Handbook of Statistical Analysis
AI and ML Applications
by Robert Nisbet, Gary D. Miner, Keith McCormick
3rd Edition
Publisher: Academic Press
Book Details
| Print ISBN | 9780443132735 |
| eText ISBN | 9780443158469 |
| Publisher | Academic Press |
| Publishing Year | 2024 |
| Edition | 3rd Edition |
| Language | English |
| Pages | 650 |
The Handbook of Statistical Analysis, 3rd Edition, presents a thorough foundation in modern statistical analysis and data mining techniques. Published by Academic Press in September 2024, this handbook outlines the complete analytical lifecycle by detailing initial data analysis, preparation methodologies, predictive model construction, and systematic model evaluation.
The handbook introduces historical background context, core theory, the overarching predictive analytic process, and distinct categories of data science software tools. It then examines procedures for data access, data understanding, graphical visualization, data cleaning, data conditioning, feature engineering, and feature selection, providing step-by-step guidance for organizing data before running analytical algorithms.
To reinforce applied techniques, the publication offers practical step-by-step tutorials detailing model construction using software tools such as Statistica Data Miner, SPSS Modeler, and KNIME. This handbook supports advanced undergraduate and graduate students completing formal upper-level coursework in Statistical Analysis & Data Mining.
Table of Contents
Chapter 1: Historical Background to Analytics
Chapter 2: Theory
Chapter 3: Data Mining and Predictive Analytic Process
Chapter 4: Data Science Tool Types: Which one is Best?
Chapter 5: Data Access
Chapter 6: Data Understanding
Chapter 7: Data Visualization
Chapter 8: Data Cleaning
Chapter 9: Data Conditioning
Chapter 10: Feature Engineering
Chapter 11: Feature Selection
Chapter 12: Data Preparation Cookbook
Chapter 13: Algorithms
Chapter 14: Modeling
Chapter 15: Model Evaluation and Enhancement
Chapter 16: Ensembles & Complexity
Chapter 17: Deep Learning vs. Traditional ML
Chapter 18: Explainable AI (XAI) put after Deep Learning
Chapter 19: Human in the Loop
Chapter 20: GENERAL OVERVIEW of an Application - Healthcare Delivery and Medical Informatics
Chapter 21: Specific Application: Business: Customer Response
Chapter 22: Specific Application: Education: Learning Analytics
Chapter 23: Specific Application: Medical Informatics: Colon Cancer Screening
Chapter 24: Specific Application: Financial: Credit Risk
Chapter 25: Specific FUTURE Application: The ‘INTELLIGENCE AGE (Revolution)’: LLMs like ChatGPT - Tiny ML - H.U.M.A.N.E. - Etc.
Chapter 26: Right Model for the Right Use
Chapter 27: Ethics in Data Science
Chapter 28: Significance of Luck
Chapter Tutorial A: Example of Data Mining Recipes Using Statistica Data Miner 13
Chapter Tutorial B: Analysis of Hurricane Data (Hurrdata.sta) Using the Statistica Data Miner 13
Chapter Tutorial C: Predicting Student Success at High-Stakes Nursing Examinations (NCLEX) Using SPSS Modeler and Statistica Data Miner 13
Chapter Tutorial D: Constructing a Histogram Using MidWest Company Personality Data Using KNIME
Chapter Tutorial E: Feature Selection Using KNIME
Chapter Tutorial F: Medical/Business Tutorial Using Statistica Data Miner 13
Chapter 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.
Chapter Tutorial H: Data Prep 1-1: Merging Data Sources Using KNIME
Chapter Tutorial I: Data Prep 1–2: Data Description Using KNIME
Chapter Tutorial J: Data Prep 2-1: Data Cleaning and Recoding Using KNIME
Chapter Tutorial K: Data Prep 2-2: Dummy Coding Category Variables Using KNIME
Chapter Tutorial L: Data Prep 2-3: Outlier Handling Using KNIME
Chapter Tutorial M: Data Prep 3-1: Filling Missing Values With Constants Using KNIME
Chapter Tutorial N: Data Prep 3-2: Filling Missing Values With Formulas Using KNIME
Chapter Tutorial O: Data Prep 3-3: Filling Missing Values With a Model Using KNIME
Chapter Appendix-A: Listing of TUTORIALS and other RESOUCES on this book’s COMPANION WEB PAGE
Chapter Appendix B: Instructions on HOW TO USE this book’s COMPANION WEB PAGE
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