Data Science Books

 An Introduction to Data Science by Jeffrey Stanton, 2013 View Free Book

School of Data Handbook by School of Data, 2015 View Free Book

 Data Jujitsu: The Art of Turning Data into Product by DJ Patil, 2012 View Free Book
 Art of Data Science by Roger D. Peng & Elizabeth Matsui, 2015 View Free Book
 The Data Science Handbook by Carl Shan, Henry Wang, William Chen, & Max Song, 2015 View Free Book
 The Data Analytics Handbook by Brian Liou, Tristan Tao, & Declan Shener, 2015 View Free Book
 Data Driven: Creating a Data Culture by Hilary Mason & DJ Patil, 2015 View Free Book
 Building Data Science Teams by DJ Patil, 2011 View Free Book
 Understanding the Chief Data O€fficer by Julie Steele, 2015 View Free Book
 The Elements of Data Analytic Style by Jeff Leek, 2015 View Free Book
 Hadoop: The Definitive Guide by Tom White, 2011 View Free Book
 Hadoop Tutorial as a PDF by Tutorials Point View Free Book
 Cloudera Impala by John Russell, 2014 View Free Book
 Data-Intensive Text Processing with MapReduce by Jimmy Lin & Chris Dyer,2010 View Free Book
 Hadoop Illuminated by Mark Kerzner & Sujee Maniyam, 2014 View Free Book
 Programming Pig by Alan Gates, 2011 View Free Book
Think Python: How would you Think Like a Computer Scientist by Allen Downey, 2012 View Free Book
Python Programming by Wikibooks, 2015 View Free Book
 Automate the Boring Stuff with Python: Practical Programming for Total Beginners by Al Sweigart, 2015 View Free Book
 Learn Python the Hard Way by Zed A. Shaw, 2013 View Free Book
 Dive Into Python 3 by Mark Pilgrim, 2009 View Free Book
 Test-Driven Development with Python by Harry J. W. Percival, 2015 View Free Book
 A Byte of Python by Swaroop C H, 2003 View Free Book
 Invent with Python by Al Sweigart, 2010 View Free Book
 Python for Informatics: Exploring Information by Dr. Charles R Severance, 2013 View Free Book
 Python Practice Book by Anand Chitipothu, 2014 View Free Book
 Learn Python, Break Python by Scott Grant, 2014 View Free Book
 Python Cookbook by David Beazley & Brian K. Jones, 2013 View Free Book
 Learning with Python 3 by Peter Wentworth, Jeffrey Elkner, Allen B. Downey, & Chris Meyers, 2012 View Free Book
 Python for You and Me by Kushal Das, 2015 View Free Book
R Programming for Data Science by Roger D. Peng, View Free Book
R Programming by Wikibooks, 2014 View Free Book
 Advanced R by Hadley Wickham, 2014 View Free Book
 A Little Book of R for Time Series by Avril Coghlan, 2015 View Free Book
 The R Manuals by R Development Core Team View Free Book
 Learning Statistics with R by Daniel Navarro, 2015 View Free Book
 LearnR by Kun Ren, 2015 View Free Book
 R by Example by Ajay Shah, 2005 View Free Book
 Practical Regression and Anova using R by Julian J. Faraway, 2002 View Free Book
 The R Inferno by Patrick Burns, 2011 View Free Book
 Ecological Models and Data in R by Benjamin M. Bolker, 2008 View Free Book
 Spatial Epidemiology Notes: Applications and Vignettes in R by Charles DiMaggio, 2014 View Free Book
 Learn SQL The Hard Way by Zed. A. Shaw, 2010 View Free Book
 SQL Tutorial as a PDF by Tutorials Point View Free Book
 SQL for Web Nerds by Philip Greenspun View Free Book
 Cassandra Tutorial as a PDF by Tutorials Point, 2015 View Free Book
 CouchDB: The Definitive Guide by J. Chris Anderson, Jan Lehnardt, & Noah Slater View Free Book
 The Little MongoDB Book by Karl Seguin, 2011 View Free Book
 MongoDB Succinctly by Agus Kurniawan View Free Book
 Extracting Data from NoSQL Databases by Petter Näsholm, 2012 View Free Book
 NoSQL Databases by Christof Strauch View Free Book
Graph Databases by Ian Robinson, Jim Webber, & Emil Eifrem, 2013 View Free Book
 Introduction to Machine Learning by Amnon Shashua, 2008 View Free Book
 Introduction to Machine Learning by Alex Smola & S.V.N. Vishwanathan, 2008 View Free Book
 Machine Learning by Abdelhamid Mellouk & Abdennacer Chebira, 450 View Free Book
Machine Learning – The Complete Guide by Wikipedia View Free Book
 Social Media Mining An Introduction by Reza Zafarani, Mohammad Ali Abbasi, & Huan Liu, 2014 View Free Book
 Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten & Eibe Frank,2005 View Free Book
 Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, & Jeff Ullman, 2014 View Free Book
 A Programmer’s Guide to Data Mining by Ron Zacharski, 2015 View Free Book
 Data Mining with Rattle and R by Graham Williams, 2011 View Free Book
 Data Mining and Analysis: Fundamental Concepts and Algorithms by Mohammed J. Zaki & Wagner Meria Jr., 2014 View Free Book
 Probabilistic Programming & Bayesian Methods for Hackers by Cam Davidson-Pilon, 2015 View Free Book
 Data Mining Techniques For Marketing, Sales, and Customer Relationship Management by Michael J.A. Berry & Gordon S. Linoff, 2004 View Free Book
 Inductive Logic Programming: Techniques and Applications by Nada Lavrac & Saso Dzeroski, 1994 View Free Book
 Pattern Recognition and Machine Learning by Christopher M. Bishop, 2006 View Free Book
 Machine Learning, Neural and Statistical Classification by D. Michie, D.J. Spiegelhalter, & C.C. Taylor, 1999 View Free Book
 Information Theory, Inference, and Learning Algorithms by David J.C. MacKay, 2005 View Free Book
 Data Mining and Business Analytics with R by Johannes Ledolter, 2013 View Free Book
 Bayesian Reasoning and Machine Learning by David Barber, 2014 View Free Book
 Gaussian Processes for Machine Learning by C. E. Rasmussen & C. K. I. Williams, 2006 View Free Book
 Reinforcement Learning: An Introduction by Richard S. Sutton & Andrew G. Barto, 2012 View Free Book
 Algorithms for Reinforcement Learning by Csaba Szepesvari , 2009 View Free Book
 Modeling With Data by Ben Klemens, 2008 View Free Book
 KB – Neural Data Mining with Python Sources by Roberto Bello, 2013 View Free Book
 Deep Learning by Yoshua Bengio, Ian J. Goodfellow, & Aaron Courville, 2015 View Free Book
 Neural Networks and Deep Learning by Michael Nielsen, 2015 View Free Book
 Data Mining Algorithms In R by Wikibooks, 2014 View Free Book
 Data Mining and Analysis: Fundamental Concepts and Algorithms by Mohammed J. Zaki & Wagner Meira Jr., 2014 View Free Book
 Theory and Applications for Advanced Text Mining by Shigeaki Sakurai, 2012 View Free Book
 Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, 2014 View Free Book
 Real-World Active Learning by Ted Cuzzillo, 2015 View Free Book
 A Course in Machine Learning by Hal Daumé III, 2014 View Free Book
 A First Encounter with Machine Learning by Max Welling, 2011 View Free Book
 The LION Way: Machine Learning plus Intelligent Optimization by Roberto Battiti & Mauro Brunato, 2013 View Free Book
 Learning Deep Architectures for AI by Yoshua Bengio, 2009 View Free Book
 Artificial Intelligence A Modern Approach, 1st Edition by Stuart Russell, 1995 View Free Book
 Artificial Intelligence: Foundations of Computational Agents by David Poole & Alan Mackworth, 2010 View Free Book
 Think Stats: Exploratory Data Analysis in Python by Allen B. Downey, 2014 View Free Book
 Think Bayes: Bayesian Statistics Made Simple by Allen B. Downey, 2012 View Free Book
 The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, & Jerome Friedman, 2008 View Free Book
 An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani, 2013 View Free Book
 A First Course in Design and Analysis of Experiments by Gary W. Oehlert, 2010 View Free Book
 Time Series Analysis and Its Applications: With R Examples by Robert H. Shumway & David S. Stoffer, 2011 View Free Book
 An Introduction to Statistics with Python by Thomas Haslwanter, 2015 View Free Book
 OpenIntro Statistics by David M Diez, Christopher D Barr, & Mine Çetinkaya-Rundel, 2015 View Free Book
 Intro Stat with Randomization and Simulation by David M Diez, Christopher D Barr, & Mine Çetinkaya-Rundel, 2015 View Free Book
 D3 Tips and Tricks by Malcolm Maclean, 2015 View Free Book
 Interactive Data Visualization for the Web by Scott Murray, 2013 View Free Book
 Disruptive Possibilities: How Big Data Changes Everything by Jeffrey Needham, 2013 View Free Book
 Real-Time Big Data Analytics: Emerging Architecture by Mike Barlow, 2013 View Free Book
 Big Data Now: 2012 Edition by O’Reilly Media, Inc., 2012 View Free Book
 Natural Language Processing with Python by Steven Bird, 2009 View Free Book
 Computer Vision by Richard Szeliski, 2010 View Free Book
 Concise Computer Vision by Reinhard Klette, 2010 View Free Book
 Programming Computer Vision with Python by Jan Erik Solem, 2012 View Free Book
 A First Course in Linear Algebra by Robert A Beezer, 2012 View Free Book
 Linear Algebra: An Introduction to Mathematical Discourse by Wikibooks View Free Book
 Probability and Statistics Cookbook by Matthias Vallentin View Free Book
 Linear Algebra, Theory And Applications by Kenneth Kuttler, 2015 View Free Book
 Probabilistic Models in the Study of Language by R Levy, 2012 View Free Book
 Linear Algebra by David Cherney, Tom Denton & Andrew Waldron, 2013 View Free Book
 Introduction to Probability by Charles M. Grinstead & J. Laurie Snell, 1997 View Free Book
 Elementary Applied Topology by Robert Ghrist, 2014 View Free Book
 Ordinary Differential Equations by Wikibooks View Free Book
 Elementary Differential Equations by William F. Trench, 2013 View Free Book

 

Also Must Reads in Data Science

Programming Collective Intelligence

1

Programming Collective Intelligence, PCI as it is popularly known, is one of the best books to start learning machine learning. If there is one book to choose on machine learning – it is this one. I haven’t met a data scientist yet who has read this book and does not recommend to keep it on your bookshelf. A lot of them have re-read this book multiple times.

