TY  - BOOK
AU  - Hodeghatta,Umesh R.
AU  - Nayak,Umesh
TI  - Business analytics using R - A practical approach
SN  - 9781484225134
U1  - 658.40380 
PY  - 2017///
CY  - New York, : 
PB  - Apress, Distributed to the Book trade worldwide by Springer
KW  - Business
KW  - Data processing
KW  - Management information systems
KW  - R (Computer program language)
KW  - Computer Science
KW  - Big Data
KW  - Programming Techniques
KW  - Programming Languages, Compilers, Interpreters
KW  - Data Mining and Knowledge Discovery
KW  - Information Storage and Retrieval
KW  - Probability and Statistics in Computer Science
N1  - Includes bibliographical references (pages 267-272) and index; Overview of business analytics -- Introduction to R -- R for data analysis -- Introduction to descriptive analytics -- Business analytics process and data exploration -- Supervised machine learning : classification -- Unsupervised machine learning -- Simple linear regression -- Multiple linear regression -- Logistic regression -- Big data analysis : introduction and future trends
N2  - Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. You will:? Write R programs to handle data? Build analytical models and draw useful inferences from them? Discover the basic concepts of data mining and machine learning? Carry out predictive modeling? Define a business issue as an analytical problem
ER  -