Universität Wien FIND
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052215 VU Visualisation and Visual Data Analysis (2021W)

Prüfungsimmanente Lehrveranstaltung
GEMISCHT

An/Abmeldung

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

Details

max. 50 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

If you successfully registered for the course, please log in to Moodle in order to join the first lecture via BigBlueButton ("Introduction 05.10.21").
Only use the following guest link if you are NOT registered and sign in with your first and last name: https://moodle.univie.ac.at/mod/bigbluebuttonbn/guestlink.php?gid=3gc2Y8Jh1KQI

Dienstag 05.10. 13:15 - 14:45 Digital
Donnerstag 07.10. 13:15 - 14:45 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
Dienstag 12.10. 13:15 - 14:45 Digital
Donnerstag 14.10. 13:15 - 14:45 Digital
Dienstag 19.10. 13:15 - 14:45 Digital
Donnerstag 21.10. 13:15 - 14:45 Digital
Donnerstag 28.10. 13:15 - 14:45 Hörsaal 3, Währinger Straße 29 3.OG
Donnerstag 04.11. 13:15 - 14:45 Digital
Dienstag 09.11. 13:15 - 14:45 Hörsaal 3, Währinger Straße 29 3.OG
Donnerstag 11.11. 13:15 - 14:45 Digital
Dienstag 16.11. 13:15 - 14:45 Digital
Donnerstag 18.11. 13:15 - 14:45 Digital
Dienstag 23.11. 13:15 - 14:45 Digital
Donnerstag 25.11. 13:15 - 14:45 Digital
Dienstag 30.11. 13:15 - 14:45 Digital
Donnerstag 02.12. 13:15 - 14:45 Digital
Dienstag 07.12. 13:15 - 14:45 Digital
Donnerstag 09.12. 13:15 - 14:45 Digital
Dienstag 14.12. 13:15 - 14:45 Digital
Donnerstag 16.12. 13:15 - 14:45 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
Dienstag 11.01. 13:15 - 14:45 Hörsaal 3, Währinger Straße 29 3.OG
Donnerstag 13.01. 13:15 - 14:45 Digital
Dienstag 18.01. 13:15 - 14:45 Digital
Donnerstag 20.01. 13:15 - 14:45 Hörsaal 3, Währinger Straße 29 3.OG
Dienstag 25.01. 13:15 - 14:45 Hörsaal 3, Währinger Straße 29 3.OG
Donnerstag 27.01. 13:15 - 14:45 Hörsaal 3, Währinger Straße 29 3.OG
Donnerstag 27.01. 13:15 - 16:30 Hörsaal 32 Hauptgebäude, 1.Stock, Stiege 9

Information

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
* 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, 56%
2 tests, 40%
participation 4%

Mindestanforderungen und Beurteilungsmaßstab

A mandatory prerequisite for this class is the successful completion of either Foundations of Computer Graphics (05-2200) or Foundations or Data Analysis (05-2300).

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 tests combined.

Prüfungsstoff

applied exercises and tasks
readings

Literatur

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

various papers as presented on the course page

Zuordnung im Vorlesungsverzeichnis

Module: VIS VMI

Letzte Änderung: Mi 07.09.2022 15:27