The book was written long before data science and machine learning acquired the cult status they have today – but the topics and chapters are entirely relevant even today! Some of the topics covered in the book are collaborative filtering techniques, search engine features, Bayesian filtering and Support vector machines. If you don’t have a copy of this book – order it as soon as you finish reading this article! The book uses Python to deliver machine learning in a fascinating manner.Machine Learning for Hackers

2

 

This book is written by Drew Conway and John Myles White. It is majorly based on data analysis in R. This books is best suited for beginners having basic knowledge on R. It further covers the use of advanced R in data wrangling. It has interesting case studies which will help you to understand the importance of using machine learning algorithms.

 

Machine Learning by Tom M Mitchell

3

 

After you’ve read the above books, you are good to dive into machine learning. This is a great introductory book on machine learning. It provides a nice overview of ml theorems with pseudocode summaries of their algorithms. Apart from case studies, Tom has used basic examples to help you understand these algorithms easily.

Free PDF Link: Download

 

The Elements of Statistical Learning

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This book is written by Trevor Hastie, Robert Tibshirani, Jerome Friedman. This book aptly explains the machine learning algorithms mathematically from a statistical perspective. It provides a powerful world created by statistics and machine learning. This books lays emphasis on mathematical derivations to define the underlying logic behind an algorithm. This book demands a rudimentary understanding of linear algebra.

Free PDF Link: Download

 

Learning from Data

5

 

This book is written by Yaser Abu Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin. This book provides a perfect introduction to machine learning. This book prepares you to understand complex areas of machine learning. Yaser has provided ‘to the point’ explanations instead of lengthy and go-around explanations. If you choose this book, I’d suggest you to refer to onlinetutorials of Yaser Abu Mostafa as well. They’re awesome.

Free PDF Link: Download

 

Pattern Recognition and Machine Learning

6

 

This book is written by Christopher M Bishop. This book serves as a excellent reference for students keen to understand the use of statistical techniques in machine learning and pattern recognition. This books assumes the knowledge of linear algebra and multivariate calculus. It provides a comprehensive introduction to statistical pattern recognition techniques using practice exercises.

Free PDF Link: Download

 

Artificial Intelligence

Artificial Intelligence: A Modern Approach

11Who else might be the best coach to learn AI than Peter Norvig? You have to take a course from Norvig to understand his style of teaching. But once you do, you will long for it!

This book is written by Stuart Russell and Peter Norvig. It is best suited for people new to A.I. More than just providing an overview of artificial intelligence, this book thoroughly covers subjects from search algorithms, reducing problems to search problems, working with logic, planning, and more advanced topics in AI such as reasoning with partial observability, machine learning and language processing. Make it the first book on A.I in your book shelf.

Free PDF Link: Download

Artificial Intelligence for Humans

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This book is written by Jeff Heaton. It teaches basic artificial intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. It explains these algorithms using interesting examples and cases. Needless to say, this book requires good commands over mathematics. Otherwise, you’ll have tough time deciphering the equations.

 

Paradigm of Artificial Intelligence Programming

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Another one by Peter Norvig!

This book teaches advanced common lisp techniques to build major A.I systems. It delves deep into the practical aspects of A.I and teaches its readers the method to build and debug robust practical programs. It also demonstrates superior programming style and essential AI concepts. I’d recommend reading this book, if you are serious about a career in A.I specially.

Artificial Intelligence: A New Synthesis

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This book is written by Nils J Nilsson. After reading the above 3 books, you’d like something which could challenge your mind. Here’s what you are looking for. This books covers topics such as Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks and explains them with great ease. I wouldn’t recommend this book for a beginner. However, it’s a must read for advanced level user.

 

The Emotion Machine: Commonsense Thinking, Artificial Intelligence and the Future of Human Mind

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This book is written by Marvin Minsky. In this book, Marvin offers a fascinating model of how our mind works. He tries to infer the future of human mind by examining different forms of mind activity. You’ll find path breaking research findings where Marvin has challenge the status quo. This book is great to develop perspective and become aware of present to future transition of A.I

 

Artificial Intelligence (3rd Edition)

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This book is written by Patrick Henry Winston. This is an introductory book on artificial intelligence. Non-programmers can easily understand the explanations and concepts. More advanced AI topics are covered but haven’t been explained in depth. However some chapters, do cover a great deal of information. It teaches to build intelligent systems using various real life examples. All in all, this book imparts a new shape to complicated intelligence with simple explanation.

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