| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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No sequentiality
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| Degree programme | | Study hours | Credits | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | Master Computer Science profile Engineering Interactive Systems | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical |  |
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| | | Learning outcomes |
- EC
| EC 3: A graduate of the Master of Computer Science programme has the necessary knowledge and insights in at least one subdiscipline which allow to contribute to the development and the application of innovative ideas in a certain area of computer science (by deepening basic knowledge at bachelor level, including that of mathematical and other scientific foundations). | - EC
| EC 5: A graduate of the Master of Computer Science programme is able to independently model a complex problem in computer science, to introduce the necessary abstractions, to describe and to implement the solution in a structured manner, and, finally, to discuss with the stakeholders why the chosen solution and the corresponding implementation meet with the specifications. | - EC
| EC 6: A graduate of the Master of Computer Science programme is able to independently situate a scientific problem, analyse and evaluate it, to formulate a research question and propose a solution for this in a scientifically substantiated manner. | - EC
| EC 7: A graduate of the Master of Computer Science programme is able to analyse and evaluate information in a critical manner and to process this information efficiently. | - EC
| EC 8: A graduate of the Master of Computer Science programme is able to communicate information, ideas and solutions to an audience of fellow computer scientists and to non-specialists by expressing him or herself on the proper level of abstraction. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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Within the course information visualization we study visualization principles and techniques that help users to better understand and analyze data. In addition to learning a healthy critical view of existing visualizations, the participants of this course are also trained to design and implement appropriate visualizations for complex and multidimensional data sets.
In this course we give an overview of common visualisations and visualization techniques, ranging from so-called "flatland" visualizations (static visualizations that can also occur on paper) to interactive visualizations of multidimensional data. We introduce the participants to different toolkits and possibilities to build visualisations ourselves and get to work with them. In the lectures of the course, the following topics are covered: Design principles, Tufte's graphical excellence, visualization integrity and data-ink theory, perception & color theory (Gibson's affordance theory, perceptual processing, environmental factors, light, brightness, contrast, constancy), visualization of graphs and connectivity, focus + context visualizations, interaction, and both generic and specific visualizations of complex, often multi-dimensional, data. Furthermore, students work in groups on visualizations that, based on available datasets, can lead to innovative insights in a specific domain.
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Lecture ✔
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Project ✔
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Small group session ✔
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Period 2 Credits 6,00
| Evaluation method | |
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| Other evaluation method during teaching period | 50 % |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Compulsory course material |
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Copies of articles.
Course material is distributed during the class or made available via the web |
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| Recommended reading |
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- Information Visualization: Perception for Design,Colin Ware,3,Morgan Kaufmann,9780123814647,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=892223&pq-origsite=summon
- Information Visualization: Design for Interaction,Robert Spence,2,Pearson,9780132065504
- Information Graphics: A Comprehensive Illustrated Reference,Robert L. Harris,Oxford University Press,9780195135329,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=4703026&pq-origsite=summon
- The Visual Display of Quantitative Information,Edward R. Tufte,2,Graphics Press,9780961392147
- Envisioning Information,Edward R. Tufte,1,Graphics Press,9781930824140
- Readings in Information Visualization: Using Vision to Think,Stuart K. Card; Jock Mackinlay; Ben Shneiderman,1,Morgan Kaufmann,9781558605336
- Visualization Analysis & Design,Tamara Munzner,1
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| Exchange Programme Computer Science | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
| Master of Computer Science choice | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| EC 3: A graduate of the Master of Computer Science programme has the necessary knowledge and insights in at least one subdiscipline which allow to contribute to the development and the application of innovative ideas in a certain area of computer science (by deepening basic knowledge at bachelor level, including that of mathematical and other scientific foundations). | - EC
| EC 5: A graduate of the Master of Computer Science programme is able to independently model a complex problem in computer science, to introduce the necessary abstractions, to describe and to implement the solution in a structured manner, and, finally, to discuss with the stakeholders why the chosen solution and the corresponding implementation meet with the specifications. | - EC
| EC 6: A graduate of the Master of Computer Science programme is able to independently situate a scientific problem, analyse and evaluate it, to formulate a research question and propose a solution for this in a scientifically substantiated manner. | - EC
| EC 7: A graduate of the Master of Computer Science programme is able to analyse and evaluate information in a critical manner and to process this information efficiently. | - EC
| EC 8: A graduate of the Master of Computer Science programme is able to communicate information, ideas and solutions to an audience of fellow computer scientists and to non-specialists by expressing him or herself on the proper level of abstraction. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
Within the course information visualization we study visualization principles and techniques that help users to better understand and analyze data. In addition to learning a healthy critical view of existing visualizations, the participants of this course are also trained to design and implement appropriate visualizations for complex and multidimensional data sets.
In this course we give an overview of common visualisations and visualization techniques, ranging from so-called "flatland" visualizations (static visualizations that can also occur on paper) to interactive visualizations of multidimensional data. We introduce the participants to different toolkits and possibilities to build visualisations ourselves and get to work with them. In the lectures of the course, the following topics are covered: Design principles, Tufte's graphical excellence, visualization integrity and data-ink theory, perception & color theory (Gibson's affordance theory, perceptual processing, environmental factors, light, brightness, contrast, constancy), visualization of graphs and connectivity, focus + context visualizations, interaction, and both generic and specific visualizations of complex, often multi-dimensional, data. Furthermore, students work in groups on visualizations that, based on available datasets, can lead to innovative insights in a specific domain.
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 2 Credits 6,00
| Evaluation method | |
|
| Other evaluation method during teaching period | 50 % |
|
|
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Compulsory course material |
| |
Copies of articles.
Course material is distributed during the class or made available via the web |
|
|
| Recommended reading |
| |
- Information Visualization: Perception for Design,Colin Ware,3,Morgan Kaufmann,9780123814647,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=892223&pq-origsite=summon
- Information Visualization: Design for Interaction,Robert Spence,2,Pearson,9780132065504
- Information Graphics: A Comprehensive Illustrated Reference,Robert L. Harris,Oxford University Press,9780195135329,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=4703026&pq-origsite=summon
- The Visual Display of Quantitative Information,Edward R. Tufte,2,Graphics Press,9780961392147
- Envisioning Information,Edward R. Tufte,1,Graphics Press,9781930824140
- Readings in Information Visualization: Using Vision to Think,Stuart K. Card; Jock Mackinlay; Ben Shneiderman,1,Morgan Kaufmann,9781558605336
- Visualization Analysis & Design,Tamara Munzner,1
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1 examination regulations art.1.3, section 4. |
| 2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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