Universität Wien
Warning! The directory is not yet complete and will be amended until the beginning of the term.

053622 VU Visual and Exploratory Data Analysis (2022S)

4.00 ECTS (3.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work

URL http://vda.univie.ac.at/Teaching/Vis
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 01.03. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 04.03. 11:30 - 13:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 08.03. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Tuesday 15.03. 11:30 - 13:00 Digital
  • Friday 18.03. 11:30 - 13:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 22.03. 11:30 - 13:00 Digital
  • Friday 25.03. 11:30 - 13:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 29.03. 11:30 - 13:00 Digital
  • Friday 01.04. 11:30 - 13:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 05.04. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 08.04. 11:30 - 13:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 26.04. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 29.04. 11:30 - 13:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 03.05. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 06.05. 11:30 - 13:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 10.05. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Tuesday 17.05. 11:30 - 13:00 Digital
  • Tuesday 24.05. 11:30 - 13:00 Digital
  • Tuesday 31.05. 11:30 - 13:00 Digital
  • Tuesday 14.06. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Tuesday 21.06. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Tuesday 28.06. 11:30 - 13:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Friday 01.07. 09:45 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG

Information

Aims, contents and method of the course

Computer-based visualization systems provide visual representations of datasets intended to help people carry out some task more effectively. These datasets can come from very diverse sources, such as scientific experiments, simulations, medical scanners, commercial databases, financial transactions, health records, social networks and the like. In this course we deal with effective visual mappings as well as interaction principles for various data, understand perceptual and cognitive aspects of visual representations and learn how to evaluate visualization systems.

Topics covered will include (but are not limited to):

* Introduction and historical remarks
* Visual design principles and the visualization pipeline
* Design studies
* Data acquisition and representation
* Basic visual mapping concepts (marks + channels)
* Human visual perception + Color
* Visual mappings for tables and multi/high-dimensional data
* Visual mappings for networks, graphs and trees
* Visual mappings and algorithms for 2D+3D scalar, vector, and tensor fields
* Visual mappings for text data
* Principles of multiple coordinated views
* Data interaction principles including Brushing+Linking, Navigation+Zoom , Focus+context
* Principles of Evaluation of visual analysis systems
* some selected advanced topic

Course-specific goals -- students can:
* represent and interact with various data visually
* evaluate visual depictions of data and possible find improved presentations
* assist users in visual data analysis
* use different visual analysis tools, like Tableau
* use D3 to create interactive web-visualization environments

General goals -- students gain:
* insight into a new discipline and extend their scientific horizons
* an appreciation for the interplay of mathematical analysis and user-centered design
* experience working in a team

Assessment and permitted materials

handing in of homework, 5x assignments
participation
test

Minimum requirements and assessment criteria

There is no formal prerequisite. However, there are programming assignments in javascript/D3 that you will be graded on, so we expect programming skills.

The grading scale for the course will be:
1: at least 87.5%
2: at least 75.0%
3: at least 62.5%
4: at least 50.0%

In order to pass the course successfully, you will need to reach a minimum of 50% on all assignments combined, 25% of the points on the last assignment, as well as a minimum of 40% on the test.

Examination topics

applied exercises and tasks
readings

Reading list

T. Munzner: Visualization Analysis & Design: Abstractions, Principles, and Methods, CRC Press, 2014

various papers as presented on the course page

Association in the course directory

Modul: VED

Last modified: Th 11.05.2023 11:27