| Instructors |
Dr. Rafael Messias Martins
Dr. Kostiantyn Kucher
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| Course Examiner |
Prof. Dr. Andreas Kerren
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| Time & Place |
Compare here!
In general, the lectures, seminars, and tutoring sessions will take place online via Zoom.
Additional video materials will be shared via the Moodle course room.
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| Teaching Period |
I & II (2020-08-31 till 2021-01-17)
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| Assessment |
A programming project (= planning, project work, and final reports), oral presentations (= seminars), and an oral exam (= final project presentation).
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| Prerequisites |
90 credits in Computer Science (including a degree project at Bachelor level). 10 credits project course on advanced level (e.g. 4DV651, 4DV652 or equivalent). English B/English 6 or the equivalent.
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| Credits |
10 ECTS
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| Topic |
Project in Visual Analytics with a given analytical problem and setting
Visual Analytics (VA) systems bring data analysis closer to end-users by effectively combining interactive visualization and complex algorithms, guided by the underlying analytical processes inherent to the data and the application at hand.
The course is a project course with a focus on VA with a given analytical problem and setting. The students are expected to work using agile processes in teams and to manage their projects independently.
The students will be introduced to VA theoretical aspects and tools, create the conceptual design of the VA project, implement their designs, and present their results.
Covered topics are among others: the importance of data and visualization for answering analytical questions, tools and libraries for data analysis and visualization, and evaluation of VA projects.
The courses Information Visualization and Advanced Information Visualization and Applications complement this practical project course with a more in-depth view into the theory and state-of-the-art of interactive information visualization and its applications in various domains.
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| Schedule |
Preliminary Schedule (Lectures and Video Tutorials):
| # |
Date |
Topic |
Slides |
Videos |
| 1 |
2020 09 02 |
Introduction and Motivation for Visual Analytics |
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| 2 |
2020 09 03 |
Information Visualization Lecture I |
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| 3 |
2020 09 09 |
Information Visualization Lecture II |
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| 4 |
2020 09 10 |
Practical Lecture I (High-Level Visualization Tools) |
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Video Tutorial I (Initial Data Preprocessing and Exploration) |
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| 5 |
2020 09 16 |
Visual Analytics Lecture I |
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| 6 |
2020 09 17 |
Practical Lecture II (Visualization Toolkits and Libraries) |
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Video Tutorial II (Initial Design) |
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| 7 |
2020 10 01 |
Practical Lecture III (Computational Toolkits) |
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| 8 |
2020 09 30 |
Visual Analytics Lecture II |
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| 9 |
2020 10 14 |
Evaluation Lecture |
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Video Tutorial V (Project Deployment) |
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Preliminary Schedule (Seminars):
| # |
Date |
Topic |
| 1 |
2020 09 23–24 |
Project Data Set Presentation |
| 2 |
2020 10 07–08 |
Project Proposal Discussion |
| 3 |
2020 10 21–22 |
Project Implementation Kick-off |
| 4 |
2020 11 04–05 |
Initial Demo + Planning |
| 5 |
2020 11 18–19 |
Demo + Planning |
| 6 |
2020 12 02–03 |
Demo + Planning |
| 7 |
2020 12 16–17 |
Project Evaluation Session |
| 8 |
2021 01 07 |
Demo + Planning |
| 9 |
2021 01 13–14 |
Final Project Presentation + Demo |
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| 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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| Open Theses |
We permanently offer interesting topics for Bachelor's and Master's Theses that are related to Information Visualization and Software Visualization.
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