Syllabus for Scientific Visualisation

Vetenskaplig visualisering

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:
  • Revised: 2022-02-04
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2022
  • 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:

  • characterize the nature of datasets and the properties of the visual display medium,
  • describe the data- and process-flow to transform data and information into visual representations,
  • explain visualisation methods and techniques for graphical representation of the most common types of data,
  • use and program interactive software for various visualisation techniques,
  • assess computer-generated visualisations by drawing upon design principles and theories about the human visual system,
  • identify appropriate visualisation strategies and justify the chosen approaches.

Content

Classification of data and properties of visual displays. The visualisation process pipeline including classical algorithms for rendering. Visualisation techniques for discrete and continuous data (scalar, vector, tensor fields) in 1D, 2D, and 3D spatial domains. Visualisation techniques for independent quantitative observations (amounts, proportions, frequencies, associations), data in multiple categories, and relations. Relevant aspects of human visual perception and cognition. Evaluation approaches in visualisation.

The course includes projects using software for advanced visualisations.

Instruction

Lectures, computer exercises and project work.

Assessment

Written test (3 credits). 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 2022

Some titles may be available electronically through the University library.

  • Telea, Alexandru Data visualization : principles and practice

    2015

    Find in the library

    Mandatory

  • Ward, Matthew; Grinstein, Georges; Keim, Daniel. Interactive data visualization : foundations, techniques, and applications

    Second edition.: Boca Raton: CRC Press, Taylor & Francis Group, [2015]

    Find in the library

  • Wilke, Claus O. Fundamentals of data visualization : a primer on making informative and compelling figures

    First edition.: Sebastopol, CA: O'Reilly Media, 2019

    Find in the library

  • Munzner, Tamara Visualization analysis and design

    Boca Raton: CRC Press, xop. 2015.

    Find in the library

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