Universität Wien

052412 VU Business Intelligence II (2022W)

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

Kickoff Meeting (Online): 06.10. 08:00
Link: https://bbb.cs.univie.ac.at/b/mar-mzl-tjx-k5j

  • Thursday 06.10. 08:00 - 09:30 Digital (Kickoff Class)
  • Saturday 08.10. 09:00 - 12:15 Digital
  • Friday 21.10. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Friday 28.10. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Friday 04.11. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Friday 11.11. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Friday 18.11. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Friday 25.11. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Friday 02.12. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Friday 09.12. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Friday 16.12. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Friday 13.01. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Friday 20.01. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
    Seminarraum 5, Kolingasse 14-16, EG00
    Seminarraum 6, Kolingasse 14-16, EG00
  • Friday 27.01. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG

Information

Aims, contents and method of the course

The course aim is to convey and learn advanced business intelligence techniques such as:
* process mining
* social network mining
* process analysis
* decision mining

In addition, the following techniques will be discussed:
- Article spinners for businesses
- Stock exchange predictions
- Recommender systems

All methods will be carried out in several exercises working on a variety of data from BI projects.

Students, attending the course, are expected to have knowledge in the following topics:
* Advanced knowledge in Python3.6+ and performing machine learning and data analytics tasks
* Advanced knowledge in at least one programming language of your choice (e.g. Javascript, Ruby, Python, ...) recommended
* Knowledge on process mining algorithms (alpha algorithm, genetic miner, heuristic miner)
* It is recommended but not mandatory to have participated in Business Intelligence I from SS 2022 (https://ufind.univie.ac.at/de/course.html?lv=052411&semester=2022S).

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: theoretical and practical exercises submitted (max. 55 points)
* Part B: written exam (no aids allowed, max. 45 points).

Minimum requirements and assessment criteria

‣ Part A: 55% exercises (theoretical and practical)
‣ Part B: 45% written exam

Overall at least 50% of 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)
>= 50% sufficient (4)
< 50% not sufficient (5)

Examination topics

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

Reading list

* Lecture slides are available in Moodle
* W. Grossmann, S. Rinderle-Ma: Fundamentals of Business Intelligence. Springer-Verlag Berlin Heidelberg (2015)
* W. van der Aalst. Process Mining, Springer (2016)
* M. Dumas, M. La Rosa, J. Mendling, H. A. Reijers: Fundamentals of Business Process Management. Springer (2018)

Association in the course directory

Module: BUS BI2 DSA BI

Last modified: Th 11.05.2023 11:27