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About course

DD2257 Visualization 7.5 credits

Administer About course

The focus of this course is on discussing efficient techniques to visually represent large-scale data sets from simulation and measurement. We will discuss the visualization pipeline, data structures, mapping techniques and special rendering techniques for data from different application domains such as fluid dynamics, climate research, medicine or biology. Various examples will be given to outline the benefits of visualization techniques in practical applications.

Choose semester and course offering

Choose semester and course offering to see information from the correct course syllabus and course offering.

Headings with content from the Course syllabus DD2257 (Autumn 2021–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Data structures and algorithms for visualisation of spatio-temporal data sets. Topological data analysis. Feature based methods. Colour. Perception. Fundamental elements of visualization. Software tools for visualization.

Intended learning outcomes

After completing the course with a passing grade the student should be able to:
• name concepts and algorithms in visualization and relate them to one another
• describe the basics of visualization algorithms and concepts
• identify and characterise results of selected visualization algorithms
• apply visualization algorithms to small data sets.

Course disposition

No information inserted

Literature and preparations

Specific prerequisites

For non-program students:

SF1604 Linear Algebra, SF1625 One variable calculus, SF1626 Multivariable analysis, DD1337 Programming, DD1338 Algorithms and Data Structures, DH2320 Introduction to Visualisation and Computer Graphics.

Recommended prerequisites

The course DH2320 "Introduction to Visualization and Computer Graphics" is recommended.

Equipment

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Literature

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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F

Examination

  • LAB1 - Laboratory Assignments, 3.5 credits, grading scale: P, F
  • TEN1 - Examination, 4.0 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Profile picture Tino Weinkauf

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web DD2257

Offered by

EECS/Human Centered Technology

Main field of study

Mathematics

Education cycle

Second cycle

Add-on studies

Please discuss with the instructor.

Contact

Tino Weinkauf, weinkauf@kth.se

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex