Universität Wien
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040242 VO Econometrics and Statistics (MA) (2018W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften

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Details

Language: German

Examination dates

Lecturers

Classes (iCal) - next class is marked with N

Vorlesung entfällt am 31.10.2018

  • Wednesday 03.10. 16:45 - 18:15 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 10.10. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 17.10. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 24.10. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 31.10. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 07.11. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 14.11. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 21.11. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 28.11. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 05.12. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 12.12. 16:45 - 18:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 09.01. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 16.01. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 23.01. 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

Final test at the end of the course, 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.
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

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

More literature will be announced during the course

Slides of the course, will be posted on Home page

Association in the course directory

Last modified: Mo 07.09.2020 15:29