Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Module INF-19-51-M-6

Visual Analytics (M, 5.0 LP)

Module Identification

Module Number Module Name CP (Effort)
INF-19-51-M-6 Visual Analytics 5.0 CP (150 h)

Basedata

CP, Effort 5.0 CP = 150 h
Position of the semester 1 Sem. in SuSe
Level [6] Master (General)
Language [DE/EN] German or English as required
Module Manager
Lecturers
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

Courses

Type/SWS Course Number Title Choice in
Module-Part
Presence-Time /
Self-Study
SL SL is
required for exa.
PL CP Sem.
2V+2U INF-19-51-K-6
Visual Analytics
P 56 h 94 h
U-Schein
ja PL1 5.0 SuSe
  • About [INF-19-51-K-6]: Title: "Visual Analytics"; Presence-Time: 56 h; Self-Study: 94 h
  • About [INF-19-51-K-6]: 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: 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.


Contents

  • formal foundation
    • 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
    • classifiers
    • 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

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.

Literature

  • 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.

Requirements for attendance of the module (informal)

None

Requirements for attendance of the module (formal)

None

References to Module / Module Number [INF-19-51-M-6]

Course of Study Section Choice/Obligation
[INF-88.79-SG] M.Sc. Computer Science [Specialisation] Specialization 1 [WP] Compulsory Elective
Module-Pool Name
[INF-SIAK-DT-CS-MPOOL-6] SIAK Certificate "Digital Transformation" - Modules INF "Computer Science"