Customer and business analytics : applied data mining for business decision making using R /
By: Putler, Daniel S
Title By: Krider, Robert E
Material type: BookSeries: Chapman & Hall/CRC the R series.Publisher: Boca Raton, FL : CRC Press, c2012.Description: xxvi, 289 p. : ill. ; 26 cm.ISBN: 9781466503960; 1466503963Subject(s): Database marketing -- Software | Data mining | Decision making -- Data processing | R (Computer program language) | Database managementDDC classification: 658.4/0302855133Item type | Home library | Call number | Status | Date due | Barcode | Item holds |
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REGULAR | University of Wollongong in Dubai Main Collection | 658.40302855133 PU CU (Browse shelf) | Available | T0010886 |
, Shelving location: Main Collection Close shelf browser
658.40301 MO DA Data-driven organization design : sustaining the competitive edge through organizational analytics / | 658.40301 SK IN Introduction to process control : | 658.403019 SH BE Behavioral risk management : managing the psychology that drives decisions and influences operational risk | 658.40302855133 PU CU Customer and business analytics : applied data mining for business decision making using R / | 658.403028553 RU MA Making effective business decisions using Microsoft Project / | 658.403072 SA RE Research methods for business students / | 658.403072 ZI BU Business research methods / |
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.