine[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mpractitioners[39m[38;5;12m [39m[38;5;12maround[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mworld[39m[38;5;12m [39m[38;5;12mdiscover,[39m[38;5;12m [39m[38;5;12mcreate,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mlaunch[39m[38;5;12m [39m[38;5;12mmulti-cloud[39m[38;5;12m [39m[38;5;12mapps[39m[38;5;12m [39m[38;5;12mfrom[39m[38;5;12m [39m[38;5;12mtheir[39m[38;5;12m [39m[38;5;12mweb[39m[38;5;12m [39m[38;5;12mbrowser.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mTowhee[0m[38;5;12m (https://github.com/towhee-io/towhee)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA Python library that helps you encode your unstructured data into embeddings.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mLineaPy[0m[38;5;12m (https://github.com/LineaLabs/lineapy)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mEver[39m[38;5;12m [39m[38;5;12mbeen[39m[38;5;12m [39m[38;5;12mfrustrated[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mcleaning[39m[38;5;12m [39m[38;5;12mup[39m[38;5;12m [39m[38;5;12mlong,[39m[38;5;12m [39m[38;5;12mmessy[39m[38;5;12m [39m[38;5;12mJupyter[39m[38;5;12m [39m[38;5;12mnotebooks?[39m[38;5;12m [39m[38;5;12mWith[39m[38;5;12m [39m[38;5;12mLineaPy,[39m[38;5;12m [39m[38;5;12man[39m[38;5;12m [39m[38;5;12mopen[39m[38;5;12m [39m[38;5;12msource[39m[38;5;12m [39m[38;5;12mPython[39m[38;5;12m [39m[38;5;12mlibrary,[39m[38;5;12m [39m[38;5;12mit[39m[38;5;12m [39m[38;5;12mtakes[39m[38;5;12m [39m[38;5;12mas[39m[38;5;12m [39m[38;5;12mlittle[39m[38;5;12m [39m[38;5;12mas[39m[38;5;12m [39m[38;5;12mtwo[39m[38;5;239m│[39m
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+[38;5;239m│[39m[38;5;14m[1menvd[0m[38;5;12m (https://github.com/tensorchord/envd)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m🏕️ machine learning development environment for data science and AI/ML engineering teams[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mExplore Data Science Libraries[0m[38;5;12m (https://kandi.openweaver.com/explore/data-science)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mA[39m[38;5;12m [39m[38;5;12msearch[39m[38;5;12m [39m[38;5;12mengine[39m[38;5;12m [39m[38;5;12m🔎[39m[38;5;12m [39m[38;5;12mtool[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mdiscover[39m[38;5;12m [39m[38;5;12m&[39m[38;5;12m [39m[38;5;12mfind[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mcurated[39m[38;5;12m [39m[38;5;12mlist[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mpopular[39m[38;5;12m [39m[38;5;12m&[39m[38;5;12m [39m[38;5;12mnew[39m[38;5;12m [39m[38;5;12mlibraries,[39m[38;5;12m [39m[38;5;12mtop[39m[38;5;12m [39m[38;5;12mauthors,[39m[38;5;12m [39m[38;5;12mtrending[39m[38;5;12m [39m[38;5;12mproject[39m[38;5;12m [39m[38;5;12mkits,[39m[38;5;12m [39m[38;5;12mdiscussions,[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mtutorials[39m[38;5;12m [39m[38;5;12m&[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mresources[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mMLEM[0m[38;5;12m (https://github.com/iterative/mlem)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m🐶 Version and deploy your ML models following GitOps principles[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mMLflow[0m[38;5;12m (https://mlflow.org/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mMLOps framework for managing ML models across their full lifecycle[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mcleanlab[0m[38;5;12m (https://github.com/cleanlab/cleanlab)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mPython library for data-centric AI and automatically detecting various issues in ML datasets[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mAutoGluon[0m[38;5;12m (https://github.com/awslabs/autogluon)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAutoML to easily produce accurate predictions for image, text, tabular, time-series, and multi-modal data[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mArize AI[0m[38;5;12m (https://arize.com/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mArize[39m[38;5;12m [39m[38;5;12mAI[39m[38;5;12m [39m[38;5;12mcommunity[39m[38;5;12m [39m[38;5;12mtier[39m[38;5;12m [39m[38;5;12mobservability[39m[38;5;12m [39m[38;5;12mtool[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mmonitoring[39m[38;5;12m [39m[38;5;12mmachine[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mmodels[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mproduction[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mroot-causing[39m[38;5;12m [39m[38;5;12missues[39m[38;5;12m [39m[38;5;12msuch[39m[38;5;12m [39m[38;5;12mas[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mquality[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mperformance[39m[38;5;12m [39m[38;5;12mdrift.