Universität Wien

052412 VU Business Intelligence II (2022W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

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

  • Donnerstag 06.10. 08:00 - 09:30 Digital (Vorbesprechung)
  • Samstag 08.10. 09:00 - 12:15 Digital
  • Freitag 21.10. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Freitag 28.10. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Freitag 04.11. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Freitag 11.11. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Freitag 18.11. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Freitag 25.11. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Freitag 02.12. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Freitag 09.12. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Freitag 16.12. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Freitag 13.01. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
  • Freitag 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
  • Freitag 27.01. 11:30 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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).

Art der Leistungskontrolle und erlaubte Hilfsmittel

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).

Mindestanforderungen und Beurteilungsmaßstab

‣ 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)

Prüfungsstoff

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

Literatur

* 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)

Zuordnung im Vorlesungsverzeichnis

Module: BUS BI2 DSA BI

Letzte Änderung: Do 11.05.2023 11:27