Handbook of Statistical Analysis and Data Mining Applications

Preface
Forwards
Introduction

PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process
Chapter 1. History - The Phases of Data Analysis throughout the Ages
Chapter 2. Theory
Chapter 3. The Data Mining Process
Chapter 4. Data Understanding and Preparation
Chapter 5. Feature Selection - Selecting the Best Variables
Chapter 6: Accessory Tools and Advanced Features in Data

PART II: - The Algorithms in Data Mining and Text Mining, and the Organization of the Three most common Data Mining Tools
Chapter 7. Basic Algorithms
Chapter 8: Advanced Algorithms
Chapter 9. Text Mining
Chapter 10. Organization of 3 Leading Data Mining Tools
Chapter 11. Classification Trees = Decision Trees
Chapter 12. Numerical Prediction (Neural Nets and GLM
Chapter 13. Model Evaluation and Enhancement
Chapter 14. Medical Informatics
Chapter 15. Bioinformatics
Chapter 16. Customer Response Models
Chapter 17. Fraud Detection

PART III: Tutorials - Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses
Tutorials

PART IV: Paradox of Complex Models; using the "right model for the right use , on-going development, and the Future.
Chapter 18: Paradox of Ensembles and Complexity
Chapter 19: The Right Model for the Right Use
Chapter 20: The Top 10 Data Mining Mistakes
Chapter 21: Prospect for the Future - Developing Areas in Data Mining
Chapter 22: Summary

GLOSSARY of STATISICAL and DATA MINING TERMS
INDEX
CD - With Additional Tutorials, data sets, Power Points, and Data Mining software

Dr. Robert Nisbet was trained initially in Ecology and Ecosystems Analysis. He has over 30 years of experience in complex systems analysis and modeling, most recently as a Researcher (University of California, Santa Barbara). In business, he pioneered the design and development of configurable data mining applications for retail sales forecasting, and Churn, Propensity-to-buy, and Customer Acquisition in Telecommunications, Insurance, Banking, and Credit industries. In addition to data mining, he has expertise in data warehousing technology for Extract, Transform, and Load (ETL) operations, Business Intelligence reporting, and data quality analyses. He is lead author of the "Handbook of Statistical Analysis & Data Mining Applications (Academic Press, 2009), and a co-author of "Practical Text Mining" (Academic Press, 2012), and co-author of "Practical Predictive Analytics and Decisioning Systems for Medicine (Academic Press, 2015). Currently, he serves as an Instructor in the University of California, Irvine Predictive Analytics Certificate Program, teaching online and on-campus courses in Effective Data preparation, and Applications of Predictive Analytics. Additionally Bob is in the last stages of writing another book on 'Data Preparation for Predictive Analytic Modeling. Dr. Gary Miner received a B.S. from Hamline University, St. Paul, MN, with biology, chemistry, and education majors; an M.S. in zoology and population genetics from the University of Wyoming; and a Ph.D. in biochemical genetics from the University of Kansas as the recipient of a NASA pre-doctoral fellowship. He pursued additional National Institutes of Health postdoctoral studies at the U of Minnesota and U of Iowa eventually becoming immersed in the study of affective disorders and Alzheimer's disease.

In 1985, he and his wife, Dr. Linda Winters-Miner, founded the Familial Alzheimer's Disease Research Foundation, which became a leading force in organizing both local and international

"...If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner." - Eric Siegel, Ph.D., President, Prediction Impact, Inc. and Founding Chair, Predictive Analytics World "Great introduction to the real-world process of data mining. The overviews, practical advice, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners." -- Karl Rexer, PhD (President & Founder of Rexer Analytics, Boston, Massachusetts)

"...If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner."

- Eric Siegel, Ph.D., President, Prediction Impact, Inc. and Founding Chair, Predictive Analytics World

"Great introduction to the real-world process of data mining. The overviews, practical advice, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners.

-- Karl Rexer, PhD (President & Founder of Rexer Analytics, Boston, Massachusetts)