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mAureo.io[0m[38;5;12m (https://aureo.io)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAureo.io[39m[38;5;12m [39m[38;5;12mis[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mlow-code[39m[38;5;12m [39m[38;5;12mplatform[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mfocuses[39m[38;5;12m [39m[38;5;12mon[39m[38;5;12m [39m[38;5;12mbuilding[39m[38;5;12m [39m[38;5;12martificial[39m[38;5;12m [39m[38;5;12mintelligence.[39m[38;5;12m [39m[38;5;12mIt[39m[38;5;12m [39m[38;5;12mprovides[39m[38;5;12m [39m[38;5;12musers[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mcapability[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mcreate[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mpipelines,[39m[38;5;12m [39m[38;5;12mautomations[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mintegrate[39m[38;5;12m [39m[38;5;12mthem[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12martificial[39m[38;5;12m [39m[38;5;12mintelligence[39m[38;5;12m [39m[38;5;12mmodels[39m[38;5;12m [39m[38;5;12m–[39m[38;5;12m [39m[38;5;12mall[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mtheir[39m[38;5;12m [39m[38;5;12mbasic[39m[38;5;12m [39m[38;5;12mdata.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mERD Lab[0m[38;5;12m (https://www.erdlab.io/)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mFree cloud based entity relationship diagram (ERD) tool made for developers.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mArize-Phoenix[0m[38;5;12m (https://docs.arize.com/phoenix)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mMLOps in a notebook - uncover insights, surface problems, monitor, and fine tune your models.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mComet[0m[38;5;12m (https://github.com/comet-ml/comet-examples)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAn[39m[38;5;12m [39m[38;5;12mMLOps[39m[38;5;12m [39m[38;5;12mplatform[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mexperiment[39m[38;5;12m [39m[38;5;12mtracking,[39m[38;5;12m [39m[38;5;12mmodel[39m[38;5;12m [39m[38;5;12mproduction[39m[38;5;12m [39m[38;5;12mmanagement,[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mmodel[39m[38;5;12m [39m[38;5;12mregistry,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mfull[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mlineage[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12msupport[39m[38;5;12m [39m[38;5;12myour[39m[38;5;12m [39m[38;5;12mML[39m[38;5;12m [39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mworkflow[39m[38;5;12m [39m[38;5;12mfrom[39m[38;5;12m [39m[38;5;12mtraining[39m[38;5;12m [39m[38;5;12mstraight[39m[38;5;12m [39m[38;5;12mthrough[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mproduction.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mCometLLM[0m[38;5;12m (https://github.com/comet-ml/comet-llm)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mLog, track, visualize, and search your LLM prompts and chains in one easy-to-use, 100% open-source tool.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mSynthical[0m[38;5;12m (https://synthical.com)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mAI-powered[39m[38;5;12m [39m[38;5;12mcollaborative[39m[38;5;12m [39m[38;5;12menvironment[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m[38;5;12mresearch.[39m[38;5;12m [39m[38;5;12mFind[39m[38;5;12m [39m[38;5;12mrelevant[39m[38;5;12m [39m[38;5;12mpapers,[39m[38;5;12m [39m[38;5;12mcreate[39m[38;5;12m [39m[38;5;12mcollections[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mmanage[39m[38;5;12m [39m[38;5;12mbibliography,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12msummarize[39m[38;5;12m [39m[38;5;12mcontent[39m[38;5;12m [39m[38;5;12m—[39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mall[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mone[39m[38;5;12m [39m[38;5;12mplace[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mteeplot[0m[38;5;12m (https://github.com/mmore500/teeplot)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mWorkflow tool to automatically organize data visualization output[39m[38;5;12m [39m[38;5;239m│[39m
+
+
+[38;2;255;187;0m[4mLiterature and Media[0m
+[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
+
+[38;5;12mThis section includes some additional reading material, channels to watch, and talks to listen to.[39m
+
+[38;2;255;187;0m[4mBooks[0m
+[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
+
+[38;5;12m- [39m[38;5;14m[1mData Science From Scratch: First Principles with Python[0m[38;5;12m (https://www.amazon.com/Data-Science-Scratch-Principles-Python-dp-1492041130/dp/1492041130/ref=dp_ob_title_bk)[39m
+[38;5;12m- [39m[38;5;14m[1mArtificial Intelligence with Python - Tutorialspoint[0m[38;5;12m (https://www.tutorialspoint.com/artificial_intelligence_with_python/artificial_intelligence_with_python_tutorial.pdf)[39m
