The purpose of this module is:1.To inform students to the potential of data warehousing and business intelligence (BI) systems, including online data analysis and data mining, in a marketing environment;2.To enable students to identify the implications of different database design approaches on the effectiveness of data warehousing/BI applications;3. To produce students capable of applying data warehousing and data analysis/data mining techniques in an effective and result-oriented manner in a corporate environment.
Data Warehousing
Origins of data warehousing; what a data warehouse is; principles for data warehouse development; sources of data for DW, data integrity issues and data cleansing; data marts.
Database design for data warehousing
Type of analysis & performance issues; database design options – relational vs. multidimensional databases models; star-shaped schema; processing speeds and response times. Online analytical processing (OLAP); Multi-dimensional OLAP (MOLAP) and Multi-dimensional databases; Hybrid OLAP (HOLAP), Relational OLAP (ROLAP).
Business Intelligence and Data Mining
The nature of business intelligtence tools; extracting data from the data warehouse, performing data analyis on extracted data; searching for trends and patterns; categories of data mining – classification, association, sequencing, clustering. Market basket analysis – evaluating significant product pairing - rules and measures. Text mining, web mining, data visualization. Analysis techniques and algorithms.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 60 |
| Formal Examination | 40 |