DATA WAREHOUSE AND DATA MINING (4 Credits)

Learning Outcomes:

On successful completion of this course, students will be able to: Define the basic concepts, architecture and techniques of data warehouse and data mining; Explain collection of data and techniques for pre-processing the data before using in data warehouse and data mining; Design data warehouse and data mining model; Analyze the implementation of data warehouse and data mining techniques which appropriate to the need.

Topics:

1. The Data Warehouse Environment
2. The Data Warehouse and Design
3. The Data Warehouse and Technology
4. The Distributed Data Warehouse
5. External Data and the Data Warehouse
6. Unstructured Data and the Data Warehouse
7. Data Warehouse Design Review Checklist
8. Introduction/Overview of Data Mining
9. Getting to Know Your Data; Data Pre-processing
10. Classification: Basic Concepts – Decision Tree Induction
11. Classification: Basic Concepts – Rule-Based Classification
12. Classification: Basic Concepts – Bayes Classification Methods
13. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
14. Cluster Analysis: Basic Concepts and Methods
15. Data Mining Trends and Research Frontiers