- Class 30
- Practice 30
- Independent work 90
Data warehousing and business intelligence
Lecturers and associates
Introducing students to metodologies and technologies used for implementation of analystical information systems based on data warehouses.
Introducing students to metodologies and technologies used for implementation of analytical information systems based on data warehouses. Students will acquire methodological knowledge necessary for implementing data warehouses, reporting systems and multi-dimensional analysis. Also, on implementing systems for planning and budgeting, predictive analysis and big data analysis. Through the practical part they will acquire skills of using Microsoft tools for developing analytical systems.
Course handbook prepared and printed by Algebra University College
1. Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, Bob Becker: The Data Warehouse Lifecycle Toolkit 2nd Edition, Wiley, 2008
2. Robert Laberge: The Data Warehouse Mentor: Practical Data Warehouse and Business Intelligence Insights, McGraw-Hill Education; 2011
Minimal learning outcomes
- Understanding the purpose of analytical systems.
- Understanding data warehouse design elements
- Understanding the data quality concepts.
- Understanding data integration process elements.
- Understanding role and purpose of OLAP structures, reports structure and analysis process.
- Understanding new trends and the metodology of implementing analytical systems.
Preferred learning outcomes
- Specify user requests for the analytical system.
- Designing the data warehouse.
- Analyzing operational data and identifying data quality issues.
- Integrating data from different sources using the data integration platform.
- Designing OLAP structures, building reports and making analysis.
- Participating as a team member in the implementation of an analytical system.