052215 VU Visualisation and Visual Data Analysis (2021W)
Prüfungsimmanente Lehrveranstaltung
Labels
GEMISCHT
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 13.09.2021 09:00 bis Mo 20.09.2021 09:00
- Abmeldung bis Do 14.10.2021 23:59
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
Art der Leistungskontrolle und erlaubte Hilfsmittel
handing in of homework, 5x assignments, 56%
2 tests, 40%
participation 4%
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.
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
readings
Literatur
T. Munzner: Visualization Analysis & Design: Abstractions, Principles, and Methods, CRC Press, 2014various papers as presented on the course page
Zuordnung im Vorlesungsverzeichnis
Module: VIS VMI
Letzte Änderung: Fr 12.05.2023 00:13
* 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 topicCourse-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 environmentsGeneral 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