free-data-science-books.md

###Index

• Data Science Introduction
• [Data Processing] (#big-data-processing)
• [Data Analysis] (#big-data-analysis)
  • [Fundamentals] (#fundamentals)
  • [Network Analysis] (#network-analysis)
  • [Statistics] (#statistics)
  • [Data Mining] (#data-mining)
  • [Machine Learning] (#machine-learning)
• [Data Science Application] (#big-data-application)
  • [Data Visualization] (#data-visualization)
• [Uncategorized] (#uncategorized)
• [MOOCs about Data Science] (#moocs)

###Data Science Introduction

• [Big Data Now: 2012 Edition] (http://www.amazon.com/Big-Data-Now-2012-Edition-ebook/dp/B0097E4EBQ) - O'Reilly Media Inc. - Beginner
• [Data Science: An Introduction] (http://en.wikibooks.org/wiki/Data_Science:_An_Introduction) - Wikibook - Beginner
• [Disruptive Possibilities: How Big Data Changes Everything] (http://www.amazon.com/Disruptive-Possibilities-Data-Changes-Everything-ebook/dp/B00CLH387W) - Jeffrey Needham - Beginner
• Introduction to Data Science - Jeffery Stanton - Beginner
• [Real-Time Big Data Analytics: Emerging Architecture] (http://www.amazon.com/Real-Time-Big-Data-Analytics-Architecture-ebook/dp/B00DO33RSW) - Mike Barlow - Beginner
• [The Evolution of Data Products] (http://www.amazon.com/The-Evolution-Data-Products-ebook/dp/B005QEKQUY/ref=sr_1_63?s=digital-text&ie=UTF8&qid=1351898530&sr=1-63) - Mike Loukides - Beginner
• [The Promise and Peril of Big Data] (http://www.aspeninstitute.org/sites/default/files/content/docs/pubs/The_Promise_and_Peril_of_Big_Data.pdf) - David Bollier - Beginner

###Data Processing

• [Data-Intensive Text Processing with MapReduce] (http://lintool.github.io/MapReduceAlgorithms/MapReduce-book-final.pdf) - Jimmy Lin and Chris Dyer - Intermediate

###Data Analysis ####Fundamentals

• [Fundamental Numerical Methods and Data Analysis] (http://ads.harvard.edu/books/1990fnmd.book/) - George W. Collins - Beginner
• [Introduction to Metadata] (http://www.getty.edu/research/publications/electronic_publications/intrometadata/index.html) - Murtha Baca - Beginner
• [Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics] (http://cran.r-project.org/doc/manuals/R-intro.pdf) - W. N. Venables, D. M. Smith, and the R Core Team - Beginner
• [Modeling with Data: Tools and Techniques for Scientific Computing] (http://modelingwithdata.org/about_the_book.html) - Ben Klemens - Beginner

####Network Analysis

• [Introduction to Social Network Methods] (http://faculty.ucr.edu/~hanneman/nettext/) - Robert A. Hanneman and Mark Riddle - Intermediate
• [Networks, Crowds, and Markets: Reasoning About a Highly Connected World] (http://www.cs.cornell.edu/home/kleinber/networks-book/) - David Easley and Jon Kleinberg - Intermediate
• [Network Science] (http://barabasilab.neu.edu/networksciencebook/downlPDF.html) - Sarah Morrison - Beginner
• [The Wealth of Networks] (http://www.benkler.org/Benkler_Wealth_Of_Networks.pdf) - Yochai Benkler - Beginner

####Statistics

• [Advanced Data Analysis from an Elementary Point of View] (http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf) - Cosma Rohilla Shalizi - Veternan
• [An Introduction to R] (http://cran.r-project.org/doc/manuals/R-intro.pdf) - W. N. Venables, D. M. Smith, and the R Core Team - Beginner
• [Analyzing Linguistic Data: a practical introduction to statistics] (http://www.ualberta.ca/~baayen/publications/baayenCUPstats.pdf) - R. H. Baayan - Beginner
• [Applied Data Science] (http://columbia-applied-data-science.github.io/appdatasci.pdf) - Ian Langmore and Daniel Krasner - Intermediate
• [Concepts and Applications of Inferential Statistics] (http://vassarstats.net/textbook/) - Richard Lowry - Beginner
• [Forecasting: Principles and Practice] (https://www.otexts.org/fpp/) - Rob J. Hyndman and George Athanasopoulos - Intermediate
• [Introduction to Probability] (http://www.math.umass.edu/~lavine/Book/book.html) - Charles M. Grinstead and J. Laurie Snell - Beginner
• [Introduction to Statistical Thought] (http://www.math.umass.edu/~lavine/Book/book.pdf) - Michael Lavine - Beginner
• [OpenIntro Statistics - Second Edition] (http://www.openintro.org/stat/textbook.php) - David M. Diez, Christopher D. Barr, and Mine Cetinkaya-Rundel - Beginner
• [simpleR - Using R for Introductory Statistics] (http://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf) - John Verzani - Beginner
• [Statistics] (http://upload.wikimedia.org/wikipedia/commons/8/82/Statistics.pdf) - Beginner
• [Think Stats: Probability and Statistics for Programmers] (http://www.greenteapress.com/thinkstats/thinkstats.pdf) - Allen B. Downey - Beginner

