WiSe 23/24: Data Visualization
Claudia Müller-Birn
Additional information / Pre-requisites
Additional information is available on the HCC Research Group website.
Comments
Today's rapid technological development requires the processing of large amounts of data of various types in order to make them useful to people. This challenge of making data useful affects many areas of life today, including research, business, and politics. In these contexts, decision makers use data visualizations to explain information and its relationships through graphical representations. This course is designed to familiarize students with the principles, techniques, and methods of data visualization and to provide practical skills for designing and implementing data visualizations.
This course provides students with a solid introduction to the fundamentals of data visualization, including current insights from research and practice. At the end of the course, students will be able to
- select and apply appropriate methods for designing visualizations based on a problem,
- know the essential theoretical foundations of visualization for graphical perception and cognition,
- know and be able to select appropriate visualization techniques for a given problem, as well as the advantages and disadvantages of these techniques.
- be able to critically evaluate data visualizations, and
- have acquired practical skills for implementing visualizations.
This course is designed for students who are interested in using data visualization in their work as well as for students who want to develop visualization software. Basic knowledge in programming (Python, HTML, CSS, Javascript) and data analysis (e.g. Python, R) is helpful.
In addition to participating in class discussions, students will complete several programming and data analysis assignments. In a mini-project, students will work on a given problem. Finally, students will be expected to document and present their assignments and mini-project in a reproducible manner.
Please note that the course will focus on how to visually encode and present data for analysis once the data structure and content are known. We will not cover exploratory analysis methods in detail.
closeSuggested reading
Textbuch
Munzner, Tamara. Visualization analysis and design. AK Peters/CRC Press, 2014.
Zusätzliche Literatur
Kirk, Andy: Data visualisation: A handbook for data driven design. Sage. 2016.
Yau, Nathan: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley Publishing, Inc. 2011.
Spence, Robert: Information Visualization: Design for Interaction. Pearson. 2007.
close16 Class schedule
Additional appointments
Tue, 2024-04-09 10:00 - 12:00Regular appointments