+[38;5;12m- [39m[38;5;14m[1mMachine Learning from Scratch[0m[38;5;12m (https://dafriedman97.github.io/mlbook/content/introduction.html)[39m
+[38;5;12m- [39m[38;5;14m[1mProbabilistic Machine Learning: An Introduction[0m[38;5;12m (https://probml.github.io/pml-book/book1.html)[39m
+[38;5;12m- [39m[38;5;14m[1mA Comprehensive Guide to Machine Learning[0m[38;5;12m (https://www.eecs189.org/static/resources/comprehensive-guide.pdf)[39m
+[38;5;12m- [39m[38;5;14m[1mHow to Lead in Data Science[0m[38;5;12m (https://www.manning.com/books/how-to-lead-in-data-science) - Early Access[39m
+[38;5;12m- [39m[38;5;14m[1mFighting Churn With Data[0m[38;5;12m (https://www.manning.com/books/fighting-churn-with-data)[39m
+[38;5;12m- [39m[38;5;14m[1mData Science at Scale with Python and Dask[0m[38;5;12m (https://www.manning.com/books/data-science-with-python-and-dask)[39m
+[38;5;12m- [39m[38;5;14m[1mPython Data Science Handbook[0m[38;5;12m (https://jakevdp.github.io/PythonDataScienceHandbook/)[39m
+[38;5;12m- [39m[38;5;14m[1mThe Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists[0m[38;5;12m (https://www.thedatasciencehandbook.com/)[39m
+[38;5;12m- [39m[38;5;14m[1mThink Like a Data Scientist[0m[38;5;12m (https://www.manning.com/books/think-like-a-data-scientist)[39m
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+[38;5;12m- [39m[38;5;14m[1mPractical Data Science with R[0m[38;5;12m (https://www.manning.com/books/practical-data-science-with-r)[39m
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+[38;5;12m- [39m[38;5;14m[1mWhatSTheBigData[0m[38;5;12m (https://whatsthebigdata.com/) is some of, all of, or much more than the above and this blog explores its impact on information technology, the business world, government agencies, and our lives.[39m
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+[38;5;12m- [39m[38;5;14m[1mAsk Dr Chong: How to Lead in Data Science - Part 6[0m[38;5;12m (https://youtu.be/LOOt4OVC3hY)[39m
+[38;5;12m- [39m[38;5;14m[1mRegression Models: Applying simple Poisson regression[0m[38;5;12m (https://www.youtube.com/watch?v=9Hk8K8jhiOo)[39m
+[38;5;12m- [39m[38;5;14m[1mDeep Learning Architectures[0m[38;5;12m (https://www.youtube.com/playlist?list=PLv8Cp2NvcY8DpVcsmOT71kymgMmcr59Mf)[39m
+[38;5;12m- [39m[38;5;14m[1mTime Series Modelling and Analysis[0m[38;5;12m (https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK)[39m
+
+[38;2;255;187;0m[4mSocialize[0m
+[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
+
+[38;5;12mBelow are some Social Media links. Connect with other data scientists![39m
+
+[38;5;12m- [39m[38;5;14m[1mFacebook Accounts[0m[38;5;12m (#facebook-accounts)[39m
+[38;5;12m- [39m[38;5;14m[1mTwitter Accounts[0m[38;5;12m (#twitter-accounts)[39m
+[38;5;12m- [39m[38;5;14m[1mTelegram Channels[0m[38;5;12m (#telegram-channels)[39m
+[38;5;12m- [39m[38;5;14m[1mSlack Communities[0m[38;5;12m (#slack-communities)[39m
+[38;5;12m- [39m[38;5;14m[1mGitHub Groups[0m[38;5;12m (#github-groups)[39m
+[38;5;12m- [39m[38;5;14m[1mData Science Competitions[0m[38;5;12m (#data-science-competitions)[39m
+
+
+[38;2;255;187;0m[4mFacebook Accounts[0m
+[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
+
+[38;5;12m- [39m[38;5;14m[1mData[0m[38;5;12m (https://www.facebook.com/data)[39m
+[38;5;12m- [39m[38;5;14m[1mBig Data Scientist[0m[38;5;12m (https://www.facebook.com/Bigdatascientist)[39m
+[38;5;12m- [39m[38;5;14m[1mData Science Day[0m[38;5;12m (https://www.facebook.com/datascienceday/)[39m
+[38;5;12m- [39m[38;5;14m[1mData Science Academy[0m[38;5;12m (https://www.facebook.com/nycdatascience)[39m
+[38;5;12m- [39m[38;5;14m[1mFacebook Data Science Page[0m[38;5;12m (https://www.facebook.com/pages/Data-science/431299473579193?ref=br_rs)[39m
+[38;5;12m- [39m[38;5;14m[1mData Science London[0m[38;5;12m (https://www.facebook.com/pages/Data-Science-London/226174337471513)[39m
+[38;5;12m- [39m[38;5;14m[1mData Science Technology and Corporation[0m[38;5;12m (https://www.facebook.com/DataScienceTechnologyCorporation?ref=br_rs)[39m
+[38;5;12m- [39m[38;5;14m[1mData Science - Closed Group[0m[38;5;12m (https://www.facebook.com/groups/1394010454157077/?ref=br_rs)[39m
+[38;5;12m- [39m[38;5;14m[1mCenter for Data Science[0m[38;5;12m (https://www.facebook.com/centerdatasciences?ref=br_rs)[39m
+[38;5;12m- [39m[38;5;14m[1mBig data hadoop NOSQL Hive Hbase[0m[38;5;12m (https://www.facebook.com/groups/bigdatahadoop/)[39m
+[38;5;12m- [39m[38;5;14m[1mAnalytics, Data Mining, Predictive Modeling, Artificial Intelligence[0m[38;5;12m (https://www.facebook.com/groups/data.analytics/)[39m
+[38;5;12m- [39m[38;5;14m[1mBig Data Analytics using R[0m[38;5;12m (https://www.facebook.com/groups/434352233255448/)[39m
+[38;5;12m- [39m[38;5;14m[1mBig Data Analytics with R and Hadoop[0m[38;5;12m (https://www.facebook.com/groups/rhadoop/)[39m
+[38;5;12m- [39m[38;5;14m[1mBig Data Learnings[0m[38;5;12m (https://www.facebook.com/groups/bigdatalearnings/)[39m
