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 AWESOME DATA SCIENCE
 AWESOME DATA SCIENCE
!Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome) 
@@ -69,69 +69,56 @@
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.
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 │_Data scientists today are akin to Wall Street “quants” of the 1980s and 1990s. In those days people  │
│ (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century) │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 │_Data scientists are big data wranglers, gathering and analyzing large sets of structured and  │
│ (https://www.mastersindatascience.org/careers/data-scientist/) │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  │
│(https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science│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  │
│ (https://www.rstudio.com/blog/software-development-resources-for-data-scientists/) │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._ │
│ 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 │_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 │
│ (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century) │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 │_Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines  │
│ (https://www.mastersindatascience.org/careers/data-scientist/) │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  │
│(https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science│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│
│ (https://www.rstudio.com/blog/software-development-resources-for-data-scientists/) │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.
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. 
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.
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!
 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)
@@ -141,17 +128,16 @@
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). 
- 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).
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
@@ -179,25 +165,21 @@
- 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/)
- 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...)
- 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.
- 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.
- 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
@@ -235,8 +217,7 @@
- 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.
- 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/)
@@ -502,158 +483,140 @@
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 │Template repository for data science lifecycle project │
│ (https://github.com/dslp/dslp-repo-template) │ │
│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 │Representation learning on dynamic graphs. │
│ (https://github.com/benedekrozemberczki/pytorch_geometric_temporal) │ │
│Little Ball of Fur │A graph sampling library for NetworkX with a Scikit-Learn like API. │
│ (https://github.com/benedekrozemberczki/littleballoffur) │ │
│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 │is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials. │
│ (https://www.cloudera.com/downloads/hortonworks-sandbox.html) │ │
│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 │Coursera Course │
│ (https://www.coursera.org/learn/data-scientists-tools) │ │
│Data Science Toolbox (https://datasciencetoolbox.org/) │Blog │
│Wolfram Data Science Platform │Take numerical, textual, image, GIS or other data and give it the Wolfram treatment, carrying out a full spectrum  │
│ (https://www.wolfram.com/data-science-platform/) │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 │Intel® Nervana™ reference deep learning framework committed to best performance on all hardware. │
│ (https://github.com/NervanaSystems/neon) │ │
│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 │Python Data Science Handbook: full text in Jupyter Notebooks │
│ (https://github.com/jakevdp/PythonDataScienceHandbook) │ │
│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 │A search engine 🔎 tool to discover & find a curated list of popular & new libraries, top authors, trending project│
│ (https://kandi.openweaver.com/explore/data-science) │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 │
│ 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
@@ -740,8 +703,7 @@
- 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
- 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)
@@ -761,8 +723,7 @@
- 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.) 
- 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
@@ -790,8 +751,7 @@
- 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.
- 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.
@@ -810,8 +770,7 @@
- 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.
- 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
@@ -988,91 +947,81 @@
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 │
│ 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.
- 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.
@@ -1106,30 +1055,25 @@
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│Different Data Science Skills and Roles from this article  │
│.png) │(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/). │
│ 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)
@@ -1159,8 +1103,7 @@
- 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)
- 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/)