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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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...
Disclosure: the three books highlighted here do not have affiliate links.
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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.
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.
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.