inf040 - Introduction to Data Science (Complete module description)

inf040 - Introduction to Data Science (Complete module description)

Original version English PDF Download
Module label Introduction to Data Science
Modulkürzel inf040
Credit points 6.0 KP
Workload 180 h
Institute directory Department of Computing Science
Verwendbarkeit des Moduls
  • 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
Zuständige Personen
  • Wingerath, Wolfram (module responsibility)
  • Lehrenden, Die im Modul (Prüfungsberechtigt)
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).

Literaturempfehlungen

See description of the assigned course

Links
Language of instruction English
Duration (semesters) 1 Semester
Module frequency regular in summer term
Module capacity unlimited
Teaching/Learning method V + Ü
Previous knowledge Basics of databases, Python programming and statistics
Form of instruction Comment SWS Frequency Workload of compulsory attendance
Lecture 2 SoSe oder WiSe 28
Exercises 2 SoSe oder WiSe 28
Präsenzzeit Modul insgesamt 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