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| Detailed information |
| Original study plan |
Master's programme Computer Science 2025W |
| Learning Outcomes |
Competences |
| Students have a comprehensive and practical understanding of data visualization, including all its aspects in science, math, and technology. They are trained in critical thinking to judge the many analytical, practical, and design decisions involved in this activity. Students have obtained the conceptual, theoretical, and practical capabilities to master this multidisciplinary pursuit.
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| Skills |
Knowledge |
- Apply a practical data visualization design workflow to take on any data visualization challenge (k3, k6)
- Interpret different chart types and understand what insights can be derived from them (k2)
- Judge the appropriate analytical and design decisions required for different contextual circumstances (k5)
- Create elegant and informative data visualizations that help you understand data and communicate findings (k3, k6)
- Evaluate and decide in which situations you need to use state-of-the-art data visualization systems and libraries (k5)
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- Familiarity with a range of contemporary data visualization techniques
- Understanding the theories of visual perception and their relevance to data visualization
- Understanding the main principles of good visualization design
- Enhanced data, statistical, and graphical literacy
- Acquired a more sophisticated language for defining, describing, and evaluating visualization designs
- Practical understanding of relevant design concepts such as color theory and user interface design
- Refined instincts of an effective analyst
- Familiarity with state-of-the-art visualization tools and libraries
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| Criteria for evaluation |
Written exam
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| Methods |
Slides combined with case studies and in-class exercises
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| Language |
English |
| Study material |
Study material provided during the course
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| Changing subject? |
No |
| Further information |
In addition to the lecture, students can take an optional practical lab to learn how to apply visualization theory to real-world visual data analysis problems.
The course was formerly known as Visual Analytics.
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| Earlier variants |
They also cover the requirements of the curriculum (from - to)
921DASIVIAV17: VL Visual Analytics (2017W-2024S)
921CGELVIAV13: VL Visual Analytics (2013W-2017S)
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