Books

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

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4.4 (6616 Ratings)

The Visual Display of Quantitative Information

Edward R. Tufte

Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis.

Artificial Intelligence
Artificial Intelligence A Modern Approach, 1st Edition
4.2 (586 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.

Statistics
Think Stats: Exploratory Data Analysis in Python
Languages: Python
3.6 (296 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.

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4.3 (278 Ratings)

Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman, Jennifer Hill

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models.

Learning Languages
Dive Into Python 3
Languages: Python
3.8 (276 Ratings)

Dive Into Python 3

Mark Pilgrim, 2009
Mark Pilgrim is a developer advocate for open source and open standards

This is a hands-on guide to Python 3 and its differences from Python 2. Each chapter starts with a real, complete code sample, picks it apart and explains the pieces, and then puts it all back together in a summary at the end.

Data Mining and Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques
4.0 (229 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.

Big Data
Big Data Now
3.4 (189 Ratings)

Big Data Now: 2012 Edition

O’Reilly Media, Inc., 2012

This is not just a technical book or just a business guide. Data is ubiquitous and it doesn't pay much attention to borders, so we've calibrated our coverage to follow it wherever it goes.

Data Mining and Machine Learning
Understanding Machine Learning: From Theory to Algorithms
4.2 (80 Ratings)

Understanding Machine Learning: From Theory to Algorithms

Shai Shalev-Shwartz, 2014

The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.

Statistics
Think Bayes: Bayesian Statistics Made Simple
3.9 (64 Ratings)

Think Bayes: Bayesian Statistics Made Simple

Allen B. Downey, 2012

Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.

SQL, NoSQL, and Databases
Graph Databases
Languages: Graph DB
3.5 (27 Ratings)

Graph Databases

Ian Robinson, Jim Webber, & Emil Eifrem, 2013

Get started with O'Reilly's Graph Databases and discover how graph databases can help you manage and query highly connected data.

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