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https://github.com/rebecca-vickery/data-science-learning-resources

A comprehensive list of free resources for learning data science
https://github.com/rebecca-vickery/data-science-learning-resources

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A comprehensive list of free resources for learning data science

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README 

          
# Data Science Learning Resources



A comprehensive list of free resources for learning data science.



## Python



### Courses/Tutorials



* **[Datacamp](https://learn.datacamp.com/courses)** selected free courses. 

* **[Dataquest](https://www.dataquest.io/)** free trial. 

* A really good **[tutorial on OOP for data science](https://opendatascience.com/an-introduction-to-object-oriented-data-science-in-python/)**.

* **[CS50x Harvard Introduction to Computer Science](https://cs50.harvard.edu/x/2020/)**.

* **[https://realpython.com/](https://cs50.harvard.edu/x/2020/)**.

* **[Pandas basics](https://pandasguide.readthedocs.io/en/latest/Pandas/basic.html)**.



### Books



* **[The Hitchhiker's Guide to Python](https://docs.python-guide.org)**. 

* **[Automate the Boring Stuff with Python](https://automatetheboringstuff.com/2e/chapter1/)**. 

* **[Python for Everybody](https://www.py4e.com/book.php)**. 

* **[Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)**.

* **[Python for Data Analysis](https://bedford-computing.co.uk/learning/wp-content/uploads/2015/10/Python-for-Data-Analysis.pdf)**. 



## Data science general



### Courses/Tutorials



* **[Microsoft course Data-science-for-beginners](https://github.com/microsoft/Data-Science-For-Beginners)**.



## Machine Learning



### Courses/Tutorials



* **[Google's machine learning crash course](https://developers.google.com/machine-learning/crash-course/ml-intro)**. 

* **[Scikit-learn workshop](https://github.com/amueller/ml-workshop-1-of-4)** material by Andreas Mueller, core contributor to Scikit-learn.

* **[Applied machine Learning](https://github.com/amueller/COMS4995-s19)** material from Columbia University. 

* **[Machine learning with python](https://github.com/tirthajyoti/Machine-Learning-with-Python)** github repo with numerous tutorials. 

* **[Notes on data science & machine learning](https://chrisalbon.com)** by Chris Albon.

* **[Machine learning (theory) flashcards](https://github.com/gmaclenn/ml-flashcards-python/tree/master/flashcards)** by Chris Albon. 

* **[Introduction to Machine Learning with Scikit-learn](https://courses.dataschool.io/introduction-to-machine-learning-with-scikit-learn)**.

* **[Kaggle Machine Learning Explainability](https://www.kaggle.com/learn/machine-learning-explainability)**.

* **[Scikit-learn Course](https://inria.github.io/scikit-learn-mooc/ml_concepts/slides.html)**.

* **[Microsoft course ML-for-beginners](https://github.com/microsoft/ML-For-Beginners)**.



### Books

* **[Natural Language Processing with Python](http://www.nltk.org/book_1ed/)**. 

* **[Hands on Machine Learning with Scikit-learn and Tensorflow](http://index-of.es/Varios-2/Hands%20on%20Machine%20Learning%20with%20Scikit%20Learn%20and%20Tensorflow.pdf)**.

* **[A curated list of widely cited papers on machine learning](https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI)**.

* **[Introduction to Machine Learning with Python](http://noracook.io/Books/Python/introductiontomachinelearningwithpython.pdf)**. 



## Natural Language Processing



### Courses/Tutorials



* **[Introduction to Natural Language processing](https://courses.analyticsvidhya.com/courses/Intro-to-NLP)**.

* **[Awesome NLP](https://github.com/keon/awesome-nlp)** curated list of tutorials and articles.



### Books



* **[Introduction to Natural Language Processing](https://london.ac.uk/sites/default/files/study-guides/introduction-to-natural-language-processing.pdf)**

* **[Natural Language Processing with Python](https://www.nltk.org/book/)**



## Deep Learning



### Courses/Tutorials



* **[FastAI](https://course.fast.ai)** practical deep learning for coders.

* **[Scaler Topics](https://www.scaler.com/topics/what-is-deep-learning/)** Deep learning.



### Books



* **[Deep Learning](https://www.deeplearningbook.org)**.



## Maths & Statistics



### Courses/Tutorials



* **[From 0 to Research Scientist Resource Guide](https://github.com/ahmedbahaaeldin/From-0-to-Research-Scientist-resources-guide)**.

* **[Khan Academy Statistics and Probability](https://www.khanacademy.org/math/statistics-probability)**.

* **[Khan Academy Linear Algebra](https://www.khanacademy.org/math/linear-algebra)**.

* **[Khan Academy Calculus](https://www.khanacademy.org/math/calculus-1)**.

* 



### Books



* **[Practical Statistics for Data Scientists](https://github.com/Chandra0505/Data-Science-Resources/blob/master/machine-learning/Practical%20Statistics%20for%20Data%20Scientists.pdf)**. 

* **[Think Stats](https://greenteapress.com/thinkstats/)**. 

* **[Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)**. 

* **[Statistics in Plain English](https://www.book2look.com/embed/9781317526988)**. 

* **[Computer Age Statistical Inference](https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf)**.



## Data Engineering



* **[Machine learning system design - data engineering](https://docs.google.com/document/d/1b9iuZiDEGVLHyMmnf6w2y1aN6yWQhAyqk3GHlpI9q6M/edit#heading=h.a8w2b79yy875)**, Stanford lecture notes by Chip Huyen.



## Data Science Libraries



* **[Curated list of Python libraries for data science](https://github.com/krzjoa/awesome-python-data-science)**.



## Code Helpers



* **[Quickly find commonly used code snippets with codegrepper](https://www.codegrepper.com/code-examples/python)**.



## Code Practice



* **[Leetcode](https://leetcode.com/)**.



# Misc



* **[Mac setup guide](https://sourabhbajaj.com/mac-setup/)**.