040038 VO Econometrics and Statistics (MA) (2018S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften

Für diese LV gibt es KEIN Moodle!
Achtung! Am Mi, 09.05.2018 entfällt die Vorlesung.

Details

Language: German

Examination dates

Wednesday 27.06.2018 16:45 - 18:15 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock Wednesday 26.09.2018 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock Monday 26.11.2018 16:45 - 18:15 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß

Lecturers

Bertram Wassermann

Classes (iCal) - next class is marked with N

    
    Wednesday
    07.03.
    16:45 - 18:15
    Hörsaal 9  Oskar-Morgenstern-Platz 1  1.Stock
    
    Wednesday
    14.03.
    16:45 - 18:15
    Hörsaal 9  Oskar-Morgenstern-Platz 1  1.Stock
    
    Wednesday
    21.03.
    16:45 - 18:15
    Hörsaal 9  Oskar-Morgenstern-Platz 1  1.Stock
    
    Wednesday
    11.04.
    16:45 - 18:15
    Hörsaal 9  Oskar-Morgenstern-Platz 1  1.Stock
    
    Wednesday
    18.04.
    16:45 - 18:15
    Hörsaal 9  Oskar-Morgenstern-Platz 1  1.Stock
    
    Wednesday
    25.04.
    16:45 - 18:15
    Hörsaal 9  Oskar-Morgenstern-Platz 1  1.Stock
    
    Wednesday
    02.05.
    16:45 - 18:15
    Hörsaal 9  Oskar-Morgenstern-Platz 1  1.Stock
    
    Wednesday
    16.05.
    16:45 - 18:15
    Hörsaal 9  Oskar-Morgenstern-Platz 1  1.Stock
    
    Wednesday
    23.05.
    16:45 - 18:15
    Hörsaal 9  Oskar-Morgenstern-Platz 1  1.Stock
    
    Wednesday
    30.05.
    16:45 - 18:15
    Hörsaal 9  Oskar-Morgenstern-Platz 1  1.Stock
    
    Wednesday
    06.06.
    16:45 - 18:15
    Hörsaal 9  Oskar-Morgenstern-Platz 1  1.Stock
    
    Wednesday
    13.06.
    16:45 - 18:15
    Hörsaal 9  Oskar-Morgenstern-Platz 1  1.Stock
    
    Wednesday
    20.06.
    16:45 - 18:15
    Hörsaal 9  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.

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

Assessment and permitted materials

Written Exam

Minimum requirements and assessment criteria

To pass this course you have to attain min 50% 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

Luis Torgo / "Data Mining with R Learning with Case Studies"
Course slides

Association in the course directory

Last modified: Mo 07.09.2020 15:28