The books I included in this article cover enough topics for a complete beginner to learn all the statistics needed for data science. The book is most suited to those who have already covered the basics of statistics for data analysis and are familiar with some statistical notation. Each explanation is in-depth and uses practical examples such as the classification of spam data which makes quite complex ideas easier to digest. The second half of the book, which covers machine learning algorithms, is some of the best material I have seen on this subject. It also gives a thorough introduction to both Bayesian and Frequentist statistical inference methodologies. This book covers the theory behind most of the popular machine learning algorithms used by data scientists today. Suitable for: Someone with a basic understanding of statistics and statistical notation. It is, therefore, an excellent hands-on introduction to this subject. There are coded examples throughout and the Github repository, where the chapters are hosted, contains a large selection of notebooks.

This book aimed firmly at programmers (so some Python is a prerequisite), is the only material I have found that explains these concepts in a simple enough way for a non-statistician to understand.

If you are building a machine learning model, for example, you will need to be able to understand the uncertainty around the predictions that your model is delivering.īayesian methods can be quite abstract and difficult to understand. As a data scientist uncertainty is something you will need to model on a very regular basis. Suitable for: Non-statisticians with a working knowledge of Python.īayesian inference is a branch of statistics that deals with understanding uncertainty.

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