Topics to be covered in the Course: Data Warehousing and Data Mining

 

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
  14. End-user applications

 

Data Mining

 

  1. KDD and Data Mining
  2. SQL and Data Mining
  3. Association Rules
  4. Bayesian Network Approach
  5. Decision Trees
  6. Neural Networks, Genetic Algorithms, Rough Sets, SVM
  7. Temporal & Spatial Data Mining
  8. Sequence Mining
  9. Text Mining
  10. Web Mining

 

 

Suggested Text Books

 

a. R.Kimball – DataWarehouse Lifecycle Toolkit (J.Wiley)

b. R.Kimball – DataWarehouse Toolkit (J.Wiley)

c. Anahory and Murray – Data Warehousing in the Real World (Pearson Education)

d. A.K.Pujari – Data mining (University Press)

e. J. Hahn and Micheline Kamber - Data Mining: Concepts and Techniques (Morgan Kaufmann)

 

All the above books have Indian editions.