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Beginning Data Science with R (Paperback)
  • Beginning Data Science with R (Paperback)
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Beginning Data Science with R (Paperback)

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£74.99
Paperback 157 Pages / Published: 30/04/2017
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"We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library.
The goal of "Beginning Data Science with R" is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.

Publisher: Springer International Publishing AG
ISBN: 9783319374734
Number of pages: 157
Weight: 273 g
Dimensions: 235 x 155 x 9 mm
Edition: Softcover reprint of the original 1st ed. 201


MEDIA REVIEWS

"The target audience for this book is non-R programmers and non-statisticians. ... if you want to get started with R and/or new statistical procedures have a look at this book. It can be quite helpful." (David E. Booth, Technometrics, Vol. 58 (2), 2016)

"This book is written for coders who already know how to code to learn R for data science. The book covers how to install and use R ... . This is a good book to get you stated coding in R for data science." (Mary Anne, Cats and Dogs with Data, maryannedata.com, May, 2015)

"A comprehensive, yet short tutorial on practical application of R to the modern data science tasks or projects. ... Who I recommend it to: managers who work on data projects, technical team leaders, CS students, Business Intelligence professionals, beginner architects, general computer academia, statisticians, several categories of scientists or researchers as biologists, lab, criminologists, and also Finance pros or actuarials." (Compudicted, compudicted.wordpress.com, February, 2015)

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