Data Warehousing and Data Mining (4-0-0)

Course Details:

Data Warehousing

  1. Introduction to Data Warehousing – Batch, OLTP, DSS Applications. Different natures of OLTP and DW databases. Commercial Importance of DW. Data Marts
  2. Basic Elements of DataWarehouse – Source System, Data Staging Area, Presentation Server
  3. Business Dimensional Life Cycle
  4. Dimensional Modeling. Multidimensional Data Model, Data Cubes, OLAP
  5. DW Bus Architecture, Conformed Dimensions
  6. Star Schema and Snowflake Schema
  7. Normalization VS Dimensional Modeling
  8. Slicing and Dicing, Drilling, Drill-up, Drill-down, Drill-within, Drill-across.
  9. Bitmap Index
  10. Aggregation
  11. Metadata
  12. Design Issues, Partitioning, Size Estimation
  13. Example Applications: Retail, CRM, Telecom, E-Commerce, Insurance

Data Mining

  1. KDD and Data Mining
  2. SQL and Data Mining
  3. Association Rules
  4. Clustering
  5. Decision Trees
  6. Neural Networks
  7. Temporal and Spatial Data Mining
  8. Sequence Mining
  9. Text Mining
  10. Web Mining


Suggested Text Books
a. J. Hahn and Micheline Kamber - Data Mining: Concepts and Techniques (Morgan Kaufmann)
b. R.Kimball - DataWarehouse Toolkit (J.Wiley)
c. A.K.Pujari - Data mining (University Press)
All the above books have Indian editions.