Syllabus for Scientific Visualisation

Vetenskaplig visualisering

A revised version of the syllabus is available.

Syllabus

  • 7.5 credits
  • Course code: 1MD140
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1N, Image Analysis and Machine Learning A1N, Computational Science A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2021-03-04
  • Established by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2021
  • Entry requirements:

    120 credits including a basic course in programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Department of Information Technology

Learning outcomes

On completion of the course, the student should be able to:

  • describe the data flow in a visualisation system;
  • outline the methods that transform the data and information to visual representations;
  • use and program advanced software for various visualisation techniques;
  • evaluate computer-generated visualisations by drawing upon principles and theories about the human visual system;
  • select appropriate visualisation strategies and justify the chosen approaches.

Content

The visualisation pipeline. Data representations and scalar visualisation. Vector and tensor visualisation. Multidimensional visualisation. Stereo Rendering. Perceptual issues in visualisation. Information Visualisation. Rendering techniques for visualisation such as volume rendering, splatting and isosurface generation.

The course includes projects using software for advanced visualisations.

Instruction

Lectures, computer exercises and project work.

Assessment

Written test (3 credits), compulsory assignments (2.5 credits), project (2 credits).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Other directives

The course cannot be included in the same degree as 1TD389 Scientific Visualisation.

Syllabus Revisions

Reading list

Reading list

Applies from: Autumn 2021

Some titles may be available electronically through the University library.

  • Telea, Alexandru Data visualization : principles and practice

    2015

    Find in the library

    Mandatory

  • Ware, Colin Visual thinking for design

    Burlington, Mass.: Morgan Kaufmann, c2008

    Find in the library

    Mandatory

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