Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner, Third Edition
- 8h 31m
- Galit Shmueli, Nitin R. Patel, Peter C. Bruce
- John Wiley & Sons (US)
- 2016
Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data.
Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes:
- Real-world examples to build a theoretical and practical understanding of key data mining methods
- End-of-chapter exercises that help readers better understand the presented material
- Data-rich case studies to illustrate various applications of data mining techniques
- Completely new chapters on social network analysis and text mining
- A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides
Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.
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, The 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.
Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.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, 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.
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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Association Rules and Collaborative Filtering
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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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Social Network Analytics
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Text Mining
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Cases
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References
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Data Files Used in the Book
