| Professor |
Prof. Dr. Andreas Kerren
|
| Time & Place |
Compare here!
In general, the lectures will take place in the rooms B2034 and B3033 (Building B), Mondays from 13:15 till 15:00. There are several exceptions due to a number of holidays.
|
| Teaching Period |
IV (2020-03-23 till 2020-06-05)
|
| Assessment |
Assignments and oral examinations. Oral examinations will take place via Zoom in week 23.
|
| Prerequisites |
90 credits in Computer Science incl. a course in Computer Graphics (1DV800) and 4DV800 Information Visualization, or similar documented experiences.
|
| Credits |
7.5 ECTS
|
| Topic |
Information Visualization in Special Domains
This course extends Information Visualization with visualization techniques and systems for special data sets, such as networked data (graphs and networks), time-dependent data, text, document collections, or software (so-called software visualization). Furthermore, we discuss the evaluation of visualizations, collaborative/personal information visualization, as well as applications in bioinformatics, geography, etc.
This course aims at giving an overview of the most important techniques and prerequisites needed to develop effective
visualizations of abstract information. After finishing the course, the students should be able to choose and develop
the most suitable technique for special data sets and applications domains.
|
| Schedule |
Preliminary Schedule:
| # |
Date |
Topic |
Slides |
| 1 |
2020 03 23 |
Introduction and InfoVis Toolkits |
|
| 2 |
2020 03 31 |
Graphs, Networks, and Applications |
|
| 3 |
2020 04 06 |
WebVis and BioVis |
|
| 4 |
2020 04 14 |
Time-series Visualization |
|
| 7 |
2020 04 20 |
Text & Documents I |
|
| 8 |
2020 04 27 |
Text & Documents II |
↑
|
| 5 |
2020 05 04 |
Software Visualization I |
|
| 6 |
2020 05 11 |
Software Visualization II |
↑
|
| 9 |
2020 05 18 |
Collaborative & Personal Visualization |
|
| 10 |
2020 05 19 |
Visual Analytics |
|
| 11 |
2020 06 01 |
Evaluation and Top 10 InfoVis Challenges |
|
|
| Materials |
Learning Environment:
Visualization Tools and Libraries:
- D3 (JavaScript)
- Vega (JavaScript; based on D3)
- Vega-Lite (JavaScript; based on D3 and Vega)
- plotly.js (JavaScript; based on D3)
- nivo (JavaScript; based on D3 and React)
- Victory (JavaScript; based on D3 and React)
- Chart.js (JavaScript)
- vis.js (JavaScript)
- Highcharts (JavaScript)
- Google Charts (JavaScript)
- Rickshaw (Temporal data; JavaScript; based on D3)
- Leaflet (Geospatial data (maps); JavaScript)
- Sigma (Graph/network data; JavaScript)
- Bokeh (Python; generates a web-based visualization)
- Dash (Python or R; generates a web-based visualization using plotly.js)
- plotly.py (Python; generates a web-based visualization using plotly.js)
- Altair (Python; generates a web-based visualization using Vega and Vega-Lite)
- mpld3 (Python; generates a web-based visualization using D3)
- Shiny (R; generates a web-based visualization)
- Shiny Dashboard (R; generates a web-based visualization using Shiny)
- Tableau (Visualization environment, Dashboards)
- QlikView (Visualization environment, Dashboards)
- Power BI (Visualization environment, Dashboards)
- Visualize Free (Online, Dashboards)
- Keshif Online (Online, Dashboards)
- yFiles (Several platforms)
- Gephi (The Open Graph Viz Platform; Java)
- Processing (Environment for graphics programming; several platforms)
- Improvise (Java)
- JFreeChart (Java)
- ggplot2 (R)
- Chaco (Python)
- Matplotlib (Python)
No longer in active development (not recommended for assignments):
Interesting URLs:
|
| Assignments |
Assignments consist of theoretical and practical exercises as well as short class presentations/demos. They will be supervised by Angelos Chatzimparmpas.
Assignments:
- Assignment 1 (Deadline: 2020-04-05, Presentation: 2020-05-06, 15:00-16:45, Online via Zoom)
- Assignment 2 (Deadline: 2020-04-26, Presentation: 2020-04-27, 15:00-16:45, Online via Zoom)
- Assignment 3 (Deadline: 2020-05-31, Presentation: 2020-06-01, 15:00-16:45, Online via Zoom)
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