Lecture with Exercise: Data Visualization
(L: 19328301 E: 19328302)
|Type||Lecture with Exercise|
|Instructor||Dr.-Ing. Christoph Kinkeldey|
|Start||Oct 05, 2020 | 10:00 AM|
|end||Oct 23, 2020 | 03:00 PM|
Mon - Fri 10 am - 12 pm and 1 pm - 3 pm
Interested? Join us for the info meeting Sep 24, 2020 | 11 am - 12 pm
The lecture is a block course and is offered in the summer break of the winter term (Oct. 05, 2020 - Oct. 23, 2020)!
The rapid technological development requires the processing of large amounts of data of various kinds to make them usable by humans. This challenge affects many areas of life today. Especially in research, economy, and politics data visualization is used to explain information and correlations by graphical representation, to explore them by visual analysis, and to support decision making. The goal of this course is to familiarize students with the principles, techniques, and algorithms of data visualization and to develop practical skills for designing and implementing data visualizations.
This course teaches students the fundamentals of data visualization with current content from research and practice. At the end of the course the students will:
- know the essential theoretical basics of visualization for graphical perception and cognition,
- know and be able to apply methods for the visual coding of data, as well as methods of interactive visualization
- understand and be able to apply algorithms and techniques for visualizing data (diagrams, graphs, maps), including methods of interaction
- be able to critically evaluate visualization solutions, and
- have practical skills in the design of visualizations and their implementation.
Besides participating in the discussions in the course, students complete several programming and data analysis tasks, as well as a final project as an executable visualization tool. Students are expected to document and present the results of the tasks and the project.
Please note that the course focuses on how data is visually coded, presented, and analyzed once the structure of the data and its content is known. Explorative analytical methods for discovering insights in data are not the focus of the course.
Here you can find our Code of Conduct.
Munzner, Tamara. Visualization analysis and design. AK Peters/CRC Press, 2014.
Interactive Data Visualization for the Web, 2nd Edition. Scott Murray, O'Reilly Press. 2017.
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.