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 [1] (Jeffrey Stanton, 2013)
School of Data Handbook [2] (2015)
Data Jujitsu: The Art of Turning Data into Product [3](DJ Patil, 2012)
Art of Data Science [4] (Roger D. Peng & Elizabeth Matsui, 2015)
// Интервью Data Scientists
The Data Science Handbook [5] (Carl Shan, Henry Wang, William Chen, & Max Song, 2015)
The Data Analytics Handbook [6] (Brian Liou, Tristan Tao, & Declan Shener, 2015)
// Как строить Data Science Teams
Data Driven: Creating a Data Culture [7] (Hilary Mason & DJ Patil, 2015)
Building Data Science Teams [8] (DJ Patil, 2011)
Understanding the Chief Data Officer [9] (Julie Steele, 2015)
// Data Analysis
The Elements of Data Analytic Style [10] (Jeff Leek, 2015)
А ещё не забудьте про 9 книг, которые нужно прочитать этой осенью. [11]
// Инструменты
Hadoop: The Definitive Guide [12] (Tom White, 2011)
Data-Intensive Text Processing with MapReduce [13] (Jimmy Lin & Chris Dyer, 2010)
// Разработка и machine learning
Introduction to Machine Learning [14] (Amnon Shashua, 2008)
Machine Learning [15] (Abdelhamid Mellouk & Abdennacer Chebira)
Machine Learning – The Complete Guide [16] (Wikipedia)
Social Media Mining An Introduction [17] (Reza Zafarani, Mohammad Ali Abbasi, & Huan Liu, 2014)
Data Mining: Practical Machine Learning Tools and Techniques [18] (Ian H. Witten & Eibe Frank, 2005)
Mining of Massive Datasets [19] (Jure Leskovec, Anand Rajaraman, & Jeff Ullman, 2014)
A Programmer’s Guide to Data Mining [20](Ron Zacharski, 2015)
Data Mining with Rattle and R [21] (Graham Williams, 2011)
Data Mining and Analysis: Fundamental Concepts and Algorithms [22] (Mohammed J. Zaki & Wagner Meria Jr., 2014)
Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More [23] (Matthew A. Russell, 2014)
Probabilistic Programming & Bayesian Methods for Hackers [24] (Cam Davidson-Pilon, 2015)
Data Mining Techniques For Marketing, Sales, and Customer Relationship Management [25] (Michael J.A. Berry & Gordon S. Linoff, 2004)
Inductive Logic Programming: Techniques and Applications [26] (Nada Lavrac & Saso Dzeroski, 1994)
Pattern Recognition and Machine Learning [27] (Christopher M. Bishop, 2006)
Machine Learning, Neural and Statistical Classification [28] (D. Michie, D.J. Spiegelhalter, & C.C. Taylor, 1999)
Information Theory, Inference, and Learning Algorithms [29] (David J.C. MacKay, 2005)
Data Mining and Business Analytics with R [30] (Johannes Ledolter, 2013)
Bayesian Reasoning and Machine Learning [31] (David Barber, 2014)
Gaussian Processes for Machine Learning [32] (C. E. Rasmussen & C. K. I. Williams, 2006)
Reinforcement Learning: An Introduction [33] (Richard S. Sutton & Andrew G. Barto, 2012)
Algorithms for Reinforcement Learning [34] (Csaba Szepesvari, 2009)
Big Data, Data Mining, and Machine Learning [35] (Jared Dean, 2014)
Modeling With Data [36](Ben Klemens, 2008)
KB – Neural Data Mining with Python Sources [37] (Roberto Bello, 2013)
Deep Learning [38] (Yoshua Bengio, Ian J. Goodfellow, & Aaron Courville, 2015)
Neural Networks and Deep Learning [39] (Michael Nielsen, 2015)
Data Mining Algorithms In R [40] (Wikibooks, 2014)
Data Mining and Analysis: Fundamental Concepts and Algorithms [41] (Mohammed J. Zaki & Wagner Meira Jr., 2014)
Theory and Applications for Advanced Text Mining [42] (Shigeaki Sakurai, 2012)
// О статистике
Think Stats: Exploratory Data Analysis in Python [43] (Allen B. Downey, 2014)
Think Bayes: Bayesian Statistics Made Simple [44] (Allen B. Downey, 2012)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction [45] (Trevor Hastie, Robert Tibshirani, & Jerome Friedman, 2008)
An Introduction to Statistical Learning with Applications in R [46] (Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani, 2013)
A First Course in Design and Analysis of Experiments [47] (Gary W. Oehlert, 2010)
// Data-визуализация
D3 Tips and Tricks [48] (Malcolm Maclean, 2015)
Interactive Data Visualization for the Web [49] (Scott Murray, 2013)
// И просто Big Data
Disruptive Possibilities: How Big Data Changes Everything [50] (Jeffrey Needham, 2013)
Real-Time Big Data Analytics: Emerging Architecture [51] (Mike Barlow, 2013)
Big Data Now: 2012 Edition [52] (O’Reilly Media, Inc., 2012)
А ещё есть у нас отличная подборка книг-двигателей карьеры [53].
