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050136 VU Business Intelligence 1 (2014W)
Continuous assessment of course work
Labels
The course is structured in five sections:
Methodology and modeling techniques in business intelligence
Data models in business intelligence and data quality
Analysis of cross sectional data (data mining)
Analysis of process data (process mining)
Business intelligence tools (OLAP, Visualization, Text mining, Data Quality Management)
Methodology and modeling techniques in business intelligence
Data models in business intelligence and data quality
Analysis of cross sectional data (data mining)
Analysis of process data (process mining)
Business intelligence tools (OLAP, Visualization, Text mining, Data Quality Management)
Registration/Deregistration
- Registration is open from Mo 01.09.2014 09:00 to Su 28.09.2014 23:59
- Deregistration possible until Fr 31.10.2014 23:59
Details
max. 25 participants
Language: German
Lecturers
Classes (iCal) - next class is marked with N
Wednesday
08.10.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
15.10.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
22.10.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
29.10.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
05.11.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
12.11.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
19.11.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
26.11.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
03.12.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
10.12.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
17.12.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
07.01.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
14.01.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Wednesday
21.01.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Thursday
22.01.
08:00 - 09:30
Hörsaal 1, Währinger Straße 29 1.UG
Wednesday
28.01.
08:00 - 11:15
Hörsaal 2, Währinger Straße 29 2.OG
Information
Aims, contents and method of the course
Assessment and permitted materials
Solving practical exercises, presentation in the last part about Business Intelligence Tools, final discussion or final test about the solution of the exercises.
Minimum requirements and assessment criteria
Students learn basic methodology in data mining and process mining and how to apply this knowledge for solving practical problems
Examination topics
The course combines lectures about theory with practical exercises using software tools.
Reading list
The Top Ten Algorithms in Data Mining
Editor(s): Xindong Wu, University of Vermont, Burlington, USA; Vipin Kumar, University of Minnesota, Minneapolis, USA
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery SeriesStephen Marsland: Machine Learning - An Algorithmic Perspective.CRC Press 2009Wil M. van der Aalst: Process Mining. Springer 2011.Process-Aware Information Systems
Editors: Marlon Dumas, Wil M.P. van der Aalst, Arthur H.M. ter Hofstede
Series: Wiley-Interscience (2005)Gert H.N. Laurensen, Jesper Thorlund: Business Analytics for Managers - Taking Business Intelligence Beyond Reporting. Wiley 2010Weitere Literatur wird in der Lehrveranstaltung angegeben.
Editor(s): Xindong Wu, University of Vermont, Burlington, USA; Vipin Kumar, University of Minnesota, Minneapolis, USA
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery SeriesStephen Marsland: Machine Learning - An Algorithmic Perspective.CRC Press 2009Wil M. van der Aalst: Process Mining. Springer 2011.Process-Aware Information Systems
Editors: Marlon Dumas, Wil M.P. van der Aalst, Arthur H.M. ter Hofstede
Series: Wiley-Interscience (2005)Gert H.N. Laurensen, Jesper Thorlund: Business Analytics for Managers - Taking Business Intelligence Beyond Reporting. Wiley 2010Weitere Literatur wird in der Lehrveranstaltung angegeben.
Association in the course directory
Last modified: Mo 07.09.2020 15:30
Methodology and modeling techniques in business intelligence
Data models in business intelligence and data quality
Analysis of cross sectional data (data mining)
Analysis of process data (process mining)
Business intelligence tools (OLAP, Visualization, Text mining, Data Quality Management)