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

1. Basic Algorithms for Data Mining: A Brief Overview

2. Advanced Algorithms for Data Mining

3. Classification

4. Numerical Prediction

5. Model Evaluation and Enhancement

6. Predictive Analytics for Population Health and Care

7. Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors

8. Customer Response Modeling

9. 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

1. The Apparent Paradox of Complexity in Ensemble Modeling

2. The Right Model for the Right Purpose: When Less Is Good Enough

3. A Data Preparation Cookbook

4. Deep Learning

5. Significance versus Luck in the Age of Mining: The Issues of P-Value Significance and Ways to Test Significance of Our Predictive Analytic Models

6. Ethics and Data Analytics

7. IBM Watson