Customer and business analytics: applied data mining for business decision making using R Putler, Daniel S.
By: Putler, Daniel S
Contributor(s): Krider, Robert E
Material type: 
TextSeries: Chapman & Hall/CRC the R SeriesPublisher: Boca Raton CRC Press 2012Description: xxvi, 289 p.ISBN: 9781466503960Subject(s): Database marketing - Software | Data mining | Decision making - Data processing | R - Computer program language | Database managementDDC classification: 658.40302855133 Summary: Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Extensively classroom-tested, the text is ideal for students in customer and business analytics or applied data mining as well as professionals in small- to medium-sized organizations.
The book offers an intuitive understanding of how different analytics algorithms work. Where necessary, the authors explain the underlying mathematics in an accessible manner. Each technique presented includes a detailed tutorial that enables hands-on experience with real data. The authors also discuss issues often encountered in applied data mining projects and present the CRISP-DM process model as a practical framework for organizing these projects.
Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities.
| Item type | Current location | Item location | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
| Books | Vikram Sarabhai Library | Slot 2050 (2 Floor, East Wing) | Non-fiction | 658.40302855133 P8C8 (Browse shelf) | Available | 180667 |
Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Extensively classroom-tested, the text is ideal for students in customer and business analytics or applied data mining as well as professionals in small- to medium-sized organizations.
The book offers an intuitive understanding of how different analytics algorithms work. Where necessary, the authors explain the underlying mathematics in an accessible manner. Each technique presented includes a detailed tutorial that enables hands-on experience with real data. The authors also discuss issues often encountered in applied data mining projects and present the CRISP-DM process model as a practical framework for organizing these projects.
Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities.
There are no comments for this item.