Автор: icanchoose.ru
Источник [54]
Сайт-источник PVSM.RU: https://www.pvsm.ru
Путь до страницы источника: https://www.pvsm.ru/knigi/103848
Ссылки в тексте:
[1] An Introduction to Data Science: https://docs.google.com/file/d/0B6iefdnF22XQeVZDSkxjZ0Z5VUE/edit?pli=1
[2] School of Data Handbook: http://schoolofdata.org/handbook/
[3] Data Jujitsu: The Art of Turning Data into Product : http://www.oreilly.com/data/free/data-jujitsu.csp
[4] Art of Data Science: https://leanpub.com/artofdatascience
[5] The Data Science Handbook: http://www.thedatasciencehandbook.com/#get-the-book
[6] The Data Analytics Handbook: https://www.teamleada.com/handbook
[7] Data Driven: Creating a Data Culture: http://www.oreilly.com/data/free/data-driven.csp
[8] Building Data Science Teams: http://www.oreilly.com/data/free/building-data-science-teams.csp
[9] Understanding the Chief Data Officer: http://www.oreilly.com/data/free/files/understanding-chief-data-officer.pdf
[10] The Elements of Data Analytic Style: https://leanpub.com/datastyle
[11] 9 книг, которые нужно прочитать этой осенью.: http://icanchoose.ru/blog/9-novyh-knig-kotorye-stoit-prochitat-etoj-osenyu/
[12] Hadoop: The Definitive Guide: https://www.ida.liu.se/%7ETDDD43/themes/themeNOSQLlabs/2009-Hadoop.pdf
[13] Data-Intensive Text Processing with MapReduce: https://lintool.github.io/MapReduceAlgorithms/MapReduce-book-final.pdf
[14] Introduction to Machine Learning: http://arxiv.org/pdf/0904.3664.pdf
[15] Machine Learning: http://www.intechopen.com/books/machine_learning
[16] Machine Learning – The Complete Guide: https://en.wikipedia.org/wiki/Book:Machine_Learning_%E2%80%93_The_Complete_Guide
[17] Social Media Mining An Introduction: http://dmml.asu.edu/smm/book/
[18] Data Mining: Practical Machine Learning Tools and Techniques: https://www.pvsm.ruftp://ftp.ingv.it/pub/manuela.sbarra/Data%20Mining%20Practical%20Machine%20Learning%20Tools%20and%20Techniques%20-%20WEKA.pdf
[19] Mining of Massive Datasets: http://www.mmds.org/
[20] A Programmer’s Guide to Data Mining : http://A Programmer’s Guide to Data Mining http://q99.it/gJ7soup
[21] Data Mining with Rattle and R: http://mineriaddatos.wikispaces.com/file/view/Data+Mining+With+Rattle+and+R_+The+Art+of+Excavating+Data+for+Knowledge+Discovery+-+Graham+Williams.pdf
[22] Data Mining and Analysis: Fundamental Concepts and Algorithms: http://www.dataminingbook.info/pmwiki.php/Main/BookDownload
[23] Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More: http://www.learndatasci.com/wp-content/uploads/2015/08/Mining-the-Social-Web-2nd-Edition.pdf
[24] Probabilistic Programming & Bayesian Methods for Hackers: http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
[25] Data Mining Techniques For Marketing, Sales, and Customer Relationship Management: http://www.huaat.com/download/2009091Marketing.pdf
[26] Inductive Logic Programming: Techniques and Applications: http://www-ai.ijs.si/SasoDzeroski/ILPBook/ILPbook.pdf
[27] Pattern Recognition and Machine Learning: http://www.rmki.kfki.hu/%7Ebanmi/elte/Bishop%20-%20Pattern%20Recognition%20and%20Machine%20Learning.pdf
