Business Intelligence
| Type of study | Follow-up Master |
|---|---|
| Language of instruction | Czech |
| Code | 157-0377/01 |
| Abbreviation | BI |
| Course title | Business Intelligence |
| Credits | 5 |
| Coordinating department | Department of Systems Engineering and Informatics |
| Course coordinator | doc. Dr. Ing. Miroslav Hudec |
Osnova předmětu
1. Introduction to Business Intelligence principles. The essence of managing and supporting Business Intelligence systems by IT based on the principles of Inmon and Kimball.
2. Fundamental principles of dimensional modeling and methods for the data warehouses in Business Intelligence systems (conceptual, logical, and physical models).
3. Relational schemas (star, snowflake, and constellation), modeling changing dimensions, and dimension hierarchies.
4. Business Intelligence architecture – core components and architectural layers of a data warehouse; key aspects of data collection, storage, and processing; and types of data warehouses.
5. Principles of data pipeline (data pump) design and implementation; the role and use of metadata in ETL and ELT processes, key aspects of data quality management.
6. Main types and application aspects of outputs of BI system, principles of OLAP techniques.
7. Principles of SQL (relational model and relational algebra) and MDX (data cube and set theory) for querying data warehouses; visualization of query results and reporting.
8. Basic principles of data mining and its relationship to Business Intelligence.
9. The essence of Competitive Intelligence and working with Big Data sources; the basis of unstructured data analysis (including its connection to Competitive Intelligence); and the relationship to OLAP principles and management reporting.
10. Strategic and tactical aspects of planning and implementing a Business Intelligence system; the system lifecycle; the role of cloud computing and agile methodologies.
2. Fundamental principles of dimensional modeling and methods for the data warehouses in Business Intelligence systems (conceptual, logical, and physical models).
3. Relational schemas (star, snowflake, and constellation), modeling changing dimensions, and dimension hierarchies.
4. Business Intelligence architecture – core components and architectural layers of a data warehouse; key aspects of data collection, storage, and processing; and types of data warehouses.
5. Principles of data pipeline (data pump) design and implementation; the role and use of metadata in ETL and ELT processes, key aspects of data quality management.
6. Main types and application aspects of outputs of BI system, principles of OLAP techniques.
7. Principles of SQL (relational model and relational algebra) and MDX (data cube and set theory) for querying data warehouses; visualization of query results and reporting.
8. Basic principles of data mining and its relationship to Business Intelligence.
9. The essence of Competitive Intelligence and working with Big Data sources; the basis of unstructured data analysis (including its connection to Competitive Intelligence); and the relationship to OLAP principles and management reporting.
10. Strategic and tactical aspects of planning and implementing a Business Intelligence system; the system lifecycle; the role of cloud computing and agile methodologies.
E-learning
Students have all relevant presentations from lectures and instructions in LMS Moodle
Povinná literatura
VAISMAN Alejandro a ZIMÁNYI Esteban. Data Warehouse Systems. Berlin Heidelberg: Springer, 2022. ISBN 978-3-662-65166-7 .
SKYRIUS, Rymvidas. Business Intelligence: A Comprehensive Approach to Information Needs, Technologies and Culture. Cham: Springer, 2022. ISBN: 978-3-030-67034-4 .
KIMBALL, Ralph a ROSS, Margy. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3. vyd. New York: Wiley, 2013. ISBN 978-1-118-53080-1.
SKYRIUS, Rymvidas. Business Intelligence: A Comprehensive Approach to Information Needs, Technologies and Culture. Cham: Springer, 2022. ISBN: 978-3-030-67034-4 .
KIMBALL, Ralph a ROSS, Margy. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3. vyd. New York: Wiley, 2013. ISBN 978-1-118-53080-1.
Doporučená literatura
TANIAR, David a RAHAYU, Wwnny. Data warehousing and analytics: Fueling the data engine. Cham: Springer, 2021. ISBN 978-3-030-81979-8 .
GROSSMANN Wilfried a RINDERLE-MA Stefanie. Fundamentals of Business Intelligence. Berlin Heidelberg: Springer, 2015. ISBN 978-3-662-46530-1 .
MANCAS, Christian. Conceptual Data Modeling and Database Design. Oakville: Apple Academic Press, 2016. ASIN:B07V1QQDPT.
GROSSMANN Wilfried a RINDERLE-MA Stefanie. Fundamentals of Business Intelligence. Berlin Heidelberg: Springer, 2015. ISBN 978-3-662-46530-1 .
MANCAS, Christian. Conceptual Data Modeling and Database Design. Oakville: Apple Academic Press, 2016. ASIN:B07V1QQDPT.