A great collection of free data science books covering a wide range of topics from Data Science, Business Analytics, Data Mining and Big Data to Algorithms and Data Science Tools.

If machine learning is more your thing, check out our list of 16 Free Machine Learning Books.

TABLE OF CONTENTS

DATA SCIENCE OVERVIEWS

• An Introduction to Data Science (Jeffrey Stanton, 2013)
• School of Data Handbook (2015)
• Data Jujitsu: The Art of Turning Data into Product (DJ Patil, 2012)
• Art of Data Science (Roger D. Peng & Elizabeth Matsui, 2015)

DATA SCIENTISTS INTERVIEWS

• The Data Science Handbook (Carl Shan, Henry Wang, William Chen, & Max Song, 2015)
• The Data Analytics Handbook (Brian Liou, Tristan Tao, & Declan Shener, 2015)

HOW TO BUILD DATA SCIENCE TEAMS

• Data Driven: Creating a Data Culture (Hilary Mason & DJ Patil, 2015)
• Building Data Science Teams (DJ Patil, 2011)
• Understanding the Chief Data O€fficer (Julie Steele, 2015)

DATA ANALYSIS

• The Elements of Data Analytic Style (Jeff Leek, 2015)

DISTRIBUTED COMPUTING TOOLS

• Data-Intensive Text Processing with MapReduce (Jimmy Lin & Chris Dyer, 2010)

DATA MINING

• Social Media Mining An Introduction (Reza Zafarani, Mohammad Ali Abbasi, & Huan Liu, 2014)
• Data Mining: Practical Machine Learning Tools and Techniques (Ian H. Witten & Eibe Frank, 2005)
• Mining of Massive Datasets (Jure Leskovec, Anand Rajaraman, & Jeff Ullman, 2014)
• A Programmer¡¯s Guide to Data Mining (Ron Zacharski, 2015)
• Data Mining with Rattle and R (Graham Williams, 2011)
• Data Mining and Analysis: Fundamental Concepts and Algorithms (Mohammed J. Zaki & Wagner Meria Jr., 2014)
• Probabilistic Programming & Bayesian Methods for Hackers (Cam Davidson-Pilon, 2015)
• Data Mining and Business Analytics with R (Johannes Ledolter, 2013)
• Big Data, Data Mining, and Machine Learning (Jared Dean, 2014)
• Modeling With Data (Ben Klemens, 2008)
• KB ¨C Neural Data Mining with Python Sources (Roberto Bello, 2013)
• Data Mining Algorithms In R (Wikibooks, 2014)
• Data Mining and Analysis: Fundamental Concepts and Algorithms (Mohammed J. Zaki & Wagner Meira Jr., 2014)
• Theory and Applications for Advanced Text Mining (Shigeaki Sakurai, 2012)

STATISTICS AND STATISTICAL LEARNING

• Think Stats: Exploratory Data Analysis in Python (Allen B. Downey, 2014)
• Think Bayes: Bayesian Statistics Made Simple (Allen B. Downey, 2012)
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Trevor Hastie, Robert Tibshirani, & Jerome Friedman, 2008)
• An Introduction to Statistical Learning with Applications in R (Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani, 2013)
• A First Course in Design and Analysis of Experiments (Gary W. Oehlert, 2010)

DATA VISUALIZATION

• D3 Tips and Tricks (Malcolm Maclean, 2015)
• Interactive Data Visualization for the Web (Scott Murray, 2013)

BIG DATA

• Real-Time Big Data Analytics: Emerging Architecture (Mike Barlow, 2013)
• Big Data Now: 2012 Edition (O¡¯Reilly Media, Inc., 2012)