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040242 VO Econometrics and Statistics (MA) (2019W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
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 serve).
Register/Deregister for this course

Details

Language: German

Examination dates

Friday 31.01.2020 16:45 - 18:15 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß Monday 24.02.2020 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock Wednesday 06.05.2020 16:45 - 18:15 Digital

Lecturers

Classes (iCal) - next class is marked with N

Tuesday 01.10. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 08.10. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 15.10. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 22.10. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 29.10. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 06.11. 16:45 - 18:15 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Tuesday 12.11. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 19.11. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 26.11. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 03.12. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 10.12. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 17.12. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 07.01. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 14.01. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 21.01. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 28.01. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock

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.

. Sampling
. Supervised und unsupervised learning
. 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

Minimum requirements and assessment criteria

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

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: Mo 05.10.2020 10:08