Instructors |
Dr. Rafael Messias Martins
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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 (2021-08-30 till 2022-01-16)
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Assessment |
A programming project (= planning, project work, and final reports), oral presentations (= seminars), and an oral exam (= final project presentation and individual interview).
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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):
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Date |
Topic |
Slides |
Videos |
1 |
2021 09 01 |
Introduction and Motivation for Visual Analytics |
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2 |
2021 09 02 |
Information Visualization Lecture I |
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Video Tutorial I (Initial Data Preprocessing and Exploration) |
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3 |
2021 09 08 |
Information Visualization Lecture II |
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4 |
2021 09 09 |
Practical Lecture I (High-Level Visualization Tools) |
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5 |
2021 09 15 |
Visual Analytics Lecture I |
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6 |
2021 09 16 |
Practical Lecture II (Visualization Toolkits and Libraries) |
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Video Tutorial II (Initial Design) |
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7 |
2021 09 23 |
Practical Lecture III (Computational Toolkits) |
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Video Tutorial III (Getting Started with Web-Based Visualization) |
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Preliminary Schedule (Seminars):
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Date |
Topic |
1 |
2021 09 22–23 |
Project Data Set Presentation (Proposal Preparations) |
2 |
2021 09 29–30 |
Project Proposal Discussion |
3 |
2021 10 06–07 |
Project Implementation Kick-off |
4 |
2021 10 20–21 |
Initial Demo + Planning |
5 |
2021 11 10–11 |
Demo + Planning |
6 |
2021 11 24–25 |
Demo + Planning |
7 |
2021 12 08–09 |
Demo + Planning |
8 |
2021 12 15–16 |
Project Evaluation Session |
9 |
2022 01 05 |
Demo + Planning (Project Wrap-Up Preparations) |
10 |
2022 01 12–13 |
Final Project Presentation + Demo + Individual Interviews |
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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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Tutoring Sessions |
Tutoring sessions in this course offering will be conducted by Zeynab (Artemis) Mohseni.
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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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