Universität Wien
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040242 VO Data Analytics (MA) (2024W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Moodle
We 22.01. 15:00-16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß

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Details

Language: German

Examination dates

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 02.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 09.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 16.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 23.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 30.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 06.11. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 20.11. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 27.11. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 04.12. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 11.12. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 08.01. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 15.01. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß

Information

Aims, contents and method of the course

Datamining and big data based on case studies

During the course we will learn and discuss concepts of data mining and big data using case studies.

The case studies will cover areas such as

. Customer Relationship Management
. Fraud Detection
. Revenue Management
. Market Research

The presented concepts of data-naming and big data will include i.a.

. Multiple Regression,
. Logistic Regression
. Statistical Analysis of Frequency Data
. Analysis of variance
. Time series analysis

Assessment and permitted materials

Final test at the end of the course, Written Exam

The test is carried out as a face-to-face test personally at the University campus

Minimum requirements and assessment criteria

To pass this course you have to attain min 60% of the total points during the written exam.

Examination topics

1) Analyze a given Problem and sketch a solution with Datamining methods
2) Describe the steps of a standard analysis
3) Understand (= be able to read and Interpret) statistical model equations
and statistical Reports

More Details about the exam will be given during the course.
Analyze a given Problem and sketch a solution with Datamining methods

Understand (= be able to read and Interpret) statistical model equations
and Datamining concepts

More Details about the exam will be given during the course.

Reading list

Werner Brannath, Andreas Futschik, Statistik für Wirtschaftswissenschaftler

Weitere Literatur wird während der Vorlesung bekannt gegeben.

Folien, die im Kurs diskutiert werden (werden auf der Homepage veröffentlicht).

Association in the course directory

Last modified: Th 09.01.2025 10:45