Universität Wien

040101 KU Advanced Business Analytics (MA) (2021W)

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

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

This class will be offered in hybrid form. As long as allowed, we will offer one on-site appointment per week (see schedule). To attend, proof of 3G (vaccinated/tested/recovered) and registration will be necessary. All appointments (online and on-site) will be streamed through Moodle/BigBlueButton.

  • Tuesday 05.10. 11:30 - 13:00 Digital
  • Wednesday 06.10. 09:45 - 11:15 Digital
  • Tuesday 12.10. 11:30 - 13:00 Digital
    Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 13.10. 09:45 - 11:15 Digital
  • Tuesday 19.10. 11:30 - 13:00 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 20.10. 09:45 - 11:15 Digital
  • Wednesday 27.10. 09:45 - 11:15 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 03.11. 09:45 - 11:15 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 09.11. 11:30 - 13:00 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 10.11. 09:45 - 11:15 Digital
  • Tuesday 16.11. 11:30 - 13:00 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 17.11. 09:45 - 11:15 Digital
  • Tuesday 23.11. 11:30 - 13:00 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 24.11. 09:45 - 11:15 Digital
  • Tuesday 30.11. 11:30 - 13:00 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 01.12. 09:45 - 11:15 Digital
  • Tuesday 07.12. 11:30 - 13:00 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 14.12. 11:30 - 13:00 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 15.12. 09:45 - 11:15 Digital
  • Tuesday 11.01. 11:30 - 13:00 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 12.01. 09:45 - 11:15 Digital
  • Tuesday 18.01. 11:30 - 13:00 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 19.01. 09:45 - 11:15 Digital
  • Tuesday 25.01. 11:30 - 13:00 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 26.01. 09:45 - 11:15 Digital

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 modeling (e.g. cluster analysis, association analysis, classification).

Assessment and permitted materials

Midterm test (40%): **NEW: Nov 23, 11:30**
Final test (40%): Jan 26, 09:45
Homework (20%):
-- Submission 1: **NEW: Dec 1, 12:00**
-- Submission 2: Jan 19, 12:00

Minimum requirements and assessment criteria

Midterm test and one more examination (homework / final test) must be passed individually. In total, 100 points can be achieved. Grades are assigned as follows:
1 (very good) • 100-90 points
2 (good) • 89-76 points
3 (satisfactory) • 75-63 points
4 (sufficient) • 62-50 points
5 (not enough) • 49-0 points

Examination topics

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

Reading list

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: Fr 12.05.2023 00:12