Books

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

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.

Data Mining and Machine Learning
Information Theory, Inference, and Learning Algorithms
4.5 (366 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
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
4.3 (340 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.

Statistics
Think Stats: Exploratory Data Analysis in Python
Languages: Python
3.6 (287 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
Data Mining: Practical Machine Learning Tools and Techniques
4.0 (228 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.

Data Science in General
Data Jujitsu: The Art of Turning Data into Product
3.8 (180 Ratings)

Data Jujitsu: The Art of Turning Data into Product

DJ Patil, 2012
DJ is the "Data Scientist in Residence" at Greylock Partners

Learn how to use a problem's "weight" against itself. Learn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all.

Learning Languages
Ecological Models and Data in R
Languages: R
4.3 (43 Ratings)

Ecological Models and Data in R

Benjamin M. Bolker, 2008

The first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know to analyze their own data using the R language.

Data Mining and Machine Learning
Mining of Massive Datasets
4.3 (35 Ratings)

Mining of Massive Datasets

Jure Leskovec, Anand Rajaraman, & Jeff Ullman, 2014

Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond.

SQL, NoSQL, and Databases
Graph Databases
Languages: Graph DB
3.5 (28 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.

Artificial Intelligence
Learning Deep Architectures for AI
3.8 (20 Ratings)

Learning Deep Architectures for AI

Yoshua Bengio, 2009

Foundations and Trends(r) in Machine Learning.

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