Books
The best books on Data Science, Big Data, Data Mining, Machine Learning, Python, R, SQL, NoSQL and more. Sorted by popularity.
3.8 (378 Ratings)Data Driven: Creating a Data Culture
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.
3.6 (354 Ratings)Think Stats: Exploratory Data Analysis in Python
This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.
4.2 (348 Ratings)Artificial Intelligence A Modern Approach, 1st Edition
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.
3.6 (326 Ratings)Building Data Science Teams
In this in-depth report, data scientist DJ Patil explains the skills,perspectives, tools and processes that position data science teams for success.
4.4 (296 Ratings)The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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.
4.2 (273 Ratings)Test-Driven Development with Python
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.
3.9 (263 Ratings)Dive Into Python 3
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.
4.3 (250 Ratings)Data Analysis Using Regression and Multilevel/Hierarchical Models
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.
4.5 (232 Ratings)Advanced R
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.
3.8 (230 Ratings)Data Jujitsu: The Art of Turning Data into Product
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.