Books

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

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4.4 (7280 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.

Automate the Boring Stuff with Python: Practical Programming for Total Beginners
Languages: Python
4.3 (1695 Ratings)
Learning Languages

Automate the Boring Stuff with Python: Practical Programming for Total Beginners

Al Sweigart, 2015

Practical programming for total beginners. In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required.

An Introduction to Statistical Learning with Applications in R
4.6 (1663 Ratings)
Statistics

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.

Pattern Recognition and Machine Learning book cover
4.3 (1573 Ratings)

Pattern Recognition and Machine Learning

Christopher M. Bishop, 2006

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.

Natural Language Processing with Python
Languages: Python
4.1 (464 Ratings)
Computer Science Topics

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.

Interactive Data Visualization for the Web
4.1 (424 Ratings)
Data Visualization

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.

Reinforcement Learning: An Introduction
4.5 (414 Ratings)
Data Mining and Machine Learning

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.

Python Cookbook
Languages: Python
4.2 (408 Ratings)
Learning Languages

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.

Information Theory, Inference, and Learning Algorithms
4.5 (404 Ratings)
Data Mining and Machine Learning

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

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Languages: Python
4.3 (370 Ratings)
Learning Languages

Python for Everybody

Dr. Charles R Severance, 2016

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.

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