Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Module INF-19-31-M-5

Data Visualization (M, 4.0 LP)

Module Identification

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 [5] Master (Entry Level)
Language [DE/EN] German or English as required
Module Manager
Area of study [INF-VIS] Visualisation and Scientific Computing
Reference course of study [INF-88.79-SG] M.Sc. Computer Science
Livecycle-State [NORM] Active


Type/SWS Course Number Title Choice in
Presence-Time /
SL SL is
required for exa.
PL CP Sem.
2V+1U INF-19-31-K-5
Data Visualization
P 42 h 78 h
ja PL1 4.0 WiSe
  • 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.


  • Formal fundamentals
    • 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
    • Colormapping
    • Basic procedures in 2D and 3D

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 of the module (informal)


Requirements for attendance of the module (formal)


References to Module / Module Number [INF-19-31-M-5]

Course of Study Section Choice/Obligation
[WIW-82.789-SG#2009] B.Sc. Business Studies with Technical Qualifications (2009) [2009] [Fundamentals] Field of study: Computer Science [WP] Compulsory Elective
[INF-88.79-SG] M.Sc. Computer Science [Specialisation] Specialization 1 [WP] Compulsory Elective
[WIW-82.?-SG#2021] B.Sc. Business Studies with Technical Qualifications (2021) [2021] [Core Modules (non specialised)] Technical Profile Area [WP] Compulsory Elective
[SO-88A.?-SG#2021] M.A. Soziologie und empirische Sozialforschung mit Schwerpunkt Computational Social Science [2021] [Core Modules (non specialised)] Pflichtmodule [P] Compulsory
Module-Pool Name
[INF-SIAK-DT-CS-MPOOL-6] SIAK Certificate "Digital Transformation" - Modules INF "Computer Science"
[INF-VIS_Ba_V-MPOOL-4] Specialization Bachelor TA Visualization and Scientific Computing