101 lines
7.1 KiB
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101 lines
7.1 KiB
Plaintext
# Data Science Tutorials & Resources for Beginners [](https://github.com/sindresorhus/awesome)
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*If you want to know more about Data Science but don't know where to start this list is for you!* :chart_with_upwards_trend:
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No previous knowledge is required but Python and statistics basics will definitely come in handy. These resources have been used successfully for many beginners at my local Data Science student group [ML-KA](http://ml-ka.de/).
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## What is Data Science?
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- ['What is Data Science?' on Quora](https://www.quora.com/What-is-data-science)
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- [Explanation of important vocabulary](https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1?share=1) - Differentiation of Big Data, Machine Learning, Data Science.
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- [Data Science for Business (Book)](https://amzn.to/2voPJUi) - An introduction to Data Science and its use as a business asset.
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- [Data Science Process: A Beginner’s Comprehensive Guide](https://www.scaler.com/blog/data-science-process/) - Technical Skills for the Data Science: This emphasizes the practical skills needed throughout the data science process.
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## Common Algorithms and Procedures
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- [Supervised vs unsupervised learning](https://stackoverflow.com/questions/1832076/what-is-the-difference-between-supervised-learning-and-unsupervised-learning) - The two most common types of Machine Learning algorithms.
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- [9 important Data Science algorithms and their implementation](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb)
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- [Cross validation](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb) - Evaluate the performance of your algorithm/model.
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- [Feature engineering](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.04-Feature-Engineering.ipynb) - Modifying the data to better model predictions.
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- [Scientific introduction to 10 important Data Science algorithms](http://www.cs.umd.edu/%7Esamir/498/10Algorithms-08.pdf)
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- [Model ensemble: Explanation](https://www.analyticsvidhya.com/blog/2017/02/introduction-to-ensembling-along-with-implementation-in-r/) - Combine multiple models into one for better performance.
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## Data Science using Python
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This list covers only Python, as many are already familiar with this language. [Data Science tutorials using R](https://github.com/ujjwalkarn/DataScienceR).
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### General
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- [O'Reilly Data Science from Scratch (Book)](https://amzn.to/2GSjjrK) - Data processing, implementation, and visualization with example code.
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- [Coursera Applied Data Science](https://www.coursera.org/specializations/data-science-python) - Online Course using Python that covers most of the relevant toolkits.
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### Learning Python
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- [YouTube tutorial series by sentdex](https://www.youtube.com/watch?v=oVp1vrfL_w4&list=PLQVvvaa0QuDe8XSftW-RAxdo6OmaeL85M)
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- [Interactive Python tutorial website](http://www.learnpython.org/)
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### numpy
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[numpy](http://www.numpy.org/) is a Python library which provides large multidimensional arrays and fast mathematical operations on them.
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- [Numpy tutorial on DataCamp](https://www.datacamp.com/community/tutorials/python-numpy-tutorial#gs.h3DvLnk)
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### pandas
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[pandas](http://pandas.pydata.org/index.html) provides efficient data structures and analysis tools for Python. It is build on top of numpy.
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- [Introduction to pandas](http://www.synesthesiam.com/posts/an-introduction-to-pandas.html)
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- [DataCamp pandas foundations](https://www.datacamp.com/courses/pandas-foundations) - Paid course, but 30 free days upon account creation (enough to complete course).
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- [Pandas cheatsheet](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf) - Quick overview over the most important functions.
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### scikit-learn
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[scikit-learn](http://scikit-learn.org/stable/) is the most common library for Machine Learning and Data Science in Python.
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- [Introduction and first model application](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb)
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- [Rough guide for choosing estimators](http://scikit-learn.org/stable/tutorial/machine_learning_map/)
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- [Scikit-learn complete user guide](http://scikit-learn.org/stable/user_guide.html)
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- [Model ensemble: Implementation in Python](http://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/)
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### Jupyter Notebook
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[Jupyter Notebook](https://jupyter.org/) is a web application for easy data visualisation and code presentation.
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- [Downloading and running first Jupyter notebook](https://jupyter.org/install.html)
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- [Example notebook for data exploration](https://www.kaggle.com/sudalairajkumar/simple-exploration-notebook-instacart)
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- [Seaborn data visualization tutorial](https://elitedatascience.com/python-seaborn-tutorial) - Plot library that works great with Jupyter.
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### Various other helpful tools and resources
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- [Template folder structure for organizing Data Science projects](https://github.com/drivendata/cookiecutter-data-science)
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- [Anaconda Python distribution](https://www.continuum.io/downloads) - Contains most of the important Python packages for Data Science.
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- [Spacy](https://spacy.io/) - Open source toolkit for working with text-based data.
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- [LightGBM gradient boosting framework](https://github.com/Microsoft/LightGBM) - Successfully used in many Kaggle challenges.
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- [Amazon AWS](https://aws.amazon.com/) - Rent cloud servers for more timeconsuming calculations (r4.xlarge server is a good place to start).
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## Data Science Challenges for Beginners
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Sorted by increasing complexity.
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- [Walkthrough: House prices challenge](https://www.dataquest.io/blog/kaggle-getting-started/) - Walkthrough through a simple challenge on house prices.
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- [Blood Donation Challenge](https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/) - Predict if a donor will donate again.
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- [Titanic Challenge](https://www.kaggle.com/c/titanic) - Predict survival on the Titanic.
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- [Water Pump Challenge](https://www.drivendata.org/competitions/7/pump-it-up-data-mining-the-water-table/) - Predict the operating condition of water pumps in Africa.
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## More advanced resources and lists
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- [Awesome Data Science](https://github.com/bulutyazilim/awesome-datascience)
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- [Data Science Python](https://github.com/ujjwalkarn/DataSciencePython)
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- [Machine Learning Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials)
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## Contribute
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Contributions welcome! Read the [contribution guidelines](contributing.md) first.
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## License
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[](http://creativecommons.org/publicdomain/zero/1.0)
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To the extent possible under law, Simon Böhm has waived all copyright and
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related or neighboring rights to this work. Disclaimer: Some of the links are affiliate links.
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[learndatascience.md Github](https://github.com/siboehm/awesome-learn-datascience
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)
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