Scientific Visualization

Faculteit Science and Engineering
Jaar 2022/23
Vakcode WMCS018-05
Vaknaam Scientific Visualization
Niveau(s) master
Voertaal Engels
Periode semester I a
ECTS 5
Rooster rooster.rug.nl

Uitgebreide vaknaam Scientific Visualization
Leerdoelen At the end of the course, the student is able to:
1) understand the basic principles and fundamental methods of scientific visualization and the connection to related fields.
2) implement scientific visualization techniques in a small team.
3) report about the implementations, in terms of algorithms developed, experimental results, and critical analysis.
Omschrijving The aim of this course is to introduce students to the theory and practice of data visualization. After following this course, the students should have a good understanding of scientific data representation (sampling, reconstruction, interpolation, representation in computer models), the structure and operation of the data visualization pipeline, and both theoretical and implementation-level knowledge of the most frequently used algorithms for scalar, vector, and tensor data visualization. They should be able to select the appropriate algorithms, and algorithm settings, for solving a concrete scientific visualization problem for a given application domain and data source. On a practical side, the students should be capable of designing and efficiently implementing the above-mentioned algorithms in a major programming language. They should be able to explain the characteristics of the different algorithms for concrete use-cases, and support their explanations with both theoretical and practical arguments.
Uren per week
Onderwijsvorm Hoorcollege (LC), Practisch werk (PRC)
(All lectures and lab sessions are mandatory.)
Toetsvorm Practisch werk (PR), Schriftelijk tentamen (WE)
(The final grade F for this course is obtained as follows. Let P = mark practicals, E = mark written exam. If E<5 then F=E else F= (E+P)/2. Final grades are rounded to half integers, except for final grades between 5 and 6, which are rounded to integers.To pass the course, a final grade of at least 6 is required.)
Vaksoort master
Coördinator Dr. S.D. Frey
Docent(en) Dr. S.D. Frey
Verplichte literatuur
Titel Auteur ISBN Prijs
Software examples at
http://www.cs.rug.nl/svcg/DataVisualizationBook
A.C. Telea
Data Visualization - Principles and Practice, 2nd edition; CRC Press; year: 2014 A.C. Telea 9781466585263 €  60,00
Entreevoorwaarden Assumed prior knowledge/ skills:
Linear algebra
Calculus
Computer graphics
General-purpose programming (C++, Python)
Opmerkingen This course has limited enrollment:
- CS students can always enter the course, regardless of whether the course is mandatory for them or not.
- The number of enrolments for other non-CS students is limited. These students need to meet the course prerequisite requirements as mentioned on Ocasys. Priority is given to students for which the course is an official elective (see list below).
- An exception can be made for exchange students, if they have a CS background: please contact the FSE International Office. See here for more info about the enrollment procedure.
Opgenomen in
Opleiding Jaar Periode Type
MSc Artificial Intelligence  (C - Elective Course Units) - semester I a keuze
MSc Astronomy: Quantum Universe  (Optional Courses in Data Science (DS)) - semester I a keuze
MSc Astronomy: Quantum Universe  (Optional Courses in Instrumentation & Informatics (I&I)) - semester I a keuze
MSc Computational Cognitive Science  (C - Elective Course Units) - semester I a keuze
MSc Computing Science: Data Science and Systems Complexity  (Compulsory course units) 2 semester I a verplicht
MSc Computing Science: Intelligent Systems and Visual Computing  (Compulsory course units) 1 semester I a verplicht
MSc Computing Science: Science Business and Policy  (Elective course units) 1 semester I a keuze
MSc Computing Science: Software Engineering and Distributed Systems  (Guided choice course units) - semester I a keuze
MSc Courses for Exchange Students: AI - Computing Science - Mathematics - semester I a
MSc Mathematics: Statistics and Big Data  (MSc Mathematics: Statistics and Big Data) - semester I a keuze
MSc Mechanical Engineering: Advanced Instrumentation  (Electives ) 1 semester I a keuze
MSc Mechanical Engineering: Smart Factories  (General track electives (not specialisation related)) 1 semester I a keuze