040242 VO Econometrics and Statistics (MA) (2022W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften

ON-SITE

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

Language: German

Examination dates

Wednesday 25.01.2023 18:30 - 20:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock Tuesday 28.02.2023 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock Wednesday 26.04.2023 18:30 - 20:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock

Lecturers

Bertram Wassermann

Classes (iCal) - next class is marked with N

19.10. presumably Offline

    
    Wednesday
    05.10.
    18:30 - 20:00
    Hörsaal 14  Oskar-Morgenstern-Platz 1  2.Stock
    
    Wednesday
    12.10.
    18:30 - 20:00
    Hörsaal 14  Oskar-Morgenstern-Platz 1  2.Stock
    
    Wednesday
    19.10.
    18:30 - 20:00
    Hörsaal 14  Oskar-Morgenstern-Platz 1  2.Stock
    
    Wednesday
    09.11.
    18:30 - 20:00
    Hörsaal 14  Oskar-Morgenstern-Platz 1  2.Stock
    
    Wednesday
    16.11.
    18:30 - 20:00
    Hörsaal 14  Oskar-Morgenstern-Platz 1  2.Stock
    
    Wednesday
    23.11.
    18:30 - 20:00
    Hörsaal 14  Oskar-Morgenstern-Platz 1  2.Stock
    
    Wednesday
    30.11.
    18:30 - 20:00
    Hörsaal 14  Oskar-Morgenstern-Platz 1  2.Stock
    
    Wednesday
    07.12.
    18:30 - 20:00
    Hörsaal 14  Oskar-Morgenstern-Platz 1  2.Stock
    
    Wednesday
    14.12.
    18:30 - 20:00
    Hörsaal 14  Oskar-Morgenstern-Platz 1  2.Stock
    
    Wednesday
    11.01.
    18:30 - 20:00
    Hörsaal 14  Oskar-Morgenstern-Platz 1  2.Stock
    
    Wednesday
    18.01.
    18:30 - 20:00
    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

If possible, 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.

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 19.01.2023 14:28