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Customer and business analytics : applied data mining for business decision making using R / Daniel S. Putler, Robert E. Krider.

By: Contributor(s): Material type: TextTextSeries: Chapman & Hall/CRC the R seriesPublication details: Boca Raton, FL : CRC Press, c2012.Description: xxvi, 289 p. : ill. ; 26 cmISBN:
  • 9781466503960
  • 1466503963
Subject(s): DDC classification:
  • 658.4/0302855133 23
LOC classification:
  • HF5415.126 .P88 2012
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.
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Holdings
Item type Current library Call number Status Date due Barcode
REGULAR University of Wollongong in Dubai Main Collection 658.40302855133 PU CU (Browse shelf(Opens below)) Available T0010886

Includes bibliographical references and index.

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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