Hardback : £156.00
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.
Show moreCustomer 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.
Show moreI 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.
Dr. Daniel S. Putler is a Data Artisan in Residence at Alteryx, a business intelligence/analytics software company. Dr. Robert E. Krider is a professor of marketing in the Beedie School of Business at Simon Fraser University. He has also taught in Hong Kong, Shanghai, Portugal, and Germany. His research tackles questions of customer and competitor behavior in retailing and media industries.
"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 book should prove useful to its target audience."
—David Scott, International Statistical Review (2013), 81, 2
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