Who needs to learn econometrics

Statistical programming languages

(Bachelor, 3rd or 5th semester)

Goal of the course

Editing the real data sets without a computer is not possible. It is necessary to use statistical programs to perform thorough, high quality analysis quickly. The following topics are offered in this course:

  • get to know the programming language R,
  • to learn modern powerful packages,
  • apply the methods of statistics I and II to real data,
  • correct interpretation and attractive presentation of the results.

Semester schedule

  • The lectures take place in the week 12-16. October 2020 in 2-4. DS takes place as a block course. Lectures and exercises are mixed.
  • In order to receive the credit points, the participants must successfully pass a written openbook exam (120 minutes).
  • The computers in the FAL / 002 are available during the lectures. All participants can also use their own laptop with installed R software (open source) (note the GNU GENERAL PUBLIC LICENSE). Please note that the sockets in the FAL / 002 must not be used for safety reasons.
  • The course language is German.


  • Current information as well as the documents for the course can be found in OPAL, initially without a password. Please register. Thepasswordyou will receive on the first day and then the OPAL page will be password protected.



(Changes are possible)

  • The basics of R: first simple programs, first graphs;
  • Methods from Statistics 1 and 2: descriptive statistics, probability theory, decision theory, linear regression;
  • Numerical Techniques: integration, defferentiation, optimization;
  • Modern visualization tools: rpanel, ggplot;
  • Other modern powerful packages.


  • Härdle, W., Okhrin, O., Okhrin, Y., 2017.Basic Elements of Computational Statistics, Springer Verlag.
  • Wickham, H. and Grolemund, G., 2016. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, O'Reilly Media, Inc.
  • Spector, P., 2008.Data manipulation with R, Series Use R !, Springer Verlag.
  • Cowpertwait, P., Metcalfe, A., 2009.Introductory Time Series with R, Series Use R !, Springer Verlag.
  • Dalgaard, P., 2002.Introductory Statistics with R, Springer Verlag.
  • Silge, J., Robinson, D. 2017.Text Mining with R: A Tidy Approach, O'Reilly