Bob Nisbet, PhD, is a Data Scientist, currently modeling precancerous colon polyp presence with clinical data at the UC-Irvine Medical Center. He has experience in predictive modeling in Telecommunications, Insurance, Credit, Banking. His academic experience includes teaching in Ecology and in Data Science. His industrial experience includes predictive modeling at AT&T, NCR, and FICO. He has worked also in Insurance, Credit, membership organizations (e.g. AAA), Education, and Health Care industries. He retired as an Assistant Vice President of Santa Barbara Bank & Trust in charge of business intelligence reporting and customer relationship management (CRM) modeling.

Part I - Introduction
1. Historical Background to Analytics
2. Theory
3. Data Mining and Predictive Analytic Process
4. Data Science Tool Types: Which one is Best?

Part II - Data Preparation
5. Data Access
6. Data Understanding
7. Data Visualization
8. Data Cleaning
9. Data Conditioning
10. Feature Engineering
11. Feature Selection
12. Data Preparation Cookbook

Part III - Modeling

13. Algorithms
14. Modeling
15. Model Evaluation and Enhancement
16. Ensembles & Complexity
17. Deep Learning vs. Traditional ML
18. Explainable AI (XAI) put after Deep Learning
19. Human in the Loop

Part IV - Applications
20. GENERAL OVERVIEW of an Application - Healthcare Delivery and Medical Informatics
21. Specific Application: Business: Customer Response
22. Specific Application: Education: Learning Analytics
23. Specific Application: Medical Informatics: Colon Cancer Screening
24. Specific Application: Financial: Credit Risk
25. Specific FUTURE Application: The ‘INTELLIGENCE AGE (Revolution)’: LLMs like ChatGPT - Tiny ML - H.U.M.A.N.E. - Etc.

Part V - Right Models - Luck - & Ethics of Analytics
26. Right Model for the Right Use
27. Ethics in Data Science
28. Significance of Luck

Part VI - Tutorials and Case Studies
Tutorial A Example of Data Mining Recipes Using Statistica Data Miner 13
Tutorial B Analysis of Hurricane Data ([...]) Using the Statistica Data Miner 13
Tutorial C Predicting Student Success at High-Stakes Nursing Examinations (NCLEX) Using SPSS Modeler and Statistica Data Miner 13
Tutorial D Constructing a Histogram Using MidWest Company Personality Data Using KNIME
Tutorial E Feature Selection Using KNIME
Tutorial F Medical/Business Tutorial Using Statistica Data Miner 13
Tutorial G A KNIME Exercise, Using Alzheimer’s Training Data of Tutorial F (RAN note: This tutorial refers to the data used in Tutorial I, and it should be changed to refer to Tutorial F. I propose a new title: Tutorial G Medical/Business Tutorial with Tutorial F Data Using KNIME.
Tutorial H Data Prep 1-1: Merging Data Sources Using KNIME
Tutorial I Data Prep 1-2: Data Description Using KNIME
Tutorial J Data Prep 2-1: Data Cleaning and Recoding Using KNIME
Tutorial K Data Prep 2-2: Dummy Coding Category Variables Using KNIME
Tutorial L Data Prep 2-3: Outlier Handling Using KNIME
Tutorial M Data Prep 3-1: Filling Missing Values With Constants Using KNIME
Tutorial N Data Prep 3-2: Filling Missing Values With Formulas Using KNIME
Tutorial O Data Prep 3-3: Filling Missing Values With a Model Using KNIME

Back Matter:
Appendix-A - Listing of TUTORIALS and other RESOUCES on this book’s COMPANION WEB PAGE
Appendix B - Instructions on HOW TO USE this book’s COMPANION WEB PAGE