Reiter
Description
An old English adage says "a picture is worth a 1,000 words", meaning that complex ideas are often easier to convey visually. This lecture is about the craft of creating informative images from data. Starting from the basics of the human visual perception, we will learn how visualizations are designed for explorative, communicative or confirmative purposes. We will see how data can be classified, allowing us to develop algorithms that apply to a wide range of application domains.
Content
The lecture covers the following topics:
- data abstraction (data types, data set types, attribute types),
- perception and mapping (marks and channels, effectiveness, pre-attentive vision, color maps),
- web-based tool (HTML, CSS, JavaScript, React, D3),
- task abstraction and validation (actions and targets),
- tabular data (scatterplots, bar charts, parallel coordinates),
- networks and trees (static and force-directed layouts),
- visualization of neural networks (in particular for deep learning)
- scalar and vector field data (volume and particle visualization),
- medical data (MRI, CT, tensor data),
- tools for scientific visualization (VTK, ParaView, Matlab),
- view manipulation (navigation, selection, multiple views),
- data reduction (filtering, agreggation, focus and context),
- lies in visualization (human biases and rules of thumb).
The lecture is accompanied by exercises. Theoretical exercises concentrate on the classification of data and the design and analysis of visualizations, while programming exercises using web-based technologies give examples of their implementation. This course is taught in English.
Grading
The course is concluded with a written exam in presence (90 minutes) at the end of the semester. In addition, the theory exercises are conducted online as graded StudOn tests and at least 50% of the points must be reached to complete the module. If 12 out of 20 points of the programming exercises are reached, then, as a bonus, the final grade will be improved by a third grade, i.e., by 0.3 or 0.4 grade points, respectively.
Goals and Competences
Students are able to:
- use perceptual basics to select appropriate visualization methods
- explain the steps of the visualization pipeline
- calculate direct and indirect volume visualizations to given data
- explain and apply interaction concepts
- perform a data and requirement analysis for a given problem
- explain visualization techniques for scientific and abstract data
Live Stream, Lecture Recordings & Virtual Exercise
The lectures are live-streamed and recorded, and are made available on FAU.tv. Further, one of the theory exercises is provided in virtual form, making it possible to take the course fully virtually.
Registration
The course is open for registration in the weeks prior to the start of the winter semester. At any other time, you can send your IdM to ✉ tobias.guenther@fau.de and ask for admittance into the StudOn course.
Kategorien
Kurse
Visualization - WiSe 24/25
Vis, 4 SWS, Englisch
Anmeldungszeitraum: Keine Anmeldung möglich
Maximale Teilnehmeranzahl:100 Freie Plätze: 0
Verfügbarkeit: 1. Sep 2024, 11:20 - 1. Sep 2025, 11:20
Di 16:15 - 17:45
Tobias Günther
Winter 2024/25: | | » campo
Um dieses Objekt zu nutzen, müssen Sie angemeldet sein und entsprechende Zugriffsrechte besitzen.
Maximale Teilnehmeranzahl:100 Freie Plätze: 0
Verfügbarkeit: 1. Sep 2024, 11:20 - 1. Sep 2025, 11:20
Di 16:15 - 17:45
Tobias Günther
Winter 2024/25: | | » campo
Um dieses Objekt zu nutzen, müssen Sie angemeldet sein und entsprechende Zugriffsrechte besitzen.