People Innovation Excellence


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.


  1. The Data Warehouse Environment;
  2. Design data warehouse with Kimball approach;
  3. The Data Warehouse and Technology, Now and Future;
  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 and The example used;
  9. Getting to Know Your Data; Data Pre-processing and Prepare your Data before Mining;
  10. Classification: Basic Concepts – Decision Tree Induction and How it can be used in the Real World;
  11. Classification: Basic Concepts – Rule-Based Classification;
  12. Classification: Basic Concepts – Bayes Classification Methods How it can be used in the real world;
  13. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods and How it can be used in the real world;
  14. Cluster Analysis: Basic Concepts and Methods and how it can be used in the real world;
  15. Data Mining Trends and Research Frontiers.

Published at : Updated

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