[28] Machine Learning, Neural and Statistical Classification: http://www1.maths.leeds.ac.uk/%7Echarles/statlog/
[29] Information Theory, Inference, and Learning Algorithms: http://www.inference.phy.cam.ac.uk/mackay/itprnn/book.html
[30] Data Mining and Business Analytics with R: http://www.nataraz.in/data/ebook/hadoop/Data_Mining_and_Business_Analytics_with_R__Johannes_Ledolter.pdf
[31] Bayesian Reasoning and Machine Learning: http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/240415.pdf
[32] Gaussian Processes for Machine Learning: http://www.gaussianprocess.org/gpml/chapters/RW.pdf
[33] Reinforcement Learning: An Introduction: http://people.inf.elte.hu/lorincz/Files/RL_2006/SuttonBook.pdf
[34] Algorithms for Reinforcement Learning: http://www.ualberta.ca/%7Eszepesva/papers/RLAlgsInMDPs.pdf
[35] Big Data, Data Mining, and Machine Learning: http://pdf.th7.cn/down/files/1411/Big%20Data,%20Data%20Mining,%20and%20Machine%20Learning.pdf
[36] Modeling With Data : http://modelingwithdata.org/about_the_book.html
[37] KB – Neural Data Mining with Python Sources: http://www.freeopen.org/wp-content/uploads/2013/10/KB_neural_data_mining.pdf
[38] Deep Learning: http://www.iro.umontreal.ca/%7Ebengioy/dlbook/
[39] Neural Networks and Deep Learning: http://neuralnetworksanddeeplearning.com/
[40] Data Mining Algorithms In R: https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R
[41] Data Mining and Analysis: Fundamental Concepts and Algorithms: http://www.cs.rpi.edu/%7Ezaki/PaperDir/DMABOOK.pdf
[42] Theory and Applications for Advanced Text Mining: http://www.intechopen.com/books/theory-and-applications-for-advanced-text-mining
[43] Think Stats: Exploratory Data Analysis in Python: http://greenteapress.com/thinkstats2/thinkstats2.pdf
[44] Think Bayes: Bayesian Statistics Made Simple: http://greenteapress.com/thinkbayes/
[45] The Elements of Statistical Learning: Data Mining, Inference, and Prediction: http://web.stanford.edu/%7Ehastie/local.ftp/Springer/OLD/ESLII_print4.pdf
[46] An Introduction to Statistical Learning with Applications in R: http://www-bcf.usc.edu/%7Egareth/ISL/ISLR%20Fourth%20Printing.pdf
[47] A First Course in Design and Analysis of Experiments: http://users.stat.umn.edu/%7Egary/book/fcdae.pdf
[48] D3 Tips and Tricks: https://leanpub.com/D3-Tips-and-Tricks
[49] Interactive Data Visualization for the Web: http://chimera.labs.oreilly.com/books/1230000000345/index.html
[50] Disruptive Possibilities: How Big Data Changes Everything: http://hortonworks.com/wp-content/uploads/downloads/2013/04/DisruptivePossibilities.pdf
[51] Real-Time Big Data Analytics: Emerging Architecture: http://www.pentaho.com/assets/pdf/CqPxTROXtCpfoLrUi4Bj.pdf
[52] Big Data Now: 2012 Edition: http://cdn.oreillystatic.com/oreilly/radarreport/0636920028307/Big_Data_Now_2012_Edition.pdf
[53] книг-двигателей карьеры: http://icanchoose.ru/blog/knigi-dvigateli-karery/
[54] Источник: http://megamozg.ru/post/21350/
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