inf040 - Introduction to Data Science (Complete module description)

Module label	Introduction to Data Science
Module code	inf040
Credit points	6.0 KP
Workload	180 h
Institute directory	Department of Computing Science
Applicability of the module	Bachelor's Programme Business Informatics (Bachelor) > Akzentsetzungsbereich Praktische Informatik und Angewandte Informatik Bachelor's Programme Computing Science (Bachelor) > Akzentsetzungsbereich - Wahlbereich Informatik Bachelor's Programme Sustainability Economics (Bachelor) > Wahlpflichtbereich Master Applied Economics and Data Science (Master) > Data Science Master of Education Programme (Gymnasium) Computing Science (Master of Education) > Wahlpflichtmodule (Praktische Informatik) Master of Education Programme (Gymnasium) Computing Science (Master of Education) > Wahlpflichtmodule (Theoretische Informatik) Master of Education Programme (Hauptschule and Realschule) Computing Science (Master of Education) > Mastermodule Master of Education Programme (Vocational and Business Education) Computing Science (Master of Education) > Akzentsetzungsbereich Master's Programme Computing Science (Master) > Praktische Informatik
Responsible persons	Wingerath, Wolfram (module responsibility) Lehrenden, Die im Modul (authorised to take exams)
Prerequisites	Basics of databases, Python programming and statistics
Skills to be acquired in this module	The module teaches fundamentals from the field of Data Science, covering purposes, challenges, and common best practices. Professional competences The students have knowledge of basic concepts, problems and solution approaches from the field of Data Science. are able to justify the choice of specific data analysis methods for a given problem include possible imponderables in the analysis when evaluating analysis results Methological competences The students are able to translate questions from a specific domain into a feasible analysis work on Data Science tasks to expand their understanding of the different approaches and methods. Social competences The students discuss approaches and problems encountered in smaller and larger groups Self competences The students reflect on their actions when identifying possible solutions and critically question their own results
Module contents	Data Science is an interdisciplinary science at the intersection of statistics, machine learning, data visualization, and mathematical modeling. This course is designed to provide a practical introduction to the field of Data Science by teaching theoretical principles while also applying them practically. Topics covered range from data collection and preparation (data sources & formats, data cleaning, data bias), mathematical foundations (statistical distributions, correlation analysis, significance) and methods for visualization (tables & plots, histograms, best practices) to the development of models for classifying or predicting values (linear regression, classification, clustering).
Recommended reading	The Data Science Design Manual (Seven Kiena, 2017) Invisible Women: Data Bias in a World Designed for Men (Caroline Criado-Perez, 2019)
Links
Language of instruction	English
Duration (semesters)	1 Semester
Module frequency	regular in summer term
Module capacity	unlimited
Teaching/Learning method	V+Ü

Type of course	SWS	Frequency	Workload of compulsory attendance
Lecture	2	SuSe or WiSe	28
Exercises	2	SuSe or WiSe	28
Total module attendance time			56 h

Examination	Prüfungszeiten	Type of examination
Final exam of module	At the end of the lecture period or by arrangement with the instructor.	Written or oral exam or portfolio or project or practical exercise