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26.05.2020 12:53:56
inf966 - Foundations of STS Eng.: Statistics and Programming (Complete module description)
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Module label Foundations of STS Eng.: Statistics and Programming
Module code inf966
Credit points 6.0 KP
Workload 180 h
Faculty/Institute Department of Computing Science
Used in course of study
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Fundamentals/Foundations
Contact person
Module responsibility
Authorized examiners
Entry requirements
Skills to be acquired in this module
Professional competences:
The students learn:
To plan, program and interpret statistical data evaluation via programming.

Methodological competences:
The students:
  • understand the main statistical methods and their practical use through application
  • can evaluate statistical methods regarding the qualities and their limits
  • learn the use of statistical software in application scenarios
  • can implement programms via a programming language
  • know how to program statistical data analyses

Social competences:
The students gain experience in interdisciplinary work.

The students gain experiences in
  • Pursuing goals: Thinking, problem solving and acting
  • Ability to analyze and evaluate the effects and relevance of datasets for specific research questions
Module contents
The module consists of a lecture and an exercise part:
Lecture: Introduction to the concepts and methods for computer supported statistically data evaluation. Special emphasis is put on statistically methiodal as well as on a basic understanding of programming languages.
1. Fundamental Computer Science Concepts in regard to the handling of imperative programming languages including:
  • variable types and variable handling
  • typical code structures (such as "while / for loops" or "if-then else" statements)
  • data-handling and computation approaches
2. Fundamental static methodology such as:
  • estimating parameters through the method of maximum likelihood
  • confidence intervals and classical significance testing
  • classical regression analysis
  • modern advancements in regression analysis

Exercises: Stepwise practical or paper based use of the learned concepts, methods and tools.
Reader's advisory
Language of instruction English
Duration (semesters) 1 Semester
Module frequency Once a year
Module capacity unlimited
Modullevel BC (Basiscurriculum / Base curriculum)
Modulart Pflicht o. Wahlpflicht / compulsory or optioal
Lern-/Lehrform / Type of program V+Ü
Vorkenntnisse / Previous knowledge
Course type Comment SWS Frequency Workload attendance
Lecture 2.00 WiSe 28 h
Exercises 2.00 WiSe 28 h
Total time of attendance for the module 56 h
Examination Time of examination Type of examination
Final exam of module
At the end of the lecture period
Written or oral exam