Beginning Data Science with R

en Limba Engleză Hardback – 18 dec 2014

“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.

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Specificații

ISBN-13: 9783319120652
ISBN-10: 3319120654
Pagini: 157
Ilustrații: XI, 157 p. 155 illus., 26 illus. in color.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.42 kg
Ediția:2014
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland

Public țintă

Graduate

Cuprins

Introduction.- Overview of the R Programming Language.- Getting Data into R.- Data Visualization.- Exploratory Data Analysis.- Regression.- Classification.- Text Mining.

Recenzii

“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 scientistsor researchers as biologists, lab, criminologists, and also Finance pros or actuarials.” (Compudicted, compudicted.wordpress.com, February, 2015)

Notă biografică

Dr. Manas A. Pathak received a BTech degree in computer science from Visvesvaraya National Institute of Technology, Nagpur, India, in 2006, and MS and PhD degrees from the Language Technologies Institute at Carnegie Mellon University (CMU) in 2009 and 2012 respectively. His PhD thesis on "Privacy-Preserving Machine Learning for Speech Processing" was published as a monograph in the Springer best thesis series. His research received significant press coverage, including articles in the Economist and MIT Tech Review. He has many years of experience with data analysis using the R programming language. He is currently working as a staff software engineer at @WalmartLabs.

Textul de pe ultima copertă

“Data Science with R” deals with implementing many useful data analysis methodologies with the R programming language. The target audience for this book is non-R programmers and non-statisticians. The book will cover all the necessary concepts from the basics to state-of-the-art technologies like working with big data. The author attempts to strike a balance between the “how”: specific processes and methodologies, while also talking about the “why”: giving an intuition behind how a particular technique works, so that the reader can apply the generalized solution to the problem at hand.

Caracteristici

Introduces fundamental data science methodologies using the R programming language Covers concepts through real-world datasets and case studies Examines cutting edge topics in both research and commercial applications Includes supplementary material: sn.pub/extras

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