# 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.