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Course Catalog 2014-2015
MAT-82306 Scientific Visualization, 4 cr |
Additional information
Lectured every second calendar year (during academic years starting with an even number). Implementation announcements and news are available at the teaching home page of the TUT Department of Mathematics, Intelligent Information Systems Laboratory.
Suitable for postgraduate studies
Person responsible
Ossi Nykänen
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Learning Outcomes
Scientific visualization is about rigorously and semi-automatically processing and projecting data in terms of (usually visual) representations with analysis or application utility. In this course, we familiarize ourselves with the basic concepts, techniques, and applications related to scientific visualization. The methodological foundation of the course is established by the analysis of various representation data structures and visualization techniques/metaphors, mainly relying onto tools of scientific computation and (webized) viewer applications. The main objectives of the course are to get an overview of the methods of the domain, and to get hands-on experience on implementing simple applications.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Methodological foundation: Visualization use cases and success criteria; Pipeline architecture; Data quality and understandability. | Design stances (e.g. hypothesis formulation vs. visual analytics vs. exploration) | |
| 2. | Basic techniques: Data structures and representation systems (2D, 3D, 3+D, images, volume, information); Colors, time and interaction; Visualization metaphors and common applications. | "Non-scientific" applications (e.g. design visualization); Feedback mechanisms. | |
| 3. | Applications: Dataset, pipeline, and view management; Basic tools (matlab, scripting, processors, common tools and viewers); Assignments on various topics. | General-purpose data processing pipelines and visualization systems. |
Instructions for students on how to achieve the learning outcomes
The grade is based on the assignments and the final exam.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Additional information about prerequisites
Hands-on programming/scripting skills are recommended. Basic understanding on the rudimentary (numerical) scientific computation methods is helpful, but strictly speaking not needed in every application.
Prerequisite relations (Requires logging in to POP)
Correspondence of content
There is no equivalence with any other courses
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |