Lecture Course: Data Visualization, Summer Term 2017
To become familiar with the basic concepts and algorithms used in the field of visualization. The students should be able to visualize abstract and spatial data in a way that enhances our perception of the desired relationships in the underlying data. In addition, the students should be able to implement a wide range of visualization techniques in existing frameworks or even to design and implement them from the ground up.
The students will be taught the basics in the areas of Information Visualization (INFOVIS) and Scientific Visualization (SCIVIS). The different techniques in the context of the visualization pipeline are covered, which serves as a red thread for the course. The main focus is on interactive visualization techniques that allows the user for example to interact with the visualizations in order to filter the data being displayed or to change display parameters. The course covers the following topics:
- The visualization pipeline
- Data structures for spatial data
- Visualization of scalar, vector and tensor fields
- Visualization of multi-parametric data
- Glyph based techniques
- Key aspects of visual perception
- Applications of modern visualization systems
The labs take place in the lecture block and are interleaved with the lectures.
The lecture course will be held as 3+1 semester week hours scheme. Every other Friday will be used for lab-sessions.
- Tuesdays 14 - 16 ct
- Thursdays 10-12 ct
The lectures as well as the lab-sessions will take place in room O28 / 1002.
- Computer Science, B.Sc., Main Subject
- Computer Science, M.Sc., Core Subject, Practical and Applied Computer Science
- Computer Science and Media, B.Sc., Main Subject
- Computer Science and Media, M.Sc., Core Subject, Mediale Informatik
- Software-Engineering, B.Sc., Main Subject
- Software-Engineering, M.Sc., Core Subject, Practical and Applied Computer Science
- Cognitive Systems, M.Sc., Specialization Subject
- Cognitive Systems, M.Sc., Application Subject Visual Computing
- Computer Science, Lehramt, Optional Module