Modulcode:	infDataVis-01a
Englische Bezeichnung:	Data Visualization
Modulverantwortliche(r):	Dr.-Ing. Claudius Zelenka
Turnus:	unregelmäßig (WS22/23 WS23/24 WS24/25)
Präsenzzeiten:	2V 2Ü
ECTS:	6
Workload:	30 Std. Vorlesung, 30 Std. Präsenzübung, 120 Std. Selbststudium
Dauer:	ein Semester
Modulkategorien:	BSc-Inf-WP (BSc Inf (21)) BSc-WInf-WP-Inf (BSc WInf (21)) MSc-Inf-WP (MSc Inf (21)) 2F-MEd-Inf-WP (MEd-Hdl Inf (21)) 2F-MA-Inf-WP (2F-MA Inf (21)) MSc-WInf-WP-Inf (MSc WInf (21))
Lehrsprache:	Englisch
Voraussetzungen:

Kurzfassung:

Visualization makes data understandable. It makes patterns and connections visible, acts as a key to new insights, and enables communication and decision-making in science, business or teaching. The goal of this course is to introduce you to visual representation methods and techniques that increase the understanding of complex data.

Lernziele:

Students learn how to

• understand what makes a good visualization
• create and analyze data visualizations
• reflect on the strengths and weaknesses of visualization techniques
• give and receive constructive critique and advice

Lehrinhalte:

Starting with the fundamentals of visual perception by the human vision system, data abstraction and color this course covers a variety of good design practices and visualization techniques in the following applications and topics:

• One and multidimensional data
• Spatial data
• Time series
• Networks
• Interactive visualizations
• If time permits: Text

Weitere Voraussetzungen:

basic to medium level python programming skills. If you attended any of the lectures with a lot of python usage such as Introduction to algorithms, Data Science, Computer networks or similar you should do fine, otherwise some self study may be required. Resources will be made available on the course page.

Prüfungsleistung:

Oral or written exam at the end of the semester

Lehr- und Lernmethoden:

Lectures and exercises with small projects

Verwendbarkeit:

Literatur:

• Tamara Munzner. Visualization Analysis and Design. A K Peters Visualization Series, CRC Press, 2014.
• Andy Kirk, Data Visualisation. A Handbook for Data Driven Design. Second Edition (Revised Edition).
• Claus O. Wilke: Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O'Reilly, first edition, online: https://serialmentor.com/dataviz

Verweise:

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