Books

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

Statistics and Statistical Learning
An Introduction to Statistical Learning with Applications in R
4.6 (881 Ratings)

An Introduction to Statistical Learning with Applications in R

Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani, 2013

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
Artificial Intelligence A Modern Approach, 1st Edition
4.1 (464 Ratings)

Artificial Intelligence A Modern Approach, 1st Edition

Stuart Russell, 1995

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
Interactive Data Visualization for the Web
4.1 (410 Ratings)

Interactive Data Visualization for the Web

Scott Murray, 2013

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
Information Theory, Inference, and Learning Algorithms
4.5 (310 Ratings)

Information Theory, Inference, and Learning Algorithms

David J.C. MacKay, 2005

"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
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
4.2 (290 Ratings)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Trevor Hastie, Robert Tibshirani, & Jerome Friedman, 2008

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
Reinforcement Learning: An Introduction
4.4 (232 Ratings)

Reinforcement Learning: An Introduction

Richard S. Sutton & Andrew G. Barto, 2012

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
Data Mining: Practical Machine Learning Tools and Techniques
4.0 (219 Ratings)

Data Mining: Practical Machine Learning Tools and Techniques

Ian H. Witten & Eibe Frank, 2005

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
Think Stats: Exploratory Data Analysis in Python
Languages: Python
3.6 (214 Ratings)

Think Stats: Exploratory Data Analysis in Python

Allen B. Downey, 2014

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

Data Mining and Machine Learning
Bayesian Reasoning and Machine Learning
3.9 (139 Ratings)

Bayesian Reasoning and Machine Learning

David Barber, 2014

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
OpenIntro Statistics
4.5 (93 Ratings)

OpenIntro Statistics

David M Diez, Christopher D Barr, & Mine Çetinkaya-Rundel, 2015

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

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