+[38;5;12m- [39m[38;5;14m[1mBig Data, Data Science, Data Mining & Statistics[0m[38;5;12m (https://www.facebook.com/groups/bigdatastatistics/)[39m
+[38;5;12m- [39m[38;5;14m[1mBigData/Hadoop Expert[0m[38;5;12m (https://www.facebook.com/groups/BigDataExpert/)[39m
+[38;5;12m- [39m[38;5;14m[1mData Mining / Machine Learning / AI[0m[38;5;12m (https://www.facebook.com/groups/machinelearningforum/)[39m
+[38;5;12m- [39m[38;5;14m[1mData Mining/Big Data - Social Network Ana[0m[38;5;12m (https://www.facebook.com/groups/dataminingsocialnetworks/)[39m
+[38;5;12m- [39m[38;5;14m[1mVademecum of Practical Data Science[0m[38;5;12m (https://www.facebook.com/datasciencevademecum)[39m
+[38;5;12m- [39m[38;5;14m[1mVeri Bilimi Istanbul[0m[38;5;12m (https://www.facebook.com/groups/veribilimiistanbul/)[39m
+[38;5;12m- [39m[38;5;14m[1mThe Data Science Blog[0m[38;5;12m (https://www.facebook.com/theDataScienceBlog/)[39m
+
+
+[38;2;255;187;0m[4mTwitter Accounts[0m
+[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
+
+[38;5;239m│[39m[38;5;12m [39m[38;5;12mTwitter[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m [39m[38;5;12mDescription[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m├[39m[38;5;239m──────────────────────────────────────────────────────────[39m[38;5;239m┼[39m[38;5;239m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────[39m[38;5;239m┤[39m
+[38;5;239m│[39m[38;5;14m[1mBig Data Combine[0m[38;5;12m (https://twitter.com/BigDataCombine)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mRapid-fire, live tryouts for data scientists seeking to monetize their models as trading strategies[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;12mBig Data Mania[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Viz Wiz, Data Journalist, Growth Hacker, Author of Data Science for Dummies (2015)[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mBig Data Science[0m[38;5;12m (https://twitter.com/analyticbridge)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mBig Data, Data Science, Predictive Modeling, Business Analytics, Hadoop, Decision and Operations Research.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;12mCharlie Greenbacker[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mDirector of Data Science at @ExploreAltamira[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mChris Said[0m[38;5;12m (https://twitter.com/Chris_Said)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData scientist at Twitter[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mClare Corthell[0m[38;5;12m (https://twitter.com/clarecorthell)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mDev, Design, Data Science @mattermark #hackerei[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mDADI Charles-Abner[0m[38;5;12m (https://twitter.com/DadiCharles)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m#datascientist @Ekimetrics. , #machinelearning #dataviz #DynamicCharts #Hadoop #R #Python #NLP #Bitcoin #dataenthousiast[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mData Science Central[0m[38;5;12m (https://twitter.com/DataScienceCtrl)[39m[38;5;239m│[39m[38;5;12mData Science Central is the industry's single resource for Big Data practitioners.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mData Science London[0m[38;5;12m (https://twitter.com/ds_ldn)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Science. Big Data. Data Hacks. Data Junkies. Data Startups. Open Data[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mData Science Renee[0m[38;5;12m (https://twitter.com/BecomingDataSci)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mDocumenting my path from SQL Data Analyst pursuing an Engineering Master's Degree to Data Scientist[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mData Science Report[0m[38;5;12m (https://twitter.com/TedOBrien93)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mMission is to help guide & advance careers in Data Science & Analytics[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mData Science Tips[0m[38;5;12m (https://twitter.com/datasciencetips)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mTips and Tricks for Data Scientists around the world! #datascience #bigdata[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mData Vizzard[0m[38;5;12m (https://twitter.com/DataVisualizati)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mDataViz, Security, Military[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mDataScienceX[0m[38;5;12m (https://twitter.com/DataScienceX)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;12mdeeplearning4j[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mDJ Patil[0m[38;5;12m (https://twitter.com/dpatil)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mWhite House Data Chief, VP @ RelateIQ.