Wuyi Free Data Science Books

Source: Internet
Author: User

Wuyi Free Data Science Books

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 machine learning, algorithms and Data Science Tools.

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
    • 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, 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. Lino FF, 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. 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)
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, & Jero Me 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
    • 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)

Wuyi Free Data Science Books

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