Course syllabus for Scientific visualization

Course syllabus adopted 2025-02-22 by Head of Programme (or corresponding).

Overview

  • Swedish nameVetenskaplig visualisering
  • CodeMVE080
  • Credits7.5 Credits
  • OwnerMPENM
  • Education cycleFirst-cycle
  • Main field of studyMathematics
  • DepartmentMATHEMATICAL SCIENCES
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 20153
  • Maximum participants45 (at least 10% of the seats are reserved for exchange students)
  • Open for exchange studentsYes

Credit distribution

0105 Written and oral assignments 7.5 c
Grading: TH		 7.5 c

In programmes

Examiner

Eligibility

General entry requirements for bachelor's level (first cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

The same as for the programme that owns the course.
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

A basic course in programming.

Aim

The aim of this course is to provide an overview of the tools and techniques of scientific visualization.

Learning outcomes (after completion of the course the student should be able to)

* think in visualization terms
* produce clarifying graphics in a number of common situations
* present graphics to convey a message

Content

In this course you will learn about different concepts, techniques and tools for visualizing scientific data in 2D. The course covers the basics of effective communication via graphics, data exploration and explanation. During the course you will learn how to produce images with Python/R.

Organisation

Lectures and computer labs. The labs, which form an essential part of the course, consist of several tasks where the student solves various visualisation problems. 

Literature

Lecture slides

Wilke, Claus O. Fundamentals of data visualization: a primer on making informative and compelling figures. O’Reilly Media, 2019. https://clauswilke.com/dataviz/

Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2-book.org/.

Franconeri, Steven L., et al. ”The science of visual data communication: What works.” Psychological Science in the public interest 22.3 (2021): 110-161. https://journals.sagepub.com/doi/full/10.1177/15291006211051956

Examination including compulsory elements

Compulsory laboratory work, project and take-home exam.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.