Winter Semester 2016/2017

"20-00-0294-iv Information Visualization and Visual Analytics"

Course Content

This lecture will give a detailed introduction to the scientific topics of information visualization and Visual Analytics, and will cover current research areas as well as practical application scenarios of Visual Analytics.

  • Overview of information visualization and Visual Analytics (definitions, models, history)
  • Data representation and data transformation
  • Mapping of data to visual structures
  • Introduction to human cognition
  • Visual representations and interaction for bivariate and multivariate Data,
    time series, networks and geographic data
  • Basic data mining techniques
  • Visual Analytics - Analytics reasoning - Data mining - Statistics Analytical techniques and scaling
  • Evaluation of Visual Analytics Systems

Learning Outcomes

After successfully attending the course, students will be able to

  • use information visualization methods for specific data types
  • design interactive visualization systems for data from various application domains
  • couple visualization and automated methods to solve large-scale data analysis problems
  • apply knowledge about key characteristics of the human visual and cognitive system for information visualization and visual analytics
  • choose evaluation methods that are used for specific situations and scenarios


Interest in methods of computer graphics and visualization

This lecture is designed for students in computer science, information management, mathematics on a master or diploma level and other related or interested domains (e.g. biology or psychology).

Further Grading Information


Enrollment and TUCAN

Enrollment via TUCAN is necessary both for the lectures and for the exam.


University Calendar:


Winter Semester

Tuesday (L) 13:30 - 15:10

Wednesday (E) 13:30 - 15:10 (group 1) or 15:20 - 17:00 (group 2)

18.10.16 (L),19.10.16 (E)

S305|074 (L), S305|072 (E)

Dr.-Ing. Jörn Kohlhammer, Tatiana von Landesberger

Thorsten May, Kathrin Ballweg

Probably written exam


Elective Course


Course Materials

All course materials and homework assignments will be available in MOODLE.


Will be announced in the lecture. Examples may be:

  • C. Ware: Information Visualization: Perception for Design
  • Ellis et al: Mastering the Information Age

Tableau's data visualization software is provided through the Tableau for Teaching program.


Technische Universität Darmstadt

Interactive Graphics Systems Group

Fraunhoferstr. 5
64283 Darmstadt

Tel. +49 6151 155 679

icon email office@gris.tu-

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