Books

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

Data Mining and Analysis: Fundamental Concepts and Algorithms
4.1 (11 Ratings)
Data Mining and Machine Learning

Data Mining and Analysis: Fundamental Concepts and Algorithms

Mohammed J. Zaki & Wagner Meria Jr., 2014

The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers many more cutting-edge data mining topics.

Data Mining with Rattle and R
Languages: R
4.1 (36 Ratings)
Data Mining and Machine Learning

Data Mining with Rattle and R

Graham Williams, 2011

This book aims to get you into data mining quickly. Load some data (e.g., from a database) into the Rattle toolkit and within minutes you will have the data visualised and some models built.

OpenIntro Statistics
4.1 (34 Ratings)
Statistics

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.

Computer Vision
4.2 (99 Ratings)
Computer Science Topics

Computer Vision

Richard Szeliski, 2010

Challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which you can use on you own personal media

Elementary Applied Topology
4.3 (21 Ratings)
Math Topics

Elementary Applied Topology

Robert Ghrist, 2014

This text gives a brisk and engaging introduction to the mathematics behind the recently established field of Applied Topology.

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4.0 (22 Ratings)

Design and Analysis of Experiments, 8th Edition

Douglas C. Montgomery

This book helps senior and graduate students in engineering, business, and statistics-as well as working practitioners-to design and analyze experiments for improving the quality, efficiency and performance of working systems.

Dive Into Python 3
Languages: Python
3.9 (254 Ratings)
Learning Languages

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.

Python for Informatics: Exploring Information
Languages: Python
4.0 (242 Ratings)
Learning Languages

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.

Data Mining: Practical Machine Learning Tools and Techniques
3.9 (155 Ratings)
Data Mining and Machine Learning

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.

Advanced R
Languages: R
4.6 (197 Ratings)
Learning Languages

Advanced R

Hadley Wickham, 2014

Useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With ten+ years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R.

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