Universität Wien
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040077 KU Advanced Business Analytics (MA) (2024S)

6.00 ECTS (3.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work

The course language is English.

Only students who signed up for the class in univis/u:space are allowed to take the class (that means, that you have to at least be on the waiting list if you want to take this class). No exceptions possible.
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. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

The course language is English.
The first appointment will be on March 5th.

  • Tuesday 05.03. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 06.03. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 13.03. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 19.03. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 09.04. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 10.04. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 16.04. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 17.04. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 23.04. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 30.04. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Thursday 02.05. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 07.05. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 08.05. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 14.05. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 15.05. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 21.05. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 22.05. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 28.05. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 29.05. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 04.06. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 05.06. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 11.06. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 12.06. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 18.06. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 19.06. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 25.06. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 26.06. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Thursday 27.06. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00

Information

Aims, contents and method of the course

In this course, fundamentals of business analytics will be covered. The students will be able to derive relevant business knowledge through methods of advanced business analytics from large, complex databases.
They will be able to identify the underlying analytics tasks of a business problem, to select and apply appropriate data mining algorithms, and to derive plans of actions from their outputs to solve the business problems. The students will have an overview of relevant analytics methods, including a selection of particular methods such as explorative data analysis, descriptive and predictive modelling (e.g. cluster analysis, association analysis, classification).

Assessment and permitted materials

Midterm test (35%): Thu, May 2, 15:00-16:00 [changed on 01-03-24]
Final test (35%): Thu, Jun 27, 15:00-16:00 [changed on 01-03-24]
Homework (30%):
-- Submission 1: Wed, Apr 24
-- Submission 2: Wed, Jun 19

1) Fulfilling all partial achievements is a prerequisite for positive assessment, if nothing else has been explicitly stated.
2) The use of AI tools (e.g. ChatGPT) for the production of texts is only allowed, if this is expressly requested by the course instructor (e.g. for specific assignments).
3) To ensure good scientific practice, the course instructor may request a "grade-relevant talk" (plausibility check) regarding the submitted written work. This interview has to be completed successfully.

For more details, see here: https://ufind.univie.ac.at/en/vvz_sub.html?from=1&to=2&path=S8504&semester=2024S

Minimum requirements and assessment criteria

In total, 100 points can be achieved. Grades are assigned as follows:
[88,100]: 1
[76,88[ : 2
[63,76[ : 3
[50,63[ : 4
< 50 : 5

Examination topics

Midterm test/Final test: Slides and topics covered in the lectures and exercises.
Homework: topics covered in the exercises.

Reading list

Provost, Foster; Fawcett, Tom (2013): Data Science for Business. What you need to know about data mining and data-analytic thinking. Köln: O`Reilly.
Berthold, Michael R.; Borgelt, Christian; Höppner, Frank; Klawonn, Frank; Silipo, Rosaria (2020): Guide to Intelligent Data Science. Cham: Springer International Publishing.
Sutton, Richard S.; Barto, Andrew G. (2018): Reinforcement learning. An introduction / Richard S. Sutton and Andrew G. Barto. Second edition. Cambridge, Massachusetts: The MIT Press (Adaptive computation and machine learning).
Witten, I. H. (2017): Data mining. Practical machine learning tools and techniques. Fourth edition. Amsterdam: Elsevier.
Tan, Pang-Ning; Steinbach, Michael; Kumar, Vipin; Karpatne, Anuj (2019): Introduction to Data Mining, Global Edition. 2nd ed. Harlow, United Kingdom: Pearson Education Limited.

Association in the course directory

Last modified: We 31.07.2024 11:25