####Data Mining

• [Data Mining and Analysis: Fundamental Concepts and Algorithms] (http://www2.dcc.ufmg.br/livros/miningalgorithms/files/pdf/dmafca.pdf) - Mohammed J. Zaki and Wagner Meira Jr. - Intermediate
• [Data Mining and Knowledge Discovery in Real Life Applications] (http://www.intechopen.com/books/data_mining_and_knowledge_discovery_in_real_life_applications) - Julio Ponce and Adem Karahoca - Beginner
• [Data Mining for Social Network Data] (http://link.springer.com/book/10.1007%2F978-1-4419-6287-4) - Springer - Veteran
• [Mining of Massive Datasets] (http://infolab.stanford.edu/~ullman/mmds/book.pdf) - Anand Rajaraman, Jure Leskovec, and Jeffrey D. Ullman - Intermediate
• [Knowledge-Oriented Applications in Data Mining] (http://www.intechopen.com/books/knowledge-oriented-applications-in-data-mininge) - Kimito Funatsu - Intermediate
• [New Fundamental Technologies in Data Mining] (http://www.intechopen.com/books/new-fundamental-technologies-in-data-mining) - Kimito Funatsu - Intermediate
• [R and Data Mining: Examples and Case Studies] (http://cran.r-project.org/doc/contrib/Zhao_R_and_data_mining.pdf) - Yanchang Zhao - Beginner
• [The Elements of Statistical Learning] (http://statweb.stanford.edu/~tibs/ElemStatLearn/) - Trevor Hastie, Robert Tibshirani, and Jerome Friedman - Intermediate
• [Theory and Applications for Advanced Text Mining] (http://www.intechopen.com/books/theory-and-applications-for-advanced-text-mining) - Shigeaki Sakurai - Intermediate

####Machine Learning

• [A Course in Machine Learning] (http://ciml.info/) - Hal Daume - Beginner
• [A First Encounter with Machine Learning] (https://www.ics.uci.edu/~welling/teaching/273ASpring10/IntroMLBook.pdf) - Max Welling - Beginner
• [Bayesian Reasoning and Machine Learning] (http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/031013.pdf) - David Barber - Veteran
• [Gaussian Processes for Machine Learning] (http://www.gaussianprocess.org/gpml/chapters/) - Carl Edward Rasmussen and Christopher K. I. Williams - Veteran
• [Introduction to Machine Learning] (http://alex.smola.org/drafts/thebook.pdf) - Alex Smola and S.V.N. Vishwanathan - Intermediate
• [Probabilistic Programming & Bayesian Methods for Hackers] (http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) - Cam Davidson-Pilon (main author) - Intermediate
• [The LION Way: Machine Learning plus Intelligent Optimization] (http://www.lionsolver.com/LIONbook/) - Robert Battiti and Mauro Brunato - Intermediate
• [Thinking Bayes] (http://www.greenteapress.com/thinkbayes/) - Allen B. Downey - Beginner

###Data Science Application ####Information Retrieval

• [Introduction to Information Retrival] (http://nlp.stanford.edu/IR-book/) - Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze - Intermediate

####Data Visualization

• [Interactive Data Visualization for the Web] (http://chimera.labs.oreilly.com/books/1230000000345/index.html) - Scott Murray - Beginner

###Uncategorized

• [Data Journalism Handbook] (http://datajournalismhandbook.org/1.0/en/) - Jonathan Gray, Liliana Bounegru, and Lucy Chambers - Beginner
• [Building Data Science Teams] (http://assets.en.oreilly.com/1/eventseries/23/Building-Data-Science-Teams.pdf) - DJ Patil - Beginner
• [Information Theory, Inference, and Learning Algorithms] (http://www.inference.phy.cam.ac.uk/itprnn/book.html) - David MacKay - Intermediate
• [Mathematics for Computer Science] (http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/readings/MIT6_042JF10_notes.pdf) - Eric Lehman, Thomas Leighton, and Albert R. Meyer - Beginner
• [The Field Guide to Data Science] (http://www.boozallen.com/media/file/The-Field-Guide-to-Data-Science.pdf) - Beginner

###MOOCs about Data Science

• [Data Mining with Weka] (http://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/) - Ian H. Witten - Intermediate
• [Introduction to Data Science] (https://class.coursera.org/datasci-001/class) - Bill Howe (Coursera) - Beginner
• [Introduction to Hadoop and MapReduce] (https://www.udacity.com/course/ud617) - Udacity - Beginner
• [Machine Learning] (https://class.coursera.org/ml-003/class) - Andrew Ng (Coursera) - Beginner
• [Machine Learning Foundatiaons (taught in Chinese)] (https://class.coursera.org/ntumlone-001) - Hsuan-Tien Lin - Beginner
• [Machine Learning Video Library] (http://work.caltech.edu/library/#!?goback=.gde_35222_member_5810981726511443971) - Yaser Abu-Mostafa - Intermediate
• [Natural Language Processing] (https://class.coursera.org/nlp/lecture/preview) - Dan Jurafsky and Christopher Manning (Coursera) - Intermediate
• [Social and Economic Networks: Models and Analysis] (https://class.coursera.org/networksonline-001/class) - Matthew O. Jackson (Coursera) - Intermediate
• [Social Network Analysis] (https://class.coursera.org/sna-003/class) - Lada Adamic (Coursera) - Intermediate