Beginning Data Science with R
Springer International Publishing (Verlag)
978-3-319-37473-4 (ISBN)
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.
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.
Introduction.- Overview of the R Programming Language.- Getting Data into R.- Data Visualization.- Exploratory Data Analysis.- Regression.- Classification.- Text Mining.
"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)
| Erscheinungsdatum | 23.03.2017 |
|---|---|
| Zusatzinfo | XI, 157 p. 155 illus., 26 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 270 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Technik | |
| Schlagworte | Appl.Mathematics/Computational Methods of Engineer • Creating Tag Clouds • Engineering: general • Imaging systems and technology • Mathematical and statistical software • mathematics and statistics • Maths for engineers • R Code • R Interfacing • R Programming • Signal, Image and Speech Processing • Signal Processing • Social Network Data Analysis • statistical modeling • Statistics and Computing/Statistics Programs • time series data |
| ISBN-10 | 3-319-37473-7 / 3319374737 |
| ISBN-13 | 978-3-319-37473-4 / 9783319374734 |
| Zustand | Neuware |
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