Acharya S. Data Analytics Using R

McGraw-Hill Education (India), 2018. — 579 p. — ISBN13: 978-93-5260-524-8.This book is aimed at undergraduate students of computer science and engineering. The book will be useful companion for IT professionals to data analysts and decision makers responsible for driving strategic initiatives and management graduates and business analysts, engaged in self-study. This book by Acharya unleashes the power of R as a statistical data analytics and visualization tool and introduces the learners to several data mining algorithms and chart forms/visualizations. It has good emphasis on "asking the right questions".
We are in very exciting times! Statistical computing and high-scale data analysis tasks need a new category of computer language other than the procedural and object-oriented programming languages. The main objective of this category of language is to support various types of statistical analysis and data analysis tasks rather than developing new software. There are mounds of data available today which can be analyzed in different ways and can provide a wide range of useful insights for different operations in different industries. However, the problem was the lack of support, tools and techniques for data analysis for different purposes. R, a statistical and analytical language, has come to our rescue! To add to the benefits, it is an open-source.
Salient Features
Exhaustive coverage includes installation of R and its package, getting accustomed to R interface and R commands, working with data from disparate data sources (csv, JSON, XML, RDBMS etc.), getting conversant with classification, clustering, association rule mining, regression, text mining etc.
12 Case studies namely Insurance Fraud Detection, Customer Insights Analysis, Sales Forecasting, Credit Card spending by Customer groups and helping retailers predict in-store customer traffic
Pedagogical features
300 plus chapter-end and check your progress questions for self-assessment
200 multiple-choice questions
10 plus hands-on practical exercises
Exhaustive illustrationsIntroduction to R
Getting Started with R
Loading and Handling Data in R
Exploring Data in R Chapter 5 Linear Regression using R
Linear Regression using R
Logistic Regression
Decision Tree
Time Series in R
Clustering
Association Rules
Text Mining
Parallel Computing with R