Awesome Material

📚 You can learn a lot for free on the Internet. This page puts together resources on data, data science and related fields which I find absolutely brilliant. I also list some awesome books (I will specify if they are freely available).

This list is continuously updated.

📊 Statistics, Probability and the science of Data

• C Bergstrom, J West, Calling Bullshit in the age of Big Data, course about the manipulative use of data and the wrong use of statistics. The authors have also published a book.
• [book] D Huff, How to lie with Statistics (1954), nice little book on the common mistakes and misunderstandings aroud data. Old but very valuable and entertaining. Note: some of the examples used can be perceived as sexist and out of place today, so keep in mind to contextualise to the 1950s.
• T Vigen, Spurious Correlations, visually displays correlations between completely unrelated variables, to illustrate the old adage that correlation is not causation. A favourite within the data community.
• [book] A B Downey, Think Stats (O’Reilly 2011), freely available online.
• Seeing Theory, a visual introduction to probability and statistics, a site built by students at Brown University.
• W Chen, Probability Cheatsheet.
• The Scipy Lecture Notes, brilliant and obviously focussed on Python, but useful for general concepts too.
• [book] W Mckinney, Python for Data Analysis (O’Reilly 2022), freely available online.

🤖 Machine Learning - general material

• S Yee, T Chu, R2D3, visual intro to Machine Learning.
• V Powell, L Lehe, Explained Visually, another visual site.
• [book] C Molnar, Interpretable Machine Learning (Leanpub 2019), freely available.
• [book] T Hastie, R Tibshirani, J Friedman, The Elements of Statistical Learning (Springer 2001), freely available.
• [book], G James, D Witten, T Hastie, R Tibshirani, Introduction to Statistical Learning (Springer 2013), a more high-level book by some of the same authors of the above. Again, freely available - it exists in versions with R and Python code examples (the latter adds J Taylor as author).
• The scikit-learn docs have tutorials and extensive explanations for every supported algorithm as well as general notes on Machine Learning concepts.
• MLU-Explain, a website by Amazon that also presents ML concepts in a visual way

🧠 Neural Networks & Deep Learning

• [book] M Nielsen, Neural Networks & Deep Learning(Determination Press 2015), a fantastic online book, free.
• V Maggio, Deep Learning with Keras & Tensorflow, a set of tutorials in Jupyter notebooks.
• [book] F Chollet, Deep Learning with Python (Manning 2017), a book by the creator of Keras (not free).
• The TensorFlow Neural Network playground, an interactive tool to visualise the inner workings of ANNs.
• Practical Deep Learning for Coders, a fast.ai course, with annexed book (freely available) and more material, see all at the link

👀 Computer Vision

• The Hypermedia Image Processing Reference, website built by the University of Edinburgh, School of Informatics.
• A Rosebrock, Pyimagesearch, website with tutorials and explanations on Computer Vision and Machine/Deep Learning. Material now for purchase.

💻 Coding and Computer Science

• Sorting Algorithms, Toptal.
• [book] Gayle Laakmann McDowell, Cracking the Coding Interview (CareerCup 2008), general resource not just to prepare for interviews but for general challenges.

🐍 Python

These resources are general to Python, regardless of its use for data science.

• C Moffitt, Practical Business Python, website devoted to best practices on using Python for practical reasons.
• [book] K Reitz, T Schlusser, The Hitchhiker’s guide to Python (O’Reilly 2016), freely available.