Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner, 3rd Edition

Praise for the Second Edition

“…full of vivid and thought-provoking anecdotes… needs to be read by anyone with a serious interest in research and marketing.”

– Research Magazine

“Shmueli et al. have done a wonderful job in presenting the field of data mining – a welcome addition to the literature.”

– ComputingReviews.com

“Excellent choice for business analysts…The book is a perfect fit for its intended audience.” 

– Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization

“…extremely well organized, clearly written and introduces all of the basic ideas quite well.” 

– Robert L. Phillips, Professor of Professional Practice, Columbia Business School

Data 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
• Free 140-day license to use XLMiner for Education software

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.

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.

Table of Contents

Part I: Preliminaries
Chapter 1: Introduction
Chapter 2: Overview of the Data Mining Process

Part II: Data Exploration and Dimension Reduction
Chapter 3: Data Visualization
Chapter 4: Dimension Reduction

Part III: Performance Evaluation
Chapter 5: Evaluating Predictive Performance

Part IV: Prediction and Classification Methods
Chapter 6: Multiple Linear Regression
Chapter 7: k-Nearest-Neighbors (k-NN)
Chapter 8: The Naive Bayes Classifier
Chapter 9: Classification and Regression Trees
Chapter 10: Logistic Regression
Chapter 11: Neural Nets
Chapter 12: Discriminant Analysis
Chapter 13: Combining Methods: Ensembles and Uplift Modeling

Part V: Mining Relationships among Records
Chapter 14: Association Rules and Collaborative Filtering
Chapter 15: Cluster Analysis

Part VI: Forecasting Time Series
Chapter 16: Handling Time Series
Chapter 17: Regression-Based Forecasting
Chapter 18: Smoothing Methods

Part VII: Data Analytics
Chapter 19: Social Network Analytics
Chapter 20: Text Mining

Part VIII: Cases
Chapter 21: Cases