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Customer and business analytics: applied data mining for business decision making using R

By: Putler, Daniel S.
Contributor(s): Krider, Robert E.
Material type: materialTypeLabelBookSeries: Chapman & Hall/CRC the R Series. Publisher: Boca Raton CRC Press 2012Description: xxvi, 289 p.ISBN: 9781466503960.Subject(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.
List(s) this item appears in: Big data | VR_Data Analytics, Data Visualization and Big Data
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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.

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