Universität Wien

050136 VU Business Intelligence 1 (2014W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work

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)

CEWebs

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

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

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)

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 Series

Stephen Marsland: Machine Learning - An Algorithmic Perspective.CRC Press 2009

Wil 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 2010

Weitere Literatur wird in der Lehrveranstaltung angegeben.

Association in the course directory

Last modified: Mo 07.09.2020 15:30