51 бесплатная книга о Data Science

    Смирись, человек 21 века, что твой главный инструмент — это информация, данные, цифры и управление с их помощью. Сегодня мы делимся с вами очень полезным списком литературы о Data Science!



    // Книги общего характера


    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)

    // Интервью Data Scientists


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

    // Как строить 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 Officer (Julie Steele, 2015)

    // Data Analysis


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

    А ещё не забудьте про 9 книг, которые нужно прочитать этой осенью.

    // Инструменты


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

    // Разработка и 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, Google+, GitHub, and More (Matthew A. Russell, 2014)
    Probabilistic Programming & Bayesian Methods for Hackers (Cam Davidson-Pilon, 2015)
    Data Mining Techniques For Marketing, Sales, and Customer Relationship Management (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)

    // О статистике


    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-визуализация


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

    // И просто Big Data


    Disruptive Possibilities: How Big Data Changes Everything (Jeffrey Needham, 2013)
    Real-Time Big Data Analytics: Emerging Architecture (Mike Barlow, 2013)
    Big Data Now: 2012 Edition (O’Reilly Media, Inc., 2012)

    А ещё есть у нас отличная подборка книг-двигателей карьеры.
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