In many contexts we are confronted with growing amounts of data that are increasingly harder to make sense of. One possibility to face this problem of information overload is Information Visualization (InfoVis). By taking advantage of the powerful human perceptual system that is extremely efficient in processing visual input, visualization can help to make sense of data, explore complex information spaces or spot patterns and relationships within the data. Information Visualization is an interdisciplinary field of research that is concerned with interactive visualizations of mainly abstract data and has its roots primarily in computer science. Interactivity or the possibility to engage in an active discourse with the representation lies at the core of Information Visualization. Visual Analytics extends InfoVis by integrating automated analysis methods (e.g., statistics, data mining, machine learning) and focusing on analytical reasoning supported by interactive visual interfaces.
The goal of this course is to gain knowledge about methods,concepts, and techniques of Information Visualization (InfoVis) and Visual Analytics. The students should be able to assess the quality of visual representations and gain skills for problem analysis, the design and implementation, as well as the evaluation of visualization systems.
Didactic Concept:
- Lectures about concepts, methods, and techniques
- Providing references to primary and advanced literature
- Discussion of advantages and disadvantages of concepts, techniques, and scientific literature
- Usage of the InfoVis:Wiki
- Practical application of concepts, methods, and techniques in the accompanying InfoVis lab lecture (188.308)
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 a discussion on knowledge cristallization and the information visualization reference model. Afterwards important topics like focus+context, brushing, linking, and interaction will be discussed.