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mDomino Data Lab[0m[38;5;12m (https://twitter.com/DominoDataLab)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mDrew Conway[0m[38;5;12m (https://twitter.com/drewconway)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData nerd, hacker, student of conflict.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;12mEmilio Ferrara[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m#Networks, #MachineLearning and #DataScience. I work on #Social Media. Postdoc at @IndianaUniv[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mErin Bartolo[0m[38;5;12m (https://twitter.com/erinbartolo)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mRunning with #BigData--enjoying a love/hate relationship with its hype. @iSchoolSU #DataScience Program Mgr.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mGreg Reda[0m[38;5;12m (https://twitter.com/gjreda)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mWorking @ _GrubHub_ about data and pandas[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mGregory Piatetsky[0m[38;5;12m (https://twitter.com/kdnuggets)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mKDnuggets President, Analytics/Big Data/Data Mining/Data Science expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, part-time philosopher.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mHadley Wickham[0m[38;5;12m (https://twitter.com/hadleywickham)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mChief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mHakan Kardas[0m[38;5;12m (https://twitter.com/hakan_kardes)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Scientist[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mHilary Mason[0m[38;5;12m (https://twitter.com/hmason)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Scientist in Residence at @accel.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mJeff Hammerbacher[0m[38;5;12m (https://twitter.com/hackingdata)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mReTweeting about data science[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mJohn Myles White[0m[38;5;12m (https://twitter.com/johnmyleswhite)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mScientist at Facebook and Julia developer. Author of Machine Learning for Hackers and Bandit Algorithms for Website Optimization. Tweets reflect my views only.[39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mJuan Miguel Lavista[0m[38;5;12m (https://twitter.com/BDataScientist)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mPrincipal Data Scientist @ Microsoft Data Science Team[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mJulia Evans[0m[38;5;12m (https://twitter.com/b0rk)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mHacker - Pandas - Data Analyze[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mKenneth Cukier[0m[38;5;12m (https://twitter.com/kncukier)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mThe Economist's Data Editor and co-author of Big Data (http://www.big-data-book.com/).[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;12mKevin Davenport[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mOrganizer of https://www.meetup.com/San-Diego-Data-Science-R-Users-Group/[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mKevin Markham[0m[38;5;12m (https://twitter.com/justmarkham)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData science instructor, and founder of [39m[38;5;14m[1mData School[0m[38;5;12m (https://www.dataschool.io/)[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mKim Rees[0m[38;5;12m (https://twitter.com/krees)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mInteractive data visualization and tools. Data flaneur.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mKirk Borne[0m[38;5;12m (https://twitter.com/KirkDBorne)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mDataScientist, PhD Astrophysicist, Top #BigData Influencer.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;12mLinda Regber[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData storyteller, visualizations.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mLuis Rei[0m[38;5;12m (https://twitter.com/lmrei)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mPhD Student. Programming, Mobile, Web. Artificial Intelligence, Intelligent Robotics Machine Learning, Data Mining, Natural Language Processing, Data Science.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;12mMark Stevenson[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Analytics Recruitment Specialist at Salt (@SaltJobs) Analytics - Insight - Big Data - Data science[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mMatt Harrison[0m[38;5;12m (https://twitter.com/__mharrison__)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mOpinions of full-stack Python guy, author, instructor, currently playing Data Scientist. Occasional fathering, husbanding, organic gardening.