Universität Wien
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052411 VU Business Intelligence 1 (2026S)

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

Diese Lehrveranstaltung wird im SS 2023 voraussichtlich nicht stattfinden können.
Moodle

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: English

Lecturers

Classes (iCal) - next class is marked with N

  • Friday 06.03. 09:45 - 13:00 Hörsaal II NIG Erdgeschoß
  • Friday 13.03. 09:45 - 13:00 Hörsaal II NIG Erdgeschoß
  • Friday 20.03. 09:45 - 13:00 Hörsaal II NIG Erdgeschoß
  • Friday 27.03. 09:45 - 13:00 Hörsaal II NIG Erdgeschoß
  • Friday 17.04. 09:45 - 13:00 Hörsaal II NIG Erdgeschoß
  • Friday 24.04. 09:45 - 13:00 Hörsaal II NIG Erdgeschoß
  • Friday 08.05. 09:45 - 13:00 Hörsaal II NIG Erdgeschoß
  • Friday 15.05. 09:45 - 14:45 Seminarraum 11, Währinger Straße 29 2.OG
  • Friday 22.05. 09:45 - 13:00 Hörsaal II NIG Erdgeschoß
  • Friday 29.05. 09:45 - 13:00 Hörsaal II NIG Erdgeschoß
  • Friday 05.06. 09:45 - 13:00 Hörsaal II NIG Erdgeschoß
  • Friday 12.06. 09:45 - 13:00 Hörsaal II NIG Erdgeschoß
  • Friday 19.06. 09:45 - 13:00 Hörsaal II NIG Erdgeschoß
  • Friday 26.06. 09:45 - 13:00 Hörsaal II NIG Erdgeschoß

Information

Aims, contents and method of the course

Goals:
The goal of this course is to familiarize you with and teach you how to apply foundational concepts, modeling and analysis techniques, and tools that allow you to gain insights into the operations of organizations in a data-driven manner.

The content of the course consists of:
- Foundational concepts for business intelligence (BI)
- Architectures and modeling techniques for data preparation and integration in BI settings
- How to take a process-oriented view on organizational operations
- Data-driven analysis of organizational processes using process mining
- Using BI and process mining tools to analyze real-world data

Students, attending the course, are expected to have knowledge in the following topics:
* Basic knowledge of Python 3

Knowledge about data modeling (e.g., entity-relationship models) and process modeling (e.g., Petri nets or BPMN) is helpful but not mandatory.

Assessment and permitted materials

The grade is derived from the sum of the two parts (i.e. a maximum of 100 points in total):
* Part A: two practical assignments conducted in groups (max. 60 points)
* Part B: written exam (no aids allowed, max. 40 points).

Minimum requirements and assessment criteria

‣ Part A: 60% group assignments
‣ Part B: 40% written exam

Overall at least 51% of the points need to be achieved.

The grade is calculated from the total points as follows:
>= 87,5% very good (1)
>= 75% good (2)
>= 62,5% satisfactory (3)
>= 51% sufficient (4)
< 51% not sufficient (5)

Examination topics

* Lecture (slides)
* Exercises (theoretical and practical)

Reading list

* Lecture slides
* Rick Sherman: Business Intelligence Guidebook: From Data Integration to Analytics, Morgan Kaufmann (1st edition), 2014.
* Wil van der Aalst: Process Mining – Data Science in Action (2nd edition), Springer, 2016

Association in the course directory

Module: BI BI1 BUS

Last modified: Mo 27.04.2026 12:45