Data Mining for Business Analytics

Data Mining for Business Analytics is used at over 560 universities and colleges, and has been translated into Korean and Chinese. It has been adapted for four software environments (R, Python, Excel and JMP) and, since it was first published in 2007, has been through 11 editions.

Popular with practitioners, researchers and students, it presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. This book serves to anchor the curriculum in predictive analytics, which was developed and is taught by the author team.

The book was co-authored by Galit ShmueliPeter Bruce, Nitin R. Patel, Inbal Yahav, and Peter Gedeck. This author team teaches 13 courses at The Institute for Statistics Education at (an Elder Research Company).

Click here to view the Table of Contents from the book.

“This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at least a definitive manual on the subject.”
Gareth James
University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best selling book An Introduction to Statistical Learning, with Applications in R.