Modul


Allgemeine Informationen
Data Visualization and Visual Analytics
Data Visualization and Visual Analytics
MADS-DVVA
DataVisVisAn-01-MA-M
Prof. Dr. Schwörer, Tillmann (tillmann.schwoerer@haw-kiel.de)
Prof. Dr. Schwörer, Tillmann (tillmann.schwoerer@haw-kiel.de)
Wintersemester 2026/27
1 Semester
In der Regel jedes Semester
Englisch
Studiengänge und Art des Moduls (gemäß Prüfungsordnung)
Studiengang Vertiefungsrichtung Schwerpunkt Modulart Fachsemester
M.Sc. - DS - Data Science Pflichtmodul

Kompetenzen / Lernergebnisse
Kompetenzbereiche: Wissen und Verstehen; Einsatz, Anwendung und Erzeugung von Wissen; Kommunikation und Kooperation; Wissenschaftliches Selbstverständnis/Professionalität.
Students know
- available visualization techniques and understand for which purpose they are most suitable,
- tools and best practices to closely integrate visual analysis, documentation, and presentation,
- programming frameworks for data visualization
Students are able to
- use visualizations as a means to detect patterns in complex data,
- design and develop expressive visualizations tailored to the specific purpose and recipient using programming languages
Students are able to
- concisely present their approach and results in technical and functional terms
- work successfully in teams on joint projects, leveraging and integrating the skills of all team members.
Students are able to
- reflect on the strengths and weaknesses of visualization techniques,
- give and receive constructive critique and advice
and they adhere to principles for scientific communication.
Angaben zum Inhalt
Foundations of Data Visualization
- Perception and Visualization Design
- Interactive Dashboards
- Visual storytelling

Python for Data Visualization
- Plotly
- Matplotlib
- Geopandas
- Streamlit

Applications
- Comparing categories
- Relationships
- Time series
- Geographic data
- Interactive visualization
- Lecture Slides
- Cole Nussbaumer Knaflic, Storytelling with Data: A Data Viszualization Guide for Business Professionals, 2015
- Jonathan Schwabish, Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks, 2021
- Claus O. Wilke, Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O'Reilly, first edition, 2019, online available: https://serialmentor.com/dataviz.
Lehrformen der Lehrveranstaltungen
Lehrform SWS
Lehrvortrag + Übung 4
Arbeitsaufwand
4 SWS
5,0 Leistungspunkte
48 Stunden
102 Stunden
Modulprüfung
Prüfungsform Dauer Gewichtung wird angerechnet gem. § 11 Absatz 2 PVO Benotet Anmerkung
Portfolioprüfung 100 %
Sonstiges
Basic knowledge of Python.