Universität Wien FIND

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040101 KU Advanced Business Analytics (MA) (2020W)

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.

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. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Watch the short welcome video!

This is a hybrid class: generally, lectures will be streamed and recorded. This means that you can participate in the classroom (with distance) or online and/or watch the recorded lecture at a later time.

Tuesday 06.10. 11:30 - 13:00 Digital
Wednesday 07.10. 09:45 - 11:15 Digital
Tuesday 13.10. 11:30 - 13:00 Digital
Wednesday 14.10. 09:45 - 11:15 Digital
Tuesday 20.10. 11:30 - 13:00 Digital
Wednesday 21.10. 09:45 - 11:15 Digital
Tuesday 27.10. 11:30 - 13:00 Digital
Wednesday 28.10. 09:45 - 11:15 Digital
Tuesday 03.11. 11:30 - 13:00 Digital
Wednesday 04.11. 09:45 - 11:15 Digital
Tuesday 10.11. 11:30 - 13:00 Digital
Wednesday 11.11. 09:45 - 11:15 Digital
Tuesday 17.11. 11:30 - 13:00 Digital
Wednesday 18.11. 09:45 - 11:15 Digital
Tuesday 24.11. 11:30 - 13:00 Digital
Wednesday 25.11. 09:45 - 11:15 Digital
Tuesday 01.12. 11:30 - 13:00 Digital
Wednesday 02.12. 09:45 - 11:15 Digital
Wednesday 09.12. 09:45 - 11:15 Digital
Tuesday 15.12. 11:30 - 13:00 Digital
Wednesday 16.12. 09:45 - 11:15 Digital
Tuesday 12.01. 11:30 - 13:00 Digital
Wednesday 13.01. 09:45 - 11:15 Digital
Tuesday 19.01. 11:30 - 13:00 Digital
Wednesday 20.01. 09:45 - 11:15 Digital
Tuesday 26.01. 11:30 - 13:00 Digital
Wednesday 27.01. 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 modelling (e.g. cluster analysis, association analysis, classification).

Assessment and permitted materials

Midterm test (40%): Nov 10, 11:30
Final test (40%): Jan 27, 09:45
Homework (20%):
-- Submission 1: Mon, Nov 30, 12:00
-- Submission 2: Mon, Jan 18, 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

Provost/Fawcett: Data Science for Business - What you need to know about data mining and data-analytic thinking. http://www.data-science-for-biz.com/

Berthold/Klawonn: Guide to Intelligent Data Analysis, Springer

Association in the course directory

Last modified: Th 28.01.2021 16:08