Universität Wien

040038 VO Econometrics and Statistics (MA) (2019S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften

Für diese LV gibt es KEIN Moodle!

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Details

Language: German

Examination dates

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 06.03. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 13.03. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 20.03. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 27.03. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 03.04. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 10.04. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 08.05. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 16.05. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 22.05. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 29.05. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 05.06. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 12.06. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 19.06. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 26.06. 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.

. 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"
Folien die im Kurs diskutiert werden .

Association in the course directory

Last modified: Mo 07.09.2020 15:28