Application programming: Data mining and data warehousing
  
Code:
INEA00111
 Supervisor:Henryk Maciejewski, PhD
   

 

 

Workload
(Hours / sem. (h))
LectureTutorialsLaboratoryProjectSeminar
3003000

 

 

Outcome:

Knowledge of methods, algorithms and application areas of data mining. Knowledge of techniques for data warehousing and OLAP. Hands-on experience using commercial software for data mining and data warehousing (preferably SAS and Microsoft solutions).

 

Content:

Data mining methods for: association rules mining, predictive modeling, clustering, time series analysis; methods for feature selection and dimensionality reduction. Techniques used for data warehouse and OLAP systems: ETL, multidimensional modeling of analytical data, database
technologies used for OLAP: relational OLAP (star, snowflake schemes), multidimensional OLAP (MDDB cubes), hybrid OLAP. MDX – query language for multidimensional data.

 

Literature:

1. J. Han, M. Kamber, Data Mining: Concepts and Techniques, Second Edition, Elsevier

2. T. Hastie, R. Tibshirani, J.H. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer

3. D. Larose, Data Mining Methods and Models
4. D. Hand, H. Mannila, P. Smyth, Principles of Data Mining
5. W. Inmon, Building the data warehouse
6. F. Silvers, Building and maintaining a data warehouse