
Data Science
Data Warehouses and Business Intelligence
- Class 30
- Practice 30
- Independent work 120
Course title
Data Warehouses and Business Intelligence
Lecture type
Obligatory
Course code
20-02-032
Semester
1
ECTS
6
Lecturers and associates
Course objectives
Introducing students to metodologies and technologies used for implementation of analystical information systems based on data warehouses.
Content
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.
Required reading
Course handbook prepared and printed by Algebra University College
Additional reading
Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, Bob Becker: The Data Warehouse Lifecycle Toolkit 2nd Edition, Wiley, 2008
Robert Laberge: The Data Warehouse Mentor: Practical Data Warehouse and Business Intelligence Insights, McGraw-Hill Education; 2011
- Study program duration
- 4 semesters (2 years)
- Semester duration
- 15 weeks of active teaching + 5 examination weeks
- Total number of ECTS points
- 120
- Certifications obtained during studies
-
IT SMF – ITIL Foundation
- Title
- struč.spec.ing.comp. (Professional Master of Computer Engineering with sub-specialization in Data Science)
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.
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