Course PM

This page contains the program of the course. Other information, such as learning outcomes, teachers, literature and examination, are in a separate course PM. In addition to Canvas, we shall use CampusWire for Q&A as a way for both students and teachers to ask and reply on questions that come up. During the introductory meeting we will send out an invitation link to all students to participate in the CampusWire virtual classroom.

Program

In this course you will learn about various concepts, techniques, and tools for visualization of scientific data in both 2 and 3 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. There is a seamless integration between lectures and computer labs. Therefore, all teaching will take place in the computer rooms MV:F24-25.

Preliminary course outline (modified throughout the course):

PART 1: 2D VISUALIZATIONS - Lecturer: Sebastian Persson
This part is based on the on-line book Fundamentals of Data Visualization by Claus O. Wilke, the on-line ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham and a recent 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 when googling for help you can search for both ggplot and plotnine.

After each lecture I will upload the slides, code used to produce the visuals, and relevant datasets.

• Monday 2023-11-04  13:15 - 15:00 : Course introduction ()
  Relevant links:
  Getting started with the CampusWire learning platform
  Python basics tutorial
  NumPy for MATLAB users
  The Anaconda Python distribution - I recommend you install Anaconda for Python3.11. For plotnine to work you must use Python 3.11.
  Jupyter Notebook basics
  Download VS Code - A good editor with support for notebooks
  The correct environment (Python version and package version) can be recreated from this yml-file: MVE080.yml. If you have correctly installed Jupyter Notebooks and plotnine you will be able to run the following notebook :  Hello_MVE080-1.ipynb
  Slides Lecture 1 : Lecture1.pdf
  Code for producing the visuals in lecture 1 : Slides_l1.ipynb
• Wednesday 2022-11-01 08:00-10:00 : Visualizing amounts and distributions ()
  Operating system dataset : mobile_os_market_share.csv 
  Median lifespan data : Life_tidy.csv 
  Weather data for Gothenburg : Weather_tidy-1.csv 
  The non summary winter dataset : Winter_vås.csv 
  Weather data for Västerås : Winter_vås_summary.csv 
  Data on Schizophrenics born : Aus_schz.csv 
  Slides lecture 2 : Lecture2.pdf
  Code for the visuals : Lecture2-1.ipynb
• Monday 2023-11-06 13:15-15:00 : Colors  and visualizing proportions ()
  Dataset used to compare colormaps : Ex_color_maps-1.csv 
  Circles dataset : Circles.csv
  Data on life-happiness for 2018 : 2018.csv 
  Data with Simpsons's paradox (testing categorical colors) : Ex_colors.csv 
  GDP data : GDP_tidy.csv 
  Swedish education data : Education_plot_format.csv 
  Titanic data : Titanic.csv 
  Spain employment data : Span_unemployment.csv 
  Data for using rainbow desaturated palette : Rainbow_desaturated.csv (in the notebook there is code for reading and using this palette)
  Slides for lecture 3 : Lecture3_MVE080.pdf
  Code for lecture 3 : Lecture3-1.ipynb
• Wednesday 2023-11-08 10:00-11:45! : Visualizing associations and time series ()
  Spain employment data : Spain_tidy.csv 
  Inflation data : Inflation.csv 
  Sweden population data : Sweden_pop.csv 
  France population data : France_pop.csv 
  Bacterial growth data : Growth_rate.csv 
  Apartment prices data : Housing_sweden.csv 
  World happiness data : World_2018.csv 
  Salary data : Salary_sweden.csv 
  Lifespan data for horizon plot : Life_tidy-1.csv 
  R-code for horizon plot : Horizon_plot.R 
  Slides for lecture 4 : Lecture4_MVE080.pdf
  Code for lecture 4 : Lecture4-1.ipynb
• Monday 2023-11-13 13:15-15:00 : Visualizing uncertainty ()
  Chocolate datasets : Chocolate_tidy-1.csv and Chocolate_sum-1.csv
  Curve fit data : Traces_fit-1.csv, Sum_curve_fit-1.csv, Sum_curve_fit-2.csv
  Probability : Plot_uncertainity-1.csv
  Code for lecture 5 : Lecture_uncertainity.ipynb
  Slides for lecture 5 : Lecture5_MVE080.pdf
• Wednesday 2023-11-15 10:00-11:45! : Storytelling with data ()
  Slides for lecture 6 : Storytelling.pdf
  Code for lecture 6 : Storytelling.ipynb
• Monday 2023-11-20 13:15-15:00 : Visualizing geospatial data and the truthful art () 
  Forest dataset : Forest_tidy.csv
  Sweden map region Kommun_Sweref99TM_region.zip and municipal Kommun_Sweref99TM_region.zip
  Sweden population data : Swe_pop_län_2021.csv and Swe_pop_2021.csv
  USA map : cb_2018_us_state_500k.zip
  Starbucks data : Startbucks_location.csv
  Code for Geospatial data lecture : Geospatial_data.ipynb
  Slides for Geospatial data : Lecture_maps.pdf
  Internet is dead data : Internet_dead-1.csv
  Swedish election data : Election_2018_lan_sum-1.csv
  Code for the truthful art : Lecture6-1.ipynb
  Slides for the truthful art : Truthful_art.pdf
• Wednesday 2023-11-22 08:00-12:00 : Computer lab

PART 2: 3D GRAPHICS AND ANIMATION
This part is based on the combination of Blender 3D and Python for scripting.

• Basic math of 3D graphics (slides)
• Blender 3D and Python scripting (slides)
  Download Blender 3D
  Blender manual pages
  Blender Fundamentals Video Tutorials
  Cheat sheet
  Blender Guru Video Tutorial
  Python scripting
• Rotations in 3D: quaternions (slides)
  Notes on quaternions
• Course project (link)
• Visualization of particle dynamics using Blender (slides)
  PDF notes about Blender particles

Back to the top