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

Continuous assessment of course work
MIXED

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. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

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

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

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

Minimum requirements and assessment criteria

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

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

Module: VIS VMI

Last modified: We 12.01.2022 12:09