Very interesting compilation published here, with a strong machine learning flavor (maybe machine learning book authors - usually academics - are more prone to making their books available for free). Many are O'Reilly books freely available. Here we display those most relevant to data science. I haven't checked all the sources, but they seem legit. If you find some issue, let us know in the comment section below. Note that at DSC, we also have our free books:

• Data Science by Analyticbridge (internal to DSC, one of the first books about data science)
• Data Science 2.0 (internal to DSC, check the red-starred articles)
• 27 free data mining books

There are several sections in the listing in question:

1. Data Science Overviews (4 books)
2. Data Scientists Interviews (2 books)
3. How To Build Data Science Teams (3 books)
4. Data Analysis (1 book)
5. Distributed Computing Tools (2 books)
6. Data Mining and Machine Learning (29 books)
7. Statistics and Statistical Learning (5 books)
8. Data Visualization (2 books)
9. Big Data (3 books)

Here we mention #1, #5 and #6:

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)

Distributed Computing Tools

• Hadoop: The Definitive Guide (Tom White, 2011)
• Data-Intensive Text Processing with MapReduce (Jimmy Lin & Chris Dyer, 2010)

Data Mining and Machine Learning

• Introduction to Machine Learning (Amnon Shashua, 2008)
• Machine Learning (Abdelhamid Mellouk & Abdennacer Chebira)
• Machine Learning – The Complete Guide (Wikipedia)
• 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)
• Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Goo... (Matthew A. Russell, 2014)
• Probabilistic Programming & Bayesian Methods for Hackers (Cam Davidson-Pilon, 2015)
• Data Mining Techniques For Marketing, Sales, and Customer Relations... (Michael J.A. Berry & Gordon S. Linoff, 2004)
• Inductive Logic Programming: Techniques and Applications (Nada Lavrac & Saso Dzeroski, 1994)
• Pattern Recognition and Machine Learning (Christopher M. Bishop, 2006)
• Machine Learning, Neural and Statistical Classification (D. Michie, D.J. Spiegelhalter, & C.C. Taylor, 1999)
• Information Theory, Inference, and Learning Algorithms (David J.C. MacKay, 2005)
• Data Mining and Business Analytics with R (Johannes Ledolter, 2013)
• Bayesian Reasoning and Machine Learning (David Barber, 2014)
• Gaussian Processes for Machine Learning (C. E. Rasmussen & C. K. I. Williams, 2006)
• Reinforcement Learning: An Introduction (Richard S. Sutton & Andrew G. Barto, 2012)
• Algorithms for Reinforcement Learning (Csaba Szepesvari, 2009)
• Big Data, Data Mining, and Machine Learning (Jared Dean, 2014)
• Modeling With Data (Ben Klemens, 2008)
• KB – Neural Data Mining with Python Sources (Roberto Bello, 2013)
• Deep Learning (Yoshua Bengio, Ian J. Goodfellow, & Aaron Courville, 2015)
• Neural Networks and Deep Learning (Michael Nielsen, 2015)
• 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)

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Additional Reading

• 50 Articles about Hadoop and Related Topics
• 10 Modern Statistical Concepts Discovered by Data Scientists
• Top data science keywords on DSC
• 4 easy steps to becoming a data scientist
• 13 New Trends in Big Data and Data Science
• 22 tips for better data science
• Data Science Compared to 16 Analytic Disciplines
• How to detect spurious correlations, and how to find the real ones
• 17 short tutorials all data scientists should read (and practice)
• 10 types of data scientists
• 66 job interview questions for data scientists
• High versus low-level data science

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