Universität Wien FIND

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040423 UK Enterprise Resource Planning Systems 1 (2019W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work

Zur endgültigen Lehrveranstaltung-Aufnahme ist ein pünktliches Erscheinen zur Vorbesprechung/1.LV-Einheit notwendig. Unentschuldigtes Fernbleiben führt zum Verlust des Lehrveranstaltung-Platzes.

Registration/Deregistration

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

Details

max. 30 participants
Language: German

Lecturers

Classes (iCal) - next class is marked with N

Saturday 09.11. 08:00 - 16:30 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
Saturday 23.11. 08:00 - 16:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Saturday 30.11. 08:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Saturday 07.12. 08:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Saturday 14.12. 08:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Monday 16.12. 08:00 - 16:30 PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Saturday 18.01. 08:00 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Friday 24.01. 09:45 - 11:15 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Saturday 25.01. 08:00 - 09:30 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock

Information

Aims, contents and method of the course

Companies use many different IT-systems. For their effective usage an independent consideration is not sufficient.
Whether on-premises architecture or modern cloud platforms - in the context of use, typically processes in which IT-systems are used, the resulting or acquired data, as well as the IT systems themselves, must be considered.

Gaining a theoretical understanding of following concepts and their implication for business practice:
- IT-systems (kinds and usage)
- cloud computing (IaaS, PaaS, SaaS)
- integration
- data processing and data analysis
- basics of machine learning and artificial intelligence
in their interdependencies for modern companies and organisations also reflecting economic potentials and inherent risks.
In this course there will be worked on a practical machine learning example using Python notebooks.

Assessment and permitted materials

- cooperation in class
- data exercise with presentation
- final test

Minimum requirements and assessment criteria

- data execise (50%), which is made up of
-- written summary (50%)
-- presentation of results (50%)

- written examination (50%)
========================================
= 100%
+ possible bonus points (only in case of positive advance with a maximum of 5%)

The course is classified and performed as "prüfungsimmanent" - insofar there is compulsory attendance! I.e. 2 missing units (lesson units), except for practical course session(s)! Further apologies are NOT accepted! If you stay away from the course more often, you will get graded with "Nicht genügend".

grading scale:
100,00 % 88,00 % 1
87,99 % 75,00 % 2
74,99 % 63,00 % 3
62,99 % 50,00 % 4
49,99 % 0,00 % 5

Examination topics

- lecture
- discussion
- data exercise
- presentation

Reading list

- Laudon et al. (2010): Wirtschaftsinformatik - Eine Einführung, 2. aktualisierte Auflage, Pearson Studium, München.
- Schubert, P., Wölfle, R., Dettling, W., Hrsg.(2003): E-Business Integration, München, Wien: Hanser.

Lecture notes / material collection is available at the DKE Chair
(wiwi.dke.univie.ac.at)

Association in the course directory

Last modified: Mo 07.09.2020 15:19