Course Outline

1. Introduction
   • Data Mining Terminology
   • What is Data Mining?
   • Data Mining Opportunities
   • Data Mining Drives Analytic Applications
   • MS Analytics Lab
     
     
2. Individual Techniques
   • Data Mining Scenarios
   • Application of Mining Techniques
   • Clustering
   • Associations
   • Classification
   • Prediction
   • Multi-model Approach
   • Teradata Warehouse Miner Lab
     
     
3. Common Mining Models
   • Linear Regression
   • Logistic Regression
   • Classification: Decision Trees
   • Neural Nets
     
     
4. The Mining Process
   • The Data Mining Process
   • A Process View to Mining
   • The Analytical Part of Data Mining
   • Advanced Application Tools
   • IBM OLAP Miner Lab
     
     
5. Project Considerations
   • Effort Distribution
   • The Data Mining Team
   • Meta Data Issues
   • Other Project Considerations
   • SAS Enterprise Miner Lab
     
     
6. BI Organization and Data Mining
   • Data Mining & Data Warehousing
   • Why Use Detailed Data?
   • Limitations of Sampling
   • Integrated Analytics
   • Mining and the BI Organization

     Course Description>