Books

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

Data Driven: Creating a Data Culture
3.8 (309 Ratings)
Forming Data Science Teams

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.

Building Data Science Teams
3.7 (297 Ratings)
Forming Data Science Teams

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.

Data Jujitsu: The Art of Turning Data into Product
3.8 (179 Ratings)
Data Science in General

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.

An Introduction to Statistical Learning with Applications in R
4.6 (1447 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.

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4.4 (7001 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.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction
4.4 (235 Ratings)
Statistics

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.

Python Cookbook
Languages: Python
4.2 (369 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.

Think Python second edition
Languages: Python
4.1 (62 Ratings)
Learning Languages

Think Python 2nd Edition

Allen Downey, 2015
Allen Downey is a Professor of Computer Science at Olin College

This hands-on guide takes you through Python a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. Updated to Python 3.

Automate the Boring Stuff with Python: Practical Programming for Total Beginners
Languages: Python
4.3 (1407 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.

Computer Age Statistical Inference Book Cover
4.4 (67 Ratings)

Computer Age Statistical Inference: Algorithms, Evidence and Data Science

Bradley Efron, Trevor Hastie

The book integrates methodology and algorithms with statistical inference, and ends with speculation on the future direction of statistics and data science.

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