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Visual Analytics

(in German: Visual Analytics - )

Module-ID: FIN-INF-140007
Link: LSF
Responsibility: Bernhard Preim
Lecturer: Bernhard Preim
Classes:
  • Lecture Visual Analytics
  • Exercise class Visual Analytics
 
Applicability in curriculum:

Abbreviation

Credit Points

6

Semester

Summer

Term

ab 1.

Duration

1 Semester

Language

english

Level

Master

Intended learning outcomes:
Students ...

  • know machine learning methods and the visualization of their results
  • apply this knowledge to analyse financial and business data
  • use statistical methods to improve visual analytics solutions
  • can assess and compare clustering methods

Content:

  • Introduction: Potential and applications of Visual Analytics
  • Visual Analytics based on clustering
  • Visual Analytics based on subspace clustering and biclustering
  • Visual Analytics with decision trees
  • Visual Analytics with association rules
  • Scatterplot-based visualizations
  • Visual Analytics of events sequences
  • Interaktive und Kooperative Methoden von Visual Analytics
  • Visual Analytics im Gesundheitswesen

Workload:

  • 56 contact hours + 124 h self study

Type of examination: Teaching method / lecture hours per week (SWS):

Written exam

  • 2 SWS Lecture
  • 2 SWS Exercise class
Prerequisites according to examination regulations: Recommended prerequisites:

none

  • Lecture Visualization
  • Knowledge of Data Analysis, for example Intelligent Data Analysis
  • Data Mining
  • Machine Learning
  • Artificial Intelligence
Media: Literature:

  • Powerpoint presentation
  • Whiteboard
  • Videos

  • J. J. Thomas, K. A. Cook (Hrsg.): Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society 2005
  • D. A. Keim, F. Mansmann, J. Schneidewind, J. Thomas, H. Ziegler: Visual analytics: Scope and challenges. Visual Data Mining, 2008

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