After successful completion of the course, students are able to describe the theoretical concepts applied in information visualization and presented in the lecture (e.g., visualization of multimdimensional data, visualization of multivariate network data). They are furthermore able to provide well-founded criticism on existing visualizations and to suggest improvements. When provided with data and usage scenarios, they are able to propose adequate state-of-the-art visualization and interaction techniques to show the data.
Information visualization is "the use of computer-supported, interactive, visual representations of abstract data to amplify cognition". The participants shall get acquainted to the concepts and techniques of information visualization. A short general introduction to visualization is followed by presentations of current research results in the field. The topics include:
- Data & Visual Encodings
- Visual & Perceptual Principles
- Multivariate & High-Dimensional Data Visualization
- Multivariate Network Visualization
- Scalable Visualization for Big Data
- Explainable AI
- Applications of visualization, e.g., for biology or engineering
- Evaluation
There is an exercise accompanying the lecture.
Didactical Approach: With slides, an introduction to information visualization is given. (International) colleagues give guest lectures concerning current special topics.
All lecture materials, lecture dates, readings, exam registrations, news etc. can be found on TUWEL. The lecture alternatives between Wednesdays 3:15-4:45 in FAV01 and Thursdays 1:15-2:45 in EI5 (see "show single appointments").
