MVE080 / MMG640 Scientific visualization Autumn 25

Course PM

MVE080/MMG640 course in scientific visualisation.

Contact details:

Sviatlana Shashkova sviatlana.shashkova@physics.gu.se
Linde Viaene linde.viaene@physics.gu.se

Student Representatives:

Miner Chen              minerchenida@outlook.com          
Felix Dahlin              felixdahlin@outlook.com   
Josefine Karlsson     josefiine_karlsson@hotmail.com    

Program

In this course you will learn about various concepts, techniques, and tools for visualisation of scientific data in two dimensions. The GU course code is MMG640. The Chalmers course code is MVE080. The schedule of the course is in TimeEdit - just search for the course code. Monday lectures will be given in the Euler lecture room; Wednesday lecture will take place in the Pascal lecture room, computer labs -- in the computer rooms MV:F23-25.

The course is based on the online book Fundamentals of Data Visualization by Claus O. Wilke, the on-line ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham and a review article The science of visual data communication: What works by Franconeri et al. We will use the grammar of graphics Python library plotnine, which is based on ggplot2. Hence, for help via searching engines, you can look for both ggplot and plotnine.

After each lecture, the slides, code used to produce some visuals, and relevant datasets will be uploaded.

Weekly homework is performed in groups of 4 people max and should be completed within two weeks. You can have two attempts. In this case, the first attempt should be submitted within a week from the date when the homework becomes available. In case of any unforeseen life circumstances, each group can ask for a deadline extension. This option is only available once during the course. The final version of the 4th homework should be submitted by December 10, 23:59.

Preliminary course outline (modified throughout the course):

*Computer lab: each Wednesday 10.00 - 11.45
It is not allowed to use ChatGPT or any other AI tool to directly answer any of the questions. Large language models can be excellent tools, e.g. for editing text, but using them to directly answer a question is bad practice, as i) you do not learn anything, and ii) these models are often incorrect - and confidently reporting an incorrect answer is unprofessional. Any direct usage of AI to answer questions will be considered cheating and reported. 

 

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