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Free Data Science eBooks - May 2017

Every month we scour the internet seeking out free eBooks to help you on your educational journey, and this month has been no different.

I hope these books prove to be a valuable resource to you and that you will visit regularly (and invite your friends too).

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Disclosure: as well as links to the free ebooks we may also include links to non-free versions of the same books, and we may earn an affiliate commission for purchases you make when using those links

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3 Free Data Science Books for May

 

This month, we have a book about Data Scientists and the work that they do, one about probability and statistical modelling and one about machine learning. They're all FREE, so what are you waiting for...

 

Enjoy!

 


 

Analyzing the Analyzers: An Introspective Survey of Data Scientists and Their Work

by Harlan Harris, Sean Murphy and Marck Vaisman

Despite the excitement around "data science," "big data," and "analytics," the ambiguity of these terms has led to poor communication between data scientists and organizations seeking their help.

In this report, authors Harlan Harris, Sean Murphy, and Marck Vaisman examine their survey of several hundred data science practitioners in mid-2012, when they asked respondents how they viewed their skills, careers, and experiences with prospective employers.

The results are striking.


NOTE: This ebook is currently listed as free only as a pdf. If you would rather have an alternative version (including paperback) you can get a paid copy from Amazon ← *big fat scary affiliate link!*

 

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From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science

by Norm Matloff

The materials here form a textbook for a course in mathematical probability and statistics for computer science students.

Computer science examples are used throughout, in areas such as: computer networks; data and text mining; computer security; remote sensing; computer performance evaluation; software engineering; data management; etc..

The R statistical/data manipulation language is used throughout. Since this is a computer science audience, a greater sophistication in programming can be assumed. It is recommended that the R tutorial, R for Programmers, be used as a supplement.

Throughout the units, mathematical theory and applications are interwoven, with a strong emphasis on modelling.


NOTE: This ebook is not currently available to purchase at Amazon, but a similar book you might like to check out is Statistical and Probabilistic Models in Reliability ← *big fat scary affiliate link!*

 

 

A Course in Machine Learning

by Hal Daumé III

CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).

Its focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a graduate course could probably cover the entire material and then some.


NOTE: This ebook is not currently available to purchase at Amazon, but a similar book you might like to check out is A First Course in Machine Learning ← *big fat scary affiliate link!*

 


 

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