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mMatthew Russell[0m[38;5;12m (https://twitter.com/ptwobrussell)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mMining the Social Web.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mMert Nuhoğlu[0m[38;5;12m (https://twitter.com/mertnuhoglu)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData Scientist at BizQualify, Developer[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mMonica Rogati[0m[38;5;12m (https://twitter.com/mrogati)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mData @ Jawbone. Turned data into stories & products at LinkedIn. Text mining, applied machine learning, recommender systems. Ex-gamer, ex-machine coder; namer.[39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mNoah Iliinsky[0m[38;5;12m (https://twitter.com/noahi)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mVisualization & interaction designer. Practical cyclist. Author of vis books: https://www.oreilly.com/pub/au/4419[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mPaul Miller[0m[38;5;12m (https://twitter.com/PaulMiller)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mCloud Computing/ Big Data/ Open Data Analyst & Consultant. Writer, Speaker & Moderator. Gigaom Research Analyst.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mPeter Skomoroch[0m[38;5;12m (https://twitter.com/peteskomoroch)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mCreating intelligent systems to automate tasks & improve decisions. Entrepreneur, ex-Principal Data Scientist @LinkedIn. Machine Learning, ProductRei, Networks[39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mPrash Chan[0m[38;5;12m (https://twitter.com/MDMGeek)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mSolution Architect @ IBM, Master Data Management, Data Quality & Data Governance Blogger. Data Science, Hadoop, Big Data & Cloud.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mQuora Data Science[0m[38;5;12m (https://twitter.com/q_datascience)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mQuora's data science topic[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mR-Bloggers[0m[38;5;12m (https://twitter.com/Rbloggers)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mTweet blog posts from the R blogosphere, data science conferences, and (!) open jobs for data scientists.[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mRand Hindi[0m[38;5;12m (https://twitter.com/randhindi)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12m [39m[38;5;239m│[39m
+[38;5;239m│[39m[38;5;14m[1mRandy Olson[0m[38;5;12m (https://twitter.com/randal_olson)[39m[38;5;12m [39m[38;5;239m│[39m[38;5;12mComputer scientist researching artificial intelligence. Data tinkerer. Community leader for @DataIsBeautiful. #OpenScience advocate.[39m[38;5;12m [39m[38;5;239m│[39m
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+[38;5;12m- [39m[38;5;14m[1mNASDAQ:DATA[0m[38;5;12m (https://data.nasdaq.com/) - Nasdaq Data Link A premier source for financial, economic and alternative datasets.[39m
+[38;5;12m- [39m[38;5;14m[1mfigshare.com[0m[38;5;12m (https://figshare.com/)[39m
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+[38;5;12m- [39m[38;5;14m[1mPublic Big Data Sets[0m[38;5;12m (https://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html)[39m
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+[38;5;12m- [39m[38;5;14m[1mOpen Data Philly[0m[38;5;12m (https://www.opendataphilly.org/) Connecting people with data for Philadelphia[39m
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+[38;5;12m- [39m[38;5;14m[1mUC Irvine Machine Learning Repository[0m[38;5;12m (https://archive.ics.uci.edu/ml/) - contains data sets good for machine learning[39m
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+[38;5;12m- [39m[38;5;14m[1mNational Centers for Environmental Information[0m[38;5;12m (https://www.ncei.noaa.gov/)[39m
+[38;5;12m- [39m[38;5;14m[1mClimateData.us[0m[38;5;12m (https://www.climatedata.us/) (related: [39m[38;5;14m[1mU.S. Climate Resilience Toolkit[0m[38;5;12m (https://toolkit.climate.gov/))[39m
+[38;5;12m- [39m[38;5;14m[1mr/datasets[0m[38;5;12m (https://www.reddit.com/r/datasets/)[39m
+[38;5;12m- [39m[38;5;14m[1mMapLight[0m[38;5;12m (https://www.maplight.org/data-series) - provides a variety of data free of charge for uses that are freely available to the general public. Click on a data set below to learn more[39m
+[38;5;12m- [39m[38;5;14m[1mGHDx[0m[38;5;12m (https://ghdx.healthdata.org/) - Institute for Health Metrics and Evaluation - a catalog of health and demographic datasets from around the world and including IHME results[39m
+[38;5;12m- [39m[38;5;14m[1mSt. Louis Federal Reserve Economic Data - FRED[0m[38;5;12m (https://fred.stlouisfed.org/)[39m
