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(dt. Statistik)

Level, degree of commitment Advanced module, compulsory elective module
Forms of teaching and learning,
Lecture (4 SWS), recitation class (2 SWS),
270 hours (90 h attendance, 180 h private study)
Credit points,
formal requirements
9 CP
Course requirement(s): Successful completion of at least 50 percent of the points from the weekly exercises.
Examination type: Written or oral examination (individual examination)
The grading is done with 0 to 15 points according to the examination regulations for the degree program B.Sc. Business Mathematics.
One semester,
Regularly alternating with other advanced modules
Person in charge of the module's outline Prof. Dr. Hajo Holzmann


  • Fundamentals of statistics: statistical models, estimating, testing, confidence intervals
  • the multivariate normal distribution
  • Statistics in the linear model
  • Exponential families and generalized linear models
  • Unbiased estimation and the Cramer - Rao bound
  • Bayes estimators, shrinkage estimators and thresholding
  • Fundamentals of Classification
  • Elements of high-dimensional statistics

Qualification Goals

Students will

  • know important statistical procedures and can analyze them mathematically,
  • can apply the procedures to data sets using the statistical software R,
  • Have further developed their understanding of data analysis and statistics,
  • have improved their oral communication skills in exercises by practicing free speech in front of an audience and in discussion.


None. The competences taught in the following modules are recommended: Elementary Probability and Statistics, Internship Stochastics.


Module imported from B.Sc. Business Mathematics.

It can be attended at FB12 in study program(s)

  • B.Sc. Data Science
  • B.Sc. Mathematics
  • B.Sc. Business Mathematics
  • M.Sc. Data Science
  • M.Sc. Computer Science
  • M.Sc. Mathematics
  • M.Sc. Business Mathematics
  • LAaG Mathematics

When studying M.Sc. Computer Science, this module can be attended in the study area Profile Area Mathematics.

Recommended Reading

  • Trabs, M., Jirak, M., Krenz, K., Reiß, M., "Statistik und maschinelles Lernen", Springer 2020
  • Richter, S. "Statistisches und
  • maschinelles Lernen. Gängige Verfahren im Überblick", Springer, 2019
  • Wasserman, L. "All of Statistics", Springer, 2003

Please note:

This page describes a module according to the latest valid module guide in Winter semester 2023/24. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.