Galit Shmueli et al. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro

Wiley, 2016. — 464 p. in color. — ISBN: 1118877438, 9781118877432.Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro presents an applied and interactive approach to data mining.
Featuring hands-on applications with JMP Pro, a statistical package from the SAS Institute, the book
uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting.
Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro also includes:
Detailed summaries that supply an outline of key topics at the beginning of each chapter
End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material
Data-rich case studies to illustrate various applications of data mining techniques
A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors
Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field.Overview of the data mining process
Data visualization
Dimension reduction
Evaluating predictive performance
Multiple linear regression
K-nearest neighbors (kNN)
The naive Bayes classifier
Classification and regression trees
Logistic regression
Neural nets
Discriminant analysis
Combining methods : ensembles and uplift modeling
Cluster analysis
Handling time series
Regression-based forecasting
Smoothing methods
Cases