Universität Wien FIND

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053622 VU Visual and Exploratory Data Analysis (2021S)

Continuous assessment of course work

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first serve).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

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

Monday 01.03. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG (Kickoff Class)
Monday 08.03. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG
Thursday 11.03. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
Monday 15.03. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG
Thursday 18.03. 16:45 - 18:15 Seminarraum 9, Währinger Straße 29 2.OG
Monday 22.03. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG
Monday 12.04. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG
Monday 19.04. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG
Thursday 22.04. 13:15 - 14:45 Seminarraum 5, Währinger Straße 29 1.UG
Monday 26.04. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG
Monday 03.05. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG
Monday 10.05. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG
Monday 17.05. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG
Monday 31.05. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG
Monday 07.06. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG
Monday 14.06. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG
Monday 21.06. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG
Monday 28.06. 16:45 - 18:15 Seminarraum 4, Währinger Straße 29 1.UG

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
* 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

Assessment and permitted materials

handing in of homework, 5x assignments
participation
test

Minimum requirements and assessment criteria

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.

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

Modul: VED

Last modified: Th 25.02.2021 12:27