Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner - Hardcover

Shmueli, Galit; Patel, Nitin R.; Bruce, Peter C.

Hardcover

ISBN 10: 0470084855 ISBN 13: 9780470084854

Publisher: Wiley–Blackwell, 2007

Synopsis

Learn how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business Intelligence

In today′s world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success. Data Mining for Business Intelligence, which was developed from a course taught at the Massachusetts Institute of Technology′s Sloan School of Management, and the University of Maryland′s Smith School of Business, uses real data and actual cases to illustrate the applicability of data mining intelligence to the development of successful business models.

Featuring XLMiner, the Microsoft Office Excel add–in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of data mining techniques are presented with hands–on, business–oriented applications. An abundant amount of exercises and examples are provided to motivate learning and understanding.

Data Mining for Business Intelligence:

• Provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis
• Features a business decision–making context for these key methods
• Illustrates the application and interpretation of these methods using real business cases and data

This book helps readers understand the beneficial relationship that can be established between data mining and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiser business decisions.

About this title

About the Author

GALIT SHMUELI, PHD, is Assistant Professor of Statistics in the Decision and Information Technologies Department of the Robert H. Smith School of Business at the University of Maryland.

NITIN R. PATEL, PHD, is Chairman, Founder, and Chief Technology Officer of Cambridge–based Cytel Incorporated and a Visiting Professor in the Engineering Systems Division at the Massachusetts Institute of Technology.

PETER C. BRUCE is President and owner of statistics.com, the leading provider of professional development courses in statistics.

In today′s world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success. Data Mining for Business Intelligence, which was developed from a course taught at the Massachusetts Institute of Technology′s Sloan School of Management, and the University of Maryland′s Smith School of Business, uses real data and actual cases to illustrate the applicability of data mining intelligence to the development of successful business models.

Featuring XLMiner, the Microsoft Office Excel add–in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of data mining techniques are presented with hands–on, business–oriented applications. An abundant amount of exercises and examples are provided to motivate learning and understanding.

Data Mining for Business Intelligence:

• Provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis
• Features a business decision–making context for these key methods
• Illustrates the application and interpretation of these methods using real business cases and data

This book helps readers understand the beneficial relationship that can be established between data mining and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiser business decisions.

Edition details

• Publisher: Wiley–Blackwell
• Publication date: 2007
• Language: English
• ISBN 10: 0470084855
• ISBN 13: 9780470084854
• Binding: Hardcover
• Number of pages: 298