Universität Wien FIND

136013 UE Visualization of humanities data (2021S)

Prüfungsimmanente Lehrveranstaltung


Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").


max. 25 Teilnehmer*innen
Sprache: Englisch


Termine (iCal) - nächster Termin ist mit N markiert

This course will be online via zoom on the moodle platform: https://moodle.univie.ac.at/course/view.php?id=209233
The first meeting (Vorbesprechung) on Tue, March 1st, at 4:45pm, will happen via zoom at https://univienna.zoom.us/j/95877235029?pwd=YncwWVVSY0cybTRTZWhCVDdqcG5QUT09
Passcode: VIS21s

Montag 01.03. 16:45 - 18:15 Hybride Lehre
Montag 08.03. 16:45 - 18:15 Hybride Lehre
Donnerstag 11.03. 16:45 - 18:15 Hybride Lehre
Montag 15.03. 16:45 - 18:15 Hybride Lehre
Donnerstag 18.03. 16:45 - 18:15 Hybride Lehre
Montag 22.03. 16:45 - 18:15 Hybride Lehre
Montag 12.04. 16:45 - 18:15 Hybride Lehre
Montag 19.04. 16:45 - 18:15 Hybride Lehre
Donnerstag 22.04. 13:15 - 14:45 Hybride Lehre
Montag 26.04. 16:45 - 18:15 Hybride Lehre
Montag 03.05. 16:45 - 18:15 Hybride Lehre
Montag 10.05. 16:45 - 18:15 Hybride Lehre
Montag 17.05. 16:45 - 18:15 Hybride Lehre
Montag 31.05. 16:45 - 18:15 Hybride Lehre
Montag 07.06. 16:45 - 18:15 Hybride Lehre
Montag 14.06. 16:45 - 18:15 Hybride Lehre
Montag 21.06. 16:45 - 18:15 Hybride Lehre
Montag 28.06. 16:45 - 18:15 Hybride Lehre


Ziele, Inhalte und Methode der Lehrveranstaltung

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

Art der Leistungskontrolle und erlaubte Hilfsmittel

handing in of homework, 5x assignments

Mindestanforderungen und Beurteilungsmaßstab

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.


applied exercises and tasks


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

various papers as presented on the course page

Zuordnung im Vorlesungsverzeichnis


Letzte Änderung: Mi 15.03.2023 00:17