Books

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

python for everybody cover.jpg
Languages: Python
4.3 (337 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.

Think Stats: Exploratory Data Analysis in Python
Languages: Python
3.6 (334 Ratings)
Statistics

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 Driven: Creating a Data Culture
3.8 (329 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
3.6 (306 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.

Dive Into Python 3
Languages: Python
3.8 (259 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.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction
4.4 (256 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.

Test-Driven Development with Python
Languages: Python
4.2 (249 Ratings)
Learning Languages

Test-Driven Development with Python

Harry J. W. Percival, 2015

By taking you through the development of a real web application from beginning to end, this hands-on guide demonstrates the practical advantages of test-driven development (TDD) with Python.

data-analysis-using-regression.jpg
4.3 (244 Ratings)

Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman, Jennifer Hill

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models.

Advanced R
Languages: R
4.6 (212 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.

Data Jujitsu: The Art of Turning Data into Product
3.8 (198 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.

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