📚 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 or not).

This list will be continuously updated.

📊 Statistics, Probability and the science of Data

• C Bergstrom, J West, Calling Bullshit in the age of Big Data, a 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, a nice little book on the common mistakes and misunderstandings around the use of stats. Old (1954), but very valuable and entertaining. Note: some of the examples it provides can be perceived as sexist and out of place today, but I think that old material needs to always be contextualised to be enjoyed.

• T Vigen, Spurious Correlations, visually displays how completely unrelated variables can be correlated, to illustrate the old adage that correlation is not causation. This site is a favourite within the data community.

• [book] A B Downey, Think Stats (O'Reilly), book, freely available online

• Seeing Theory, a visual introduction to probability and statistics, a site built by students at Brown University.

• W Chen, probability cheatsheet.

• Scipy lecture notes - they're pretty brilliant and obviously focussed on Python, but you can learn general concepts.

🤖 Machine Learning - general material

• S Yee, T Chu, R2D3, a visual introduction to Machine Learning

• V Powell, L Lehe, Explained Visually, another visual site

• [book] C Molnar, Interpretable Machine Learning, book, freely available

• [book] T Hastie, R Tibshirani, J Friedman, The Elements of Statistical Learning (Springer), freely available in PDF but you can also buy it in print

• [book] G James, D Witten, T Hastie, R Tibshirani, Introduction to Statistical Learning, a more high-level book by some of the same authors of the above. Again, freely available as PDF or you can also buy it in print. It exists in versions with R and Python code examples (the latter adds J Taylor as author).

• scikit-learn has tutorials and extensive explanations for every supported algorithm as well as general notes on Machine Learning concepts.

🧠 Neural Networks

• [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

👀 Computer Vision

• The Hypermedia Image Processing Reference, a website built by the University of Edinburgh, School of Informatics

• Pyimagesearch, a website curated by A Rosebrock on Computer Vision and Machine/Deep Learning. Rosebrock started the project years ago and built lots of valuable tutorials that used to be free. Since then, the author upgraded the project into a business so these days you can purchase courses.

💻 Coding and Computer Science

• Sorting Algorithms interactive visualizations, by Toptal

• Practical Business Python, a website By C Moffitt devoted to best practices on using Python for practical reasons, it's very good

• [book] Gayle Laakmann McDowell, Cracking the Coding Interview (CareerCup) - this is a good general resource not just to prepare for interviews but for general challenges

🐍 Python

• [book] The Hitchhiker's guide to Python - it is a book published by O’Reilly but also freely available as a guide; it i focused on best practices to create software in Python