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Lecturer(s)
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Borkovcová Monika, Ing. Ph.D.
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Majerík Filip, Ing. Ph.D.
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Course content
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1. Introduction to data warehouses (DWH) - introduction to the basic concepts of DWH. 2. Introduction to OLTP vs OLAP, DataMining, ETL, ELT, Business Intelligence. 3. Data warehouse architectures - multidimensional databases 4. Data warehouse models (star, snowflake), reporting, data warehouse layers and their responsibilities. 5. DWH development methodology - business requirements, methods of building and operating data warehouses, source data/systems analysis, design. 6. Data preparation, extraction, transformation, deployment (ETL, ELT) 7. OLAP analysis 8. SQL analysis options - CUBE and ROLLUP clauses 9. Integration tools I - ODI, topology design and creation. 10. Integration tools II, reporting - reporting tools, ODV architecture, basic principles 11. Visualization tools and dashboard creation options 12. Business Intelligence in practice 13. Data Mining in practice
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Work with text (with textbook, with book), Skills training
- Contact teaching
- 39 hours per semester
- Home preparation for classes
- 20 hours per semester
- Preparation for an exam
- 13 hours per semester
- Term paper
- 48 hours per semester
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Learning outcomes
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The aim of the subject is to extend knowledge of data warehousing and business intelligence. Students will get acquainted with the basic concepts and procedures in creation and operation of data warehouses and on practical examples they will try to prepare data, create data warehouse and work with data warehouse.
The graduate will acquire information about database warehouses and creating and managing data warehouses. They will also learn about BI and become familiar with the entire process of working with data from source systems to database systems used for analytics.
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Prerequisites
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They are expected to have advanced knowledge of SQL and PL / SQL.
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Assessment methods and criteria
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Oral examination, Home assignment evaluation, Self project defence
The conditions of successful completion of the subject are fulfillment of qualified requirements (practical solution of assigned complex task with analysis of proposed solution and verification of theoretical knowledge).
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Recommended literature
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David Taniar, Wenny Rahayu. Data warehousing and analytics: fueling the data engine. ISBN 9783030819781.
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David Taniar, Wenny Rahayu. Data Warehousing and Analytics. Springer International Publishing, 2022. ISBN 9783030819798.
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James Serra. Deciphering data architectures. ISBN 9781098150761.
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Ralph Kimball, Margy Ross. The data warehouse toolkit: the definiteve guide to dimensional modeling. ISBN 9781118530801.
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