+[38;5;12m- [39m[38;5;14m[1mNew Zealand Institute of Economic Research – Data1850[0m[38;5;12m (https://data1850.nz/)[39m
+[38;5;12m- [39m[38;5;14m[1mOpen Data Sources[0m[38;5;12m (https://github.com/datasciencemasters/data)[39m
+[38;5;12m- [39m[38;5;14m[1mUNICEF Data[0m[38;5;12m (https://data.unicef.org/)[39m
+[38;5;12m- [39m[38;5;14m[1mundata[0m[38;5;12m (https://data.un.org/)[39m
+[38;5;12m- [39m[38;5;14m[1mNASA SocioEconomic Data and Applications Center - SEDAC[0m[38;5;12m (https://sedac.ciesin.columbia.edu/)[39m
+[38;5;12m- [39m[38;5;14m[1mThe GDELT Project[0m[38;5;12m (https://www.gdeltproject.org/)[39m
+[38;5;12m- [39m[38;5;14m[1mSweden, Statistics[0m[38;5;12m (https://www.scb.se/en/)[39m
+[38;5;12m- [39m[38;5;14m[1mStackExchange Data Explorer[0m[38;5;12m (https://data.stackexchange.com) - an open source tool for running arbitrary queries against public data from the Stack Exchange network.[39m
+[38;5;12m- [39m[38;5;14m[1mSocialGrep[0m[38;5;12m (https://socialgrep.com/datasets) - a collection of open Reddit datasets.[39m
+[38;5;12m- [39m[38;5;14m[1mSan Fransisco Government Open Data[0m[38;5;12m (https://datasf.org/opendata/)[39m
+[38;5;12m- [39m[38;5;14m[1mIBM Asset Dataset[0m[38;5;12m (https://developer.ibm.com/exchanges/data/)[39m
+[38;5;12m- [39m[38;5;14m[1mOpen data Index[0m[38;5;12m (https://index.okfn.org/)[39m
+[38;5;12m- [39m[38;5;14m[1mPublic Git Archive[0m[38;5;12m (https://github.com/src-d/datasets/tree/master/PublicGitArchive)[39m
+[38;5;12m- [39m[38;5;14m[1mGHTorrent[0m[38;5;12m (https://ghtorrent.org/)[39m
+[38;5;12m- [39m[38;5;14m[1mMicrosoft Research Open Data[0m[38;5;12m (https://msropendata.com/)[39m
+[38;5;12m- [39m[38;5;14m[1mOpen Government Data Platform India[0m[38;5;12m (https://data.gov.in/)[39m
+[38;5;12m- [39m[38;5;14m[1mGoogle Dataset Search (beta)[0m[38;5;12m (https://datasetsearch.research.google.com/)[39m
+[38;5;12m- [39m[38;5;14m[1mNAYN.CO Turkish News with categories[0m[38;5;12m (https://github.com/naynco/nayn.data)[39m
+[38;5;12m- [39m[38;5;14m[1mCovid-19[0m[38;5;12m (https://github.com/datasets/covid-19)[39m
+[38;5;12m- [39m[38;5;14m[1mCovid-19 Google[0m[38;5;12m (https://github.com/google-research/open-covid-19-data)[39m
+[38;5;12m- [39m[38;5;14m[1mEnron Email Dataset[0m[38;5;12m (https://www.cs.cmu.edu/~./enron/)[39m
+[38;5;12m- [39m[38;5;14m[1m5000 Images of Clothes[0m[38;5;12m (https://github.com/alexeygrigorev/clothing-dataset)[39m
+[38;5;12m- [39m[38;5;14m[1mIBB Open Portal[0m[38;5;12m (https://data.ibb.gov.tr/en/)[39m
+[38;5;12m- [39m[38;5;14m[1mThe Humanitarian Data Exchange[0m[38;5;12m (https://data.humdata.org/)[39m
+
+[38;2;255;187;0m[4mComics[0m
+[48;5;235m[38;5;249m^ back to top ^[49m[39m[38;5;14m[1m (#awesome-data-science)[0m
+
+[38;5;12m- [39m[38;5;14m[1mComic compilation[0m[38;5;12m (https://medium.com/@nikhil_garg/a-compilation-of-comics-explaining-statistics-data-science-and-machine-learning-eeefbae91277)[39m
+[38;5;12m- [39m[38;5;14m[1mCartoons[0m[38;5;12m (https://www.kdnuggets.com/websites/cartoons.html)[39m
+
+[38;2;255;187;0m[4mOther Awesome Lists[0m
+
+[38;5;12m- Other amazingly awesome lists can be found in the [39m[38;5;14m[1mawesome-awesomeness[0m[38;5;12m (https://github.com/bayandin/awesome-awesomeness)[39m
+[38;5;12m- [39m[38;5;14m[1mAwesome Machine Learning[0m[38;5;12m (https://github.com/josephmisiti/awesome-machine-learning)[39m
+[38;5;12m- [39m[38;5;14m[1mlists[0m[38;5;12m (https://github.com/jnv/lists)[39m
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+[38;5;12m- [39m[38;5;14m[1mawesome-r[0m[38;5;12m (https://github.com/qinwf/awesome-R)[39m
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+[38;5;12m- [39m[38;5;14m[1mawesome-Machine Learning & Deep Learning Tutorials[0m[38;5;12m (https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md)[39m
+[38;5;12m- [39m[38;5;14m[1mAwesome Data Science Ideas[0m[38;5;12m (https://github.com/JosPolfliet/awesome-ai-usecases)[39m
+[38;5;12m- [39m[38;5;14m[1mMachine Learning for Software Engineers[0m[38;5;12m (https://github.com/ZuzooVn/machine-learning-for-software-engineers)[39m
+[38;5;12m- [39m[38;5;14m[1mCommunity Curated Data Science Resources[0m[38;5;12m (https://hackr.io/tutorials/learn-data-science)[39m
+[38;5;12m- [39m[38;5;14m[1mAwesome Machine Learning On Source Code[0m[38;5;12m (https://github.com/src-d/awesome-machine-learning-on-source-code)[39m
+[38;5;12m- [39m[38;5;14m[1mAwesome Community Detection[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-community-detection)[39m
+[38;5;12m- [39m[38;5;14m[1mAwesome Graph Classification[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-graph-classification)[39m
+[38;5;12m- [39m[38;5;14m[1mAwesome Decision Tree Papers[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-decision-tree-papers)[39m
