040038 VO Econometrics and Statistics (MA) (2022S)

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 served).

Details

Language: German

Examination dates

Thursday 30.06.2022 16:45 - 18:15 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß Thursday 22.09.2022 16:45 - 18:15 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß Thursday 17.11.2022 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock

Lecturers

Bertram Wassermann

Classes (iCal) - next class is marked with N

    
    Tuesday
    01.03.
    16:45 - 18:15
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.Stock
    
    Tuesday
    08.03.
    16:45 - 18:15
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.Stock
    
    Tuesday
    15.03.
    16:45 - 18:15
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.Stock
    
    Tuesday
    22.03.
    16:45 - 18:15
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.Stock
    
    Tuesday
    29.03.
    16:45 - 18:15
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.Stock
    
    Tuesday
    05.04.
    16:45 - 18:15
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.Stock
    
    Tuesday
    03.05.
    16:45 - 18:15
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.Stock
    
    Tuesday
    10.05.
    16:45 - 18:15
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.Stock
    
    Tuesday
    17.05.
    16:45 - 18:15
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.Stock
    
    Tuesday
    24.05.
    16:45 - 18:15
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.Stock
    
    Tuesday
    31.05.
    16:45 - 18:15
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.Stock
    
    Tuesday
    14.06.
    16:45 - 18:15
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.Stock
    
    Tuesday
    21.06.
    16:45 - 18:15
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.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.

. Multiple Regression,
. Logistic Regression
. Statistical Analysis of Frequency Data
. Analysis of variance
. Time series analysis
. Supervised und unsupervised learning

Assessment and permitted materials

Written Exam

Minimum requirements and assessment criteria

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

Examination topics

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

Folien und Audio
Themenbezogen Literaturhinweise während des Kurses

Association in the course directory

Last modified: Tu 10.01.2023 11:48