Handbook of Statistical Analysis (3rd Ed.) AI and ML Applications
Handbook of Statistical Analysis: AI and ML Applications, Third Edition, is a comprehensive introduction to all stages of data analysis, data preparation, model building and model evaluation. This valuable resource is useful to students and professionals across a variety of fields and settings: business analysts, scientists, engineers and researchers in academia and industry. General descriptions of algorithms together with case studies help readers understand technical and business problems, weigh the strengths and weaknesses of modern data analysis algorithms, and employ the right analytical methods for practical application. This resource is an ideal guide for users who want to address massive and complex datasets with many standard analytical approaches and be able to evaluate analyses and solutions objectively. It includes clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques; offers accessible tutorials; and discusses their application to real-world problems.
PREFACE & INTRODUCTION Foreword for the 3rd Edition About the Authors (Bios with Head Photo of the 3 co-authors) Foreword 1 – from the 1st Edition Foreword 2 - from the 1st Edition ENDORSEMENTS & Reviewer Blurbs – from the 1st Edition and the 2nd Edition 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 (Hurrdata.sta) 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
Dr. Gary Miner PhD 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 scientific meetings, bringing together all the leaders in the field of genetics of Alzheimer's from several countries, resulting in the first major book on the genetics of Alzheimer’s disease. In the mid-1990s, Dr. Miner turned his data analysis interests to the business world, joining the team at StatSoft and deciding to specialize in data mining. He started developing what eventually became the Handbook of Statistical Analysis and Data Mining Applications (co-authored with Drs. Robert A. Nisbet and John Elder), which received the 2009 American Publishers Award for Professional and Scholarly Excellence (PRO
- Offers valuable tutorials and step-by-step instruction on how to use supplied tools to build models
- Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions
- Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications
- Contains practical advice from successful real-world implementations
Date de parution : 09-2024
Ouvrage de 650 p.
21.5x27.6 cm
Thèmes de Handbook of Statistical Analysis :
Mots-clés :
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