Handbook of Statistical Analysis and Data Mining Applications

Part 1: History Of Phases Of Data Analysis, Basic Theory, And The Data Mining Process
1. The Background for Data Mining Practice
2. Theoretical Considerations for Data Mining
3. The Data Mining and Predictive Analytic Process
4. Data Understanding and Preparation
5. Feature Selection
6. Accessory Tools for Doing Data Mining

Part 2: The Algorithms And Methods In Data Mining And Predictive Analytics And Some Domain Areas
7. Basic Algorithms for Data Mining: A Brief Overview
8. Advanced Algorithms for Data Mining
9. Classification
10. Numerical Prediction
11. Model Evaluation and Enhancement
12. Predictive Analytics for Population Health and Care
13. Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors
14. Customer Response Modeling
15. Fraud Detection

Part 3: Tutorials And Case Studies
Tutorial A Example of Data Mining Recipes Using Windows 10 and Statistica 13
Tutorial B Using the Statistica Data Mining Workspace Method for Analysis of Hurricane Data (Hurrdata.sta)
Tutorial C Case Study—Using SPSS Modeler and STATISTICA to Predict Student Success at High-Stakes Nursing Examinations (NCLEX)
Tutorial D Constructing a Histogram in KNIME Using MidWest Company Personality Data
Tutorial E Feature Selection in KNIME
Tutorial F Medical/Business Tutorial
Tutorial G A KNIME Exercise, Using Alzheimer’s Training Data of Tutorial F
Tutorial H Data Prep 1-1: Merging Data Sources
Tutorial I Data Prep 1–2: Data Description
Tutorial J Data Prep 2-1: Data Cleaning and Recoding
Tutorial K Data Prep 2-2: Dummy Coding Category Variables
Tutorial L Data Prep 2-3: Outlier Handling
Tutorial M Data Prep 3-1: Filling Missing Values With Constants
Tutorial N Data Prep 3-2: Filling Missing Values With Formulas
Tutorial O Data Prep 3-3: Filling Missing Values With a Model
Tutorial P City of Chicago Crime Map: A Case Study Predicting Certain Kinds of Crime Using Statistica Data Miner and Text Miner
Tutorial Q Using Customer Churn Data to Develop and Select a Best Predictive Model for Client Defection Using STATISTICA Data Miner 13 64-bit for Windows 10
Tutorial R Example With C&RT to Predict and Display Possible Structural Relationships
Tutorial S Clinical Psychology: Making Decisions About Best Therapy for a Client

Part 4: Model Ensembles, Model Complexity; Using the Right Model for the Right Use, Significance, Ethics, and the Future, and Advanced Processes
16. The Apparent Paradox of Complexity in Ensemble Modeling
17. The "Right Model" for the "Right Purpose": When Less Is Good Enough
18. A Data Preparation Cookbook
19. Deep Learning
20. Significance versus Luck in the Age of Mining: The Issues of P-Value "Significance" and "Ways to Test Significance of Our Predictive Analytic Models"
21. Ethics and Data Analytics
22. IBM Watson