- basic terms of visual data analysis
- modular components of a visual analytics system
- common data sources and their processing
- mathematical concepts of data analysis
- visualization concepts for complex systems
- integration of automated and visual analysis procedures
analysis of classified data
- information theory to quantify the information content
- VA strategies for analysis, exploration and editing of classification algorithms
analysis of time-dependent data
- characteristics of time dependent data
- visualization concepts and algorithms for time-dependent data
- discussion of the supported time characteristics and analysis options
analysis of high-dimensional data
- animation techniques for projection processes
- distance dimensions for high-dimensional data
- non-linear projection method
- VA for evaluation and analysis of projection methods
- cluster methods and their analysis
- topological methods in the VA
Visual Analytics (M, 5.0 LP)
|Module Number||Module Name||CP (Effort)|
|INF-19-51-M-6||Visual Analytics||5.0 CP (150 h)|
|CP, Effort||5.0 CP = 150 h|
|Position of the semester||1 Sem. in SuSe|
|Level|| Master (General)|
|Language||[DE/EN] German or English as required|
|Area of study||[INF-VIS] Visualisation and Scientific Computing|
|Reference course of study||[INF-88.79-SG] M.Sc. Computer Science|
|Type/SWS||Course Number||Title||Choice in |
|SL||SL is |
required for exa.
|P||56 h||94 h||
Examination achievement PL1
- Form of examination: oral examination (20-60 Min.)
- Examination Frequency: each semester
- Examination number: 61951 ("Visual Analytics")
Evaluation of grades
The grade of the module examination is also the module grade.
Competencies / intended learning achievements
After successfully completing the module, students will be able to
- transform complex data and to describe it by means of models.
- implement algorithms for data transformation and analyze and evaluate them.
- extend automated analysis methods by visual interaction mechanisms to integrate human expertise into the analysis process.
- design and implement complex algorithmic systems that help to explore data, make decisions and design models.
- discuss the quality of Visual Analytics System.
- Illuminating the Path edited by J. Thomas and K. Cook, IEEE Press, 2006.
- Ward, Matthew O., Georges Grinstein, and Daniel Keim. Interactive data visualization: foundations, techniques, and applications. CRC Press, 2010.
- Dill, John, et al., eds. Expanding the Frontiers of Visual Analytics and Visualization. Springer Science & Business Media, 2012.