The best books on Data Science, Big Data, Data Mining, Machine Learning, Python, R, SQL, NoSQL and more.

Statistics and Statistical Learning

This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, and much more.

Artificial Intelligence

Comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.

Data Visualization

Create and publish your own interactive data visualization projects on the Web—even if you have little or no experience with data visualization or web development. It’s easy and fun with this practical, hands-on introduction.

Data Mining and Machine Learning

"Essential reading for students of electrical engineering and computer science; also a great heads-up for mathematics students concerning the subtlety of many commonsense questions." Choice

Statistics and Statistical Learning

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.

Data Mining and Machine Learning

A clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.

Data Mining and Machine Learning

Offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations.

Statistics and Statistical Learning

This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

Data Mining and Machine Learning

For final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models.

Statistics and Statistical Learning

Probability is optional, inference is key, and we feature real data whenever possible. Files for the entire book are freely available at openintro.org.