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
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
---|---|---|---|---|---|---|---|
0105 Written and oral assignments 7.5 c Grading: TH | 7.5 c |
In programmes
- MPENM - Engineering Mathematics and Computational Science, Year 1 (compulsory elective)
- MPENM - Engineering Mathematics and Computational Science, Year 2 (elective)
- TKITE - Software Engineering, Year 2 (elective)
- TKITE - Software Engineering, Year 3 (elective)
Examiner
- Sviatlana Shashkova
- Biträdande universitetslektor, Institution of physics at Gothenburg University
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 slidesWilke, Claus O. Fundamentals of data visualization: a primer on making informative and compelling figures. OReilly 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.