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…mehr
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.
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.
Inhaltsangabe
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
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
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