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.

Learning Languages
Python for Informatics: Exploring Information
Languages: Python
4.3 (488 Ratings)

Python for Informatics: Exploring Information

Dr. Charles R Severance, 2013

This book is designed to introduce students to programming and computational thinking through the lens of exploring data. You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet.

Computer Science Topics
Natural Language Processing with Python
Languages: Python
4.1 (457 Ratings)

Natural Language Processing with Python

Steven Bird, 2009

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.

Data Visualization
Interactive Data Visualization for the Web
4.1 (453 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.

Learning Languages
Python Cookbook
Languages: Python
4.2 (404 Ratings)

Python Cookbook

David Beazley & Brian K. Jones, 2013

If you need help writing programs in Python 3, or want to update older Python 2 code, this book is just the ticket. Packed with practical recipes written and tested with Python 3.3. For experienced Python developers.

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.

Data Mining and Machine Learning
Reinforcement Learning: An Introduction
4.5 (326 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.

Forming Data Science Teams
Building Data Science Teams
3.7 (301 Ratings)

Building Data Science Teams

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

In this in-depth report, data scientist DJ Patil explains the skills,perspectives, tools and processes that position data science teams for success.

Forming Data Science Teams
Data Driven: Creating a Data Culture
3.8 (299 Ratings)

Data Driven: Creating a Data Culture

DJ Patil,‎ Hilary Mason
Hilary Mason is the lead scientist at bit.ly, DJ is the "Data Scientist in Residence" at Greylock Partners

In this O’Reilly report, DJ Patil and Hilary Mason outline the steps you need to take if your company is to be truly data-driven—including the questions you should ask and the methods you should adopt.

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