Business Analytics - by Johannes Ledolter (Hardcover)

Book Synopsis

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.

Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:

• A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools
• Illustrations of how to use the outlined concepts in real-world situations
• Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials
• Numerous exercises to help readers with computing skills and deepen their understanding of the material

Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

From the Back Cover

Showcases R's critical role in the world of business

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible robust computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.

Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:

• A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools
• Illustrations of how to use the outlined concepts in real-world situations
• Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials
• Numerous exercises to help readers with computing skills and deepen their understanding of the material

Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

Review Quotes

"I first taught a Ph.D. level course in business applications of data mining 10 years ago. I regularly search the web, looking for business-oriented data mining books, and this is the first one I have found that is suitable for an MS in business analytics. I plan to use it. Anyone who teaches such a class and is inclined toward R should consider this text." (Journal of the American Statistical Association, 1 January 2014)

About the Author

JOHANNES LEDOLTER, PhD, is Professor in both the Department of Management Sciences and the Department of Statistics and Actuarial Science at the University of Iowa. He is a Fellow of the American Statistical Association and the American Society for Quality, and an Elected Member of the International Statistical Institute. Dr. Ledolter is the coauthor of Statistical Methods for Forecasting, Achieving Quality Through Continual Improvement, and Statistical Quality Control: Strategies and Tools for Continual Improvement, all published by Wiley.