- #DATA MINING FOR BUSINESS ANALYTICS HOW TO#
- #DATA MINING FOR BUSINESS ANALYTICS MANUAL#
- #DATA MINING FOR BUSINESS ANALYTICS SOFTWARE#
Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has designed and instructed data mining courses since 2004 at University of Maryland,, Indian School of Business, and National Tsing Hua University, Taiwan. Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. 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
#DATA MINING FOR BUSINESS ANALYTICS MANUAL#
If not the bible, it is at the least a definitive manual on the subject.” “ 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. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutionsĭata Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics.End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented.More than a dozen case studies demonstrating applications for the data mining techniques described.Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students.Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government.
It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. This is the fifth version of this successful text, and the first using R.
#DATA MINING FOR BUSINESS ANALYTICS HOW TO#
Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities.
#DATA MINING FOR BUSINESS ANALYTICS SOFTWARE#
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration
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