Business Intelligence
- Credits: 6
- Ending: Examination
- Range: 2P + 2C
- Semester: winter
- Year: 1
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
In particular, students acquire the following abilities:
A. knowing how to create multidimensional data models and different approaches for developing data warehouses,
B. managing the creation of data warehouses in the MySQL database and modelling in SqlDBM,
C. be capable of creating and managing ETL processes at the conceptual, logical and physical levels,
D. developing data hypercubes and applying MDX queries,
E. knowing how to apply reporting and visualisation methods (queries, charts, dashboards),
F. optimizing data warehouse (materialized views, bitmap and bitmap-join indexes, partitions)
G. understanding the basic concepts of data mining for business intelligence,
H. working with the software and platforms approved by university;
I. managing team cooperation in the development of a business intelligence solutions.
Indicative content
1. Business intelligence concept and the disposition level of data, comparison with the transactional level.
2. Multidimensional data models, data warehouses and data marts (Inmonn and Kimball approaches).
3. Managing slowly and fastly changing dimensions and managing hierarchies of dimensions.
4. ROLAP, MOLAP and HOLAP.
5. Conceptual model of data warehouse and MultiDim.
6. ETL / ELT processes.
7. External and internal data sources and data quality indicators.
8. Data governance a master data management.
9. Business intelligence architectures.
10. Querying data warehouses SQL a MDX queries.
11. Reporting a visualization (dashboard, graphical outputs, critical indicators of performance).
12. Data warehouse optimization.
13. Life cycles of business intelligence solutions, project team, managing project team and pre-project analyses.
Support literature
NĚMEC R. (2014). Principy projektování a implementace systémů business intelligence. VŠB-TU Ostrava, Ostrava.
VAISMAN A., ZIMANYI E. (2014). Data Warehouse Systems - Design and Implementation. Springer-Verlag, Berlin Heidelberg.
KIMBALL R. (2002). The Data Warehouse Toolkit, John Wiley & Sons.
HUMPHRIES M., HAWKINS M., DY M.. (2002) Data warehousing Principy a praxe, Computer Press.
GROSSMANN W., RINDERLE-MA S. (2015). Fundamentals of Business Intelligence. Springer-Verlag Berlin Heidelberg.
BRAMER M. (2020). Principles of Data Mining. Springer-Verlag London.
JENSEN C.S., PEDERSEN T.B., THOMSEN C. (2010). Multidimensional Databases and Data Warehousing. Morgan & Claypool.
Requirements to complete the course
Exam 60% The exam consists of two parts: the evaluation of the theoretical knowledge and knowledge of modelling of a specific example. The first part, verifies the achievement level of the teaching results A. C. E. G., whereas the second part verifies the level of the teaching results D, F.
Assignments during the semester 40% The project should be designed and defended. The evaluation of the students involves project and answers to the supplementary questions. The project evaluation and subsequent short test shall assess the following teaching results: A. B., C. D. H. I.
Student workload
Student workload (in hours):
6 credits x 26 hours = 156 hours
Distribution of study load:
Attendance at seminars: 26 hours
Preparation for seminars: 26 hours
Preparation for project and test: 52 hours
Preparation for the exam: 26 hours
Language whose command is required to complete the course
slovak
Date of approval: 10.02.2023
Date of the latest change: 18.05.2022