inf966 - Foundations of STS Eng.: Statistics and Programming (Complete module description)

# inf966 - Foundations of STS Eng.: Statistics and Programming (Complete module description)

 Module label Foundations of STS Eng.: Statistics and Programming Modulkürzel inf966 Credit points 6.0 KP Workload 180 h Institute directory Department of Computing Science Verwendbarkeit des Moduls Master's Programme Engineering of Socio-Technical Systems (Master) > Fundamentals/Foundations Zuständige Personen Timmer, Antje (module responsibility) Hein, Andreas (module responsibility) Lehrenden, Die im Modul (Prüfungsberechtigt) Prerequisites No participant requirement Skills to be acquired in this module Professional competences:The studentslearn to plan, program and interpret statistical data evaluation via programming.Methodological competences:The students:understand the main statistical methods and their practical use through applicationcan evaluate statistical methods regarding the qualities and their limitslearn the use of statistical software in application scenarioscan implement programms via a programming languageknow how to program statistical data analysesSocial competences:The studentsgain experience in interdisciplinary work.Self-competencesThe students:gain experiences in Pursuing goals: Thinking, problem solving and actinglern to analyze and evalutate the effects an 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 handlingtypical code structures (such as "while / for loops" or "if-then else" statements)data-handling and computation approaches2. Fundamental static methodology such as:estimating parameters through the method of maximum likelihoodconfidence intervals and classical significance testingclassical regression analysismodern advancements in regression analysisExercises: Stepwise practical or paper based use of the learned concepts, methods and tools. Literaturempfehlungen Links Language of instruction English Duration (semesters) 1 Semester Module frequency annual Module capacity unlimited Teaching/Learning method V+Ü
Form of instruction Comment SWS Frequency Workload of compulsory attendance
Lecture 2 WiSe 28
Exercises 2 WiSe 28
Präsenzzeit Modul insgesamt 56 h
Examination Prüfungszeiten Type of examination
Final exam of module

At the end of the lecture period

Written or oral exam