| Professor |
Prof. Dr. Andreas Kerren
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| Time & Place |
Compare here!
In general, the lectures will take place online via Zoom, Tuesdays from 13:15 till 15:00.
|
| Teaching Period |
II (2020-11-02 till 2021-01-22)
|
| Assessment |
Assignments and oral examinations. Oral examinations will take place via Zoom in week 3.
|
| Prerequisites |
90 credits in Computer Science (including a degree project at Bachelor level), 5 hp information visualization on advanced level (e.g., 4DV805 or equivalent), and English B/English 6 (or the equivalent).
|
| Credits |
5 ECTS
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| Topic |
Information Visualization in Special Domains
This course extends Information Visualization with visualization techniques and systems for special data sets, such as networked data (trees, graphs), 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, cf. learning objectives in the course syllabus.
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| Schedule |
Preliminary Schedule:
| # |
Date |
Topic |
Slides |
| 1 |
2020 11 03 |
Introduction and InfoVis Toolkits |
|
| 2 |
2020 11 10 |
Hierarchies (Trees) |
|
| 3 |
2020 11 17 |
Networks (Graphs) |
|
| 4 |
2020 11 24 |
Time-series Visualization |
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| 5 |
2020 12 01 |
Text & Documents I |
|
| 6 |
2020 12 02 |
Text & Documents II |
↑
|
| 7 |
2020 12 07 |
WebVis and BioVis |
|
| 8 |
2020 12 08 |
Software Visualization |
|
| 9 |
2020 12 15 |
Collaborative & Personal Visualization |
|
| 10 |
2021 01 12 |
Visual Analytics |
|
| 11 |
2021 01 19 |
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:
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| 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: 2020-11-16, Presentation: 2020-11-17, 15:00-16:45, Online via Zoom)
- Assignment 2 (Deadline: 2020-11-30, Presentation: 2020-12-01, 15:00-16:45, Online via Zoom)
- Assignment 3 (Deadline: 2021-01-18, Presentation: 2021-01-19, 15:00-16:45, Online via Zoom)
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