| Instructor |
Dr. Rafael Messias Martins
|
| Course Examiner |
Prof. Dr. Andreas Kerren
|
| Teaching Period |
II (2022-11-08 till 2023-01-16)
|
| Assessment |
Assignments and oral examinations.
Oral examinations will take place in week 2. Further details will be shared via the Moodle classroom.
|
| 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
|
| 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.
|
| Schedule |
Preliminary Schedule:
| # |
Date |
Topic |
Slides |
| 1 |
2022 11 08 |
Introduction and InfoVis Toolkits |
|
| 2 |
2022 11 09 |
Hierarchies (Trees) |
|
| 3 |
2022 11 15 |
Networks (Graphs) |
|
| 4 |
2022 11 16 |
Time-series Visualization |
|
| 5 |
2022 11 22 |
Text & Documents I |
|
| 6 |
2022 11 23 |
Text & Documents II |
|
| 7 |
2022 11 29 |
Geospatial Data |
|
| 8 |
2022 11 30 |
WebVis and BioVis |
|
| 9 |
2022 12 06 |
Software Visualization |
|
| 10 |
2022 12 07 |
Collaborative & Personal Visualization |
|
| 11 |
2022 12 14 |
Visual Analytics |
|
| 12 |
2022 12 20 |
Evaluation and Top 10 InfoVis Challenges |
|
|
| Materials |
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)
- Cytoscape.js (Graph/network data; 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)
- Kepler.gl (Online, Geospatial Networks)
- 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.
|
| Open Theses |
We permanently offer interesting topics for Bachelor's and Master's Theses that are related to Information Visualization and Software Visualization.
|
