Universität Wien FIND

053622 VU Visual and Exploratory Data Analysis (2022S)

4.00 ECTS (3.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Prüfungsimmanente Lehrveranstaltung

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

An/Abmeldung

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

Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

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

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
* 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
participation
test

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.

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

Modul: VED

Letzte Änderung: Do 11.05.2023 11:27