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Main menu for Browse IS/STAG
Course info
KIT / NNDSK
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Course description
Department/Unit / Abbreviation
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KIT
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NNDSK
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Data Warehousing
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Form of course completion
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Examination
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Form of course completion
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Examination
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Accredited / Credits
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Yes,
4
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
1
[HRS/WEEK]
Tutorial
2
[HRS/WEEK]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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No
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Included in study average
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YES
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Language of instruction
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Czech
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Occ/max
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Automatic acceptance of credit before examination
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No
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Summer semester
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16 / -
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0 / 0
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0 / 0
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Included in study average
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YES
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Winter semester
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0 / -
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0 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Summer semester
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Semester taught
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Summer semester
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Minimum (B + C) students
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not determined
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech
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Internship duration
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0
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No. of hours of on-premise lessons |
0
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Evaluation scale |
A|B|C|D|E|F |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
No
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Fundamental theoretical course |
No
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Evaluation scale |
A|B|C|D|E|F |
Evaluation scale for credit before examination |
S|N |
Substituted course
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KIT/INDSK
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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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.
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Requirements on student
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The conditions of successful completion of the subject are that a student regularly attending the lessons in a required scale and fulfillment of qualified requirements (practical solution of assigned complex task with analysis of proposed solution and verification of theoretical knowledge).
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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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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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-
Basic:
MOHANTY, Soumendra; JAGADEESH, Madhu; SRIVATSA, Harsha. Big data imperatives: Enterprise ?Big Data?warehouse,?BI?implementations and analytics. Apress, 2013. ISBN 978-1430248729.
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Basic:
LABERGE, Robert. Datové sklady: agilní metody a business intelligence. Computer Press. Brno, 2012. ISBN 978-80-251-3729-1.
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Basic:
NĚMEC, Radek. Principy projektování a implementace systémů Business Intelligence. Ostrava, 2014. ISBN 978-80-248-3452-8.
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Basic:
KIMBALL, Ralph; ROSS, Margy. The data warehouse toolkit: The definitive guide to dimensional modeling. John Wiley & Sons, 2013. ISBN 978-1118530801.
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Recommended:
TURBAN, Efraim. Business intelligence: a managerial approach. 2nd ed.. Boston: Prentice Hall, 2011. ISBN 978-0-13-610066-9.
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Time requirements
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Full-time form of study
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Activities
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Time requirements for activity [h]
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Kontaktní výuka
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39
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Semestrální práce
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48
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Příprava na zkoušku
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13
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Domácí příprava na výuku
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20
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Total
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120
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Prerequisites - other information about course preconditions |
They are expected to have advanced knowledge of SQL and PL / SQL. |
Competences acquired |
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. |
Teaching methods |
- Monologic (reading, lecture, briefing)
- Dialogic (discussion, interview, brainstorming)
- Skills training
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Assessment methods |
- Oral examination
- Home assignment evaluation
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