inf966 - Foundations of STS Eng.: Statistics and Programming

No participant requirement

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.

Self-competences
The students:

• gain experiences in Pursuing goals: Thinking, problem solving and acting
• lern to analyze and evalutate the effects an relevance of datasets for specific research questions

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.

At the end of the lecture period

Written or oral exam