- Visualization pipeline
- Human perception and laws of form
- Characteristics of data
- Visual coding and its systematization
- Interaction mechanisms
Visualization of univariate data
- Corridors visual mappings
- Discussion of the approaches
- Design guidelines and sources of error
Visualization of multivariate data
- Direct mapping procedures
- Performant implementations
- Linear projections in the visualization
Visualization of graphs
- Design strategies
- Tree representations
- Directed and undirected graphs
Scalar field visualization
- Representation of fields on the computer
- Basic procedures in 2D and 3D
Data Visualization (M, 4.0 LP)
|Module Number||Module Name||CP (Effort)|
|INF-19-31-M-5||Data Visualization||4.0 CP (120 h)|
|CP, Effort||4.0 CP = 120 h|
|Position of the semester||1 Sem. in WiSe|
|Level|| Master (Entry Level)|
|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||42 h||78 h||
- About [INF-19-31-K-5]: Title: "Data Visualization"; Presence-Time: 42 h; Self-Study: 78 h
- About [INF-19-31-K-5]: The study achievement [U-Schein] proof of successful participation in the exercise classes (ungraded) must be obtained. It is a prerequisite for the examination for PL1.
Examination achievement PL1
- Form of examination: written or oral examination
- Examination Frequency: each winter semester
- Examination number: 61932 ("Data Visualization")
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
- implement basic techniques of data visualization and apply them to concrete problems
- analyse and categorise available techniques in terms of quality, efficiency and suitability for specific data
- select and apply suitable visualization tools based on their functionality for the respective problem definition.
- Alexandru C. Telea: Data Visualization Principles and Practice, AK Peters ltd., 2007.
- Robert Spence: Information Visualization, Addison Wesley, 2000.
- Colin Ware: Information Visualization, Morgan Kaufmann, 2. Edition, 2004.
Requirements for attendance (informal)
Requirements for attendance (formal)
References to Module / Module Number [INF-19-31-M-5]
|Course of Study||Section||Choice/Obligation|
|[INF-88.79-SG] M.Sc. Computer Science||Specialization 1||[WP] Compulsory Elective|
|[WIW-82.789-SG] B.Sc. Business Studies with Technical Qualifications||Field of study: Computer Science||[WP] Compulsory Elective|
|[WIW-82.?-SG#2021] B.Sc. Business Studies with Technical Qualifications 2021 ||Technical Profile Area||[WP] Compulsory Elective|
|[INF-VIS_Ba_V-MPOOL-4]||Specialization Bachelor TA Visualization and Scientific Computing|