Universität Wien

040077 KU Advanced Business Analytics (MA) (2022S)

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. All appointments will be streamed through Moodle/Zoom. You will need a camera and microphone to participate online.

First meeting: Tuesday, March 1st, 11:30-13:00, SR1, Kolingasse 14-16

  • Tuesday 01.03. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 02.03. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 08.03. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 09.03. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 15.03. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 16.03. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 22.03. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 23.03. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 29.03. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 30.03. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 05.04. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 06.04. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 26.04. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 27.04. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 03.05. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 04.05. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 10.05. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 11.05. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 17.05. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 18.05. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 24.05. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 25.05. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 31.05. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 01.06. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 08.06. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 14.06. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 15.06. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 21.06. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 22.06. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 28.06. 11:30 - 13:00 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 29.06. 09:45 - 11:15 Hybride Lehre
    Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    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%): April 05, 11:30
Final test (35%): **June 29, 09:45** (corrected 04/03/2022)
Homework (30%):
-- Submission 1: April 27
-- Submission 2: June 8

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: Th 11.05.2023 11:27