Ledolter Data Mining and Business Analytics with R

1. Auflage 2013
ISBN: 978-1-118-57215-3
Verlag: John Wiley & Sons
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 368 Seiten, E-Book

Collecting, analyzing, and extracting valuable information froma large amount of data requires easily accessible, robust,computational and analytical tools. Data Mining and BusinessAnalytics with R utilizes the open source software R for theanalysis, exploration, and simplification of large high-dimensionaldata sets. As a result, readers are provided with the neededguidance to model and interpret complicated data and become adeptat building powerful models for prediction and classification.
Highlighting both underlying concepts and practicalcomputational skills, Data Mining and Business Analytics withR begins with coverage of standard linear regression and theimportance of parsimony in statistical modeling. The book includesimportant topics such as penalty-based variable selection (LASSO);logistic regression; regression and classification trees;clustering; principal components and partial least squares; and theanalysis of text and network data. In addition, the bookpresents:
* A thorough discussion and extensive demonstration of thetheory behind the most useful data mining tools
* Illustrations of how to use the outlined concepts inreal-world situations
* Readily available additional data sets and related Rcode allowing readers to apply their own analyses to the discussedmaterials
* Numerous exercises to help readers with computing skillsand deepen their understanding of the material
Data Mining and Business Analytics with R is an excellentgraduate-level textbook for courses on data mining and businessanalytics. The book is also a valuable reference for practitionerswho collect and analyze data in the fields of finance, operationsmanagement, marketing, and the information sciences.

Autoren/Hrsg.

Ledolter, Johannes

Über Autor(innen)

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.