Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro

Book Description

Data Mining for 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, , 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 , healthcare, education, and any other data-rich field.

Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of 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 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 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 : 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 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.

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 Cluster Analysis

Part VI: Forecasting Time Series
Chapter 15 Handling Time Series
Chapter 16 Regression-Based Forecasting
Chapter 17 Smoothing Methods

Part VII: Cases
Chapter 18 Cases

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