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Module description - Visual Analytics

ECTS 2.0
Level Intermediate
Content Visual analytics can be described as an advanced software environment, in which often multiple visualizations are linked with each other, as well as with computational tools and processes. The term visual analytics was coined in 2004, and the emphasis in the domain has been on combining strengths of humans (e.g., cognitive processes such as qualitative reasoning, creativity, interpretation of facts for decision making) with machines with the goal to generate insights. The key difference between visual analytics and (interactive) visualizations is the significant amount synergy assumed between computational (e.g., statistics, machine learning) and cognitive (e.g., human ability to recognize patterns, form hypothesis, follow qualitative reasoning) paradigms in visual analytics. Such synergy is not necessarily expected in visualization alone. In the last decade, visual analytics has become a standard in practice in many domains ranging from scientific visualization to environmental/geographic or business information systems to understand large and complex datasets.
This course covers the important concepts in visual analytics and its fundamentals as a scholarly subject, reviews underlying computational solutions and examines its use cases a practical tool to better understand data. Students will also have the opportunity to explore latest research topics in visual analytics.
Learning outcomes Algorithms
Students understand common computational solutions, e.g., algorithms behind advanced multiple-linked visualizations, and examine the practical software and visualization design decisions.

Cognition and computation
Students know the important interdisciplinary concepts in visual analytics from cognitive and computer science perspectives. They understand the human factors that motivate software choices, and the overall merits and limitation of what visual analytics can offer.

The students have an overview of the technology used creating visual analytics software environments, understand the technical challenges, and examine the. features included in current software.

Students are able to choose a method to evaluate their solutions. They understand the tradeoffs of their designs and can explain how their solution best supports finding the type of insights that are useful in the given context.
Evaluation Mark
Built on the following competences Foundation in Data Visualisation (gdv)

    Interactive Visualisation (ivi)

Modultype Portfolio Module
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