Universität Wien
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040101 KU Advanced Business Analytics (MA) (2023W)

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

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 October 3rd.

  • Tuesday 03.10. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 04.10. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 10.10. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 11.10. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 17.10. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 18.10. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 24.10. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 25.10. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 31.10. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 07.11. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 08.11. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 14.11. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 15.11. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 21.11. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 22.11. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 28.11. 11:30 - 13:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 29.11. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 05.12. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 06.12. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 12.12. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 13.12. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 09.01. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 10.01. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 16.01. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 17.01. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 23.01. 11:30 - 13:00 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 24.01. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 30.01. 11:30 - 13:00 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 31.01. 09:45 - 11:15 PC-Seminarraum 1, Kolingasse 14-16, OG01

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%): Nov 28, 11:30-12:30 [OMP HS9!]. Only a calculator is allowed.
Final test (35%): Jan 30, 11:30-12:30 [Kolingasse 14, SR5!]. Only a calculator is allowed.
Homework (30%):
-- Submission 1: Nov 22
-- Submission 2: Jan 24

The use of AI tools (e.g. ChatGPT) for the handling of tasks is only permitted if they are expressly requested by the course leader (e.g. for individual work tasks).

Minimum requirements and assessment criteria

In total, 100 points can be achieved (for proportions of the individual parts, see above). Grades are assigned as follows:
[88,100]: 1
[76,88[ : 2
[63,76[ : 3
[50,63[ : 4
< 50 : 5

Attendance is required for the first appointment and for exams.

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: Tu 23.01.2024 09:25