Customer and Business Analytics (eBook, 2015) [WorldCat.org]
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Customer and Business Analytics

Author: Daniel Putler; Robert Krider; Safari, an O'Reilly Media Company.
Publisher: Chapman and Hall/CRC, 2015.
Edition/Format:   eBook : Document : English : 1st editionView all editions and formats
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  Read more...
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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Daniel Putler; Robert Krider; Safari, an O'Reilly Media Company.
OCLC Number: 1112594572
Description: 1 online resource (315 pages)
Contents: I Purpose and Process: Database Marketing and Data Mining. A Process Model for Data Mining-CRISP-DM. II Predictive Modeling Tools: Basic Tools for Understanding Data. Multiple Linear Regression. Logistic Regression. Lift Charts. Tree Models. Neural Network Models. Putting It All Together. III Grouping Methods: Ward's Method of Cluster Analysis and Principal Components. K-Centroids Partitioning Cluster Analysis. Bibliography. Index.
Responsibility: Putler, Daniel.

Abstract:

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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"This book is derived from a lecture course in data mining for MBA students. ... assumes very little in the way of mathematical or statistical background. The writing style is generally good, and the Read more...

 
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