Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro
- 7h 56m
- Galit Shmueli, Mia L. Stephens, Nitin R. Patel, Peter C. Bruce
- John Wiley & Sons (US)
- 2017
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
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.
About the Authors
Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. 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 authored over 70 journal articles, books, textbooks, and book chapters, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley.
Peter C. Bruce is President and Founder of the Institute for Statistics Education at statistics.com He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner ®, Third Edition, both published by Wiley.
Mia Stephens is Academic Ambassador at JMP®, a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, also published by Wiley.
Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley.
In this Book
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Foreword
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Introduction
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Overview of the Data Mining Process
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Data Visualization
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Dimension Reduction
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Evaluating Predictive Performance
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Multiple Linear Regression
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k-Nearest Neighbors (k-NN)
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The Naive Bayes Classifier
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Classification and Regression Trees
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Logistic Regression
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Neural Nets
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Discriminant Analysis
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Combining Methods—Ensembles and Uplift Modeling
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Cluster Analysis
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Handling Time Series
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Regression-Based Forecasting
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Smoothing Methods
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Cases
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References
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Data Files Used in the Book