+[38;5;12m- [39m[38;5;14m[1mAwesome Fraud Detection Papers[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-fraud-detection-papers)[39m
+[38;5;12m- [39m[38;5;14m[1mAwesome Gradient Boosting Papers[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers)[39m
+[38;5;12m- [39m[38;5;14m[1mAwesome Computer Vision Models[0m[38;5;12m (https://github.com/nerox8664/awesome-computer-vision-models)[39m
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+[38;5;12m- [39m[38;5;14m[1mGlossary of common statistics and ML terms[0m[38;5;12m (https://www.analyticsvidhya.com/glossary-of-common-statistics-and-machine-learning-terms/)[39m
+[38;5;12m- [39m[38;5;14m[1m100 NLP Papers[0m[38;5;12m (https://github.com/mhagiwara/100-nlp-papers)[39m
+[38;5;12m- [39m[38;5;14m[1mAwesome Game Datasets[0m[38;5;12m (https://github.com/leomaurodesenv/game-datasets#readme)[39m
+[38;5;12m- [39m[38;5;14m[1mData Science Interviews Questions[0m[38;5;12m (https://github.com/alexeygrigorev/data-science-interviews)[39m
+[38;5;12m- [39m[38;5;14m[1mAwesome Explainable Graph Reasoning[0m[38;5;12m (https://github.com/AstraZeneca/awesome-explainable-graph-reasoning)[39m
+[38;5;12m- [39m[38;5;14m[1mTop Data Science Interview Questions[0m[38;5;12m (https://www.interviewbit.com/data-science-interview-questions/)[39m
+[38;5;12m- [39m[38;5;14m[1mAwesome Drug Synergy, Interaction and Polypharmacy Prediction[0m[38;5;12m (https://github.com/AstraZeneca/awesome-drug-pair-scoring)[39m
+[38;5;12m- [39m[38;5;14m[1mDeep Learning Interview Questions[0m[38;5;12m (https://www.adaface.com/blog/deep-learning-interview-questions/)[39m
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+[38;5;12m- [39m[38;5;14m[1mHow Generative AI Is Changing Creative Work[0m[38;5;12m (https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work)[39m
+[38;5;12m- [39m[38;5;14m[1mWhat is generative AI?[0m[38;5;12m (https://www.techtarget.com/searchenterpriseai/definition/generative-AI)[39m
+
+[38;2;255;187;0m[4mHobby[0m
+[38;5;12m- [39m[38;5;14m[1mAwesome Music Production[0m[38;5;12m (https://github.com/ad-si/awesome-music-production)[39m
+
+
+
+
+[38;5;12m window.dataLayer = window.dataLayer || [39m[38;5;12m ;[39m
+[38;5;12m function gtag(){dataLayer.push(arguments);}[39m
+[38;5;12m gtag('js', new Date());[39m
+
+[38;5;12m gtag('config', 'G-YL0RV0E5XZ');[39m
-[38;5;12mReal-time visualization library - https://github.com/fastly/epoch[39m
diff --git a/terminal/index b/terminal/index
index 62795e3..765985b 100644
--- a/terminal/index
+++ b/terminal/index
@@ -20,7 +20,7 @@
[48;5;235m[38;5;249m| |[49m[39m[48;5;235m[38;5;249m [49m[39m
[48;5;235m[38;5;249m+ ------------------------------------------ THANKS ------------------------------------------------ +[49m[39m[48;5;235m[38;5;249m [49m[39m
[48;5;235m[38;5;249m| |[49m[39m[48;5;235m[38;5;249m [49m[39m
-[48;5;235m[38;5;249m| List of awesome pages collected from awesome-awesome-awesome awesome page. |[49m[39m[48;5;235m[38;5;249m [49m[39m
+[48;5;235m[38;5;249m| List of awesome pages collected from awesome-awesome-awesome ^1 awesome page. |[49m[39m[48;5;235m[38;5;249m [49m[39m
[48;5;235m[38;5;249m| |[49m[39m[48;5;235m[38;5;249m [49m[39m
[48;5;235m[38;5;249m| Big shoutout to @t3chnoboy and @sindresorhus for their meta meta (meta) awesome pages! |[49m[39m[48;5;235m[38;5;249m [49m[39m
[48;5;235m[38;5;249m| Also to @bradoyler, @emirjp, @erichs, @oyvindrobertsen, @bayandin, @jnv and @scooperma for their |[49m[39m[48;5;235m[38;5;249m [49m[39m
@@ -28,12 +28,12 @@
[48;5;235m[38;5;249m| |[49m[39m[48;5;235m[38;5;249m [49m[39m
[48;5;235m[38;5;249m| And of course to all the people curating such awesome link lists! You are awesome :) |[49m[39m[48;5;235m[38;5;249m [49m[39m
[48;5;235m[38;5;249m| |[49m[39m[48;5;235m[38;5;249m [49m[39m
-[48;5;235m[38;5;249m| Highly inspired by cheat.sh . Give it a try. It's awesome too! |[49m[39m[48;5;235m[38;5;249m [49m[39m
+[48;5;235m[38;5;249m| Highly inspired by cheat.sh ^2. Give it a try. It's awesome too! |[49m[39m[48;5;235m[38;5;249m [49m[39m
[48;5;235m[38;5;249m| |[49m[39m[48;5;235m[38;5;249m [49m[39m
[48;5;235m[38;5;249m+ ------------------------------------------ LINKS ------------------------------------------------- +[49m[39m[48;5;235m[38;5;249m [49m[39m
[48;5;235m[38;5;249m| |[49m[39m[48;5;235m[38;5;249m [49m[39m
-[48;5;235m[38;5;249m| https://github.com/t3chnoboy/awesome-awesome-awesome |[49m[39m[48;5;235m[38;5;249m [49m[39m
-[48;5;235m[38;5;249m| https://cheat.sh |[49m[39m[48;5;235m[38;5;249m [49m[39m
+[48;5;235m[38;5;249m| ^1 https://github.com/t3chnoboy/awesome-awesome-awesome |[49m[39m[48;5;235m[38;5;249m [49m[39m
+[48;5;235m[38;5;249m| ^2 https://cheat.sh |[49m[39m[48;5;235m[38;5;249m [49m[39m
[48;5;235m[38;5;249m| |[49m[39m[48;5;235m[38;5;249m [49m[39m
[48;5;235m[38;5;249m+====================================================================================================+[49m[39m[48;5;235m[38;5;249m [49m[39m