Beginning Data Science with R

1. Front Matter

   Pages i-xi

   PDF

2. Introduction

   • Manas A. Pathak

   Pages 1-4

3. Overview of the R Programming Language

   • Manas A. Pathak

   Pages 5-17

4. Getting Data into R

   • Manas A. Pathak

   Pages 19-30

5. Data Visualization

   • Manas A. Pathak

   Pages 31-60

6. Exploratory Data Analysis

   • Manas A. Pathak

   Pages 61-85

7. Regression

   • Manas A. Pathak

   Pages 87-114

8. Classification

   • Manas A. Pathak

   Pages 115-136

9. Text Mining

   • Manas A. Pathak

   Pages 137-157

“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.

• Creating Tag Clouds
• R Code
• R Interfacing
• R Programming
• Social Network Data Analysis
• Statistical Modeling
• Time Series Data

“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)

• Foster City, USA

  Manas A. Pathak

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.

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