Introduction to Scientific Visualisation
This course is about creating data visualisations for scientific publications. The student learns to visualise data in an effective way using principles of graphic design adapted to the scientific environment. The way of demonstrating principles and theoretical concepts is technology agnostic, yet exercises will be conducted mostly in either R or Python. Therefore, the student leans effective time-proven techniques for data visualisation that can be applied to any visualisation environment.
Our topics include:
- Basic visualisation types, what are their strengths and weaknesses and how to create them
- Analytical thinking of the visualisation process
- Publication process and in which way to consider the full pipeline from data to paper
- Principles of data symbols, pictograms, lines and other stylizing features
- Maps, contours, choropleths, projections and other map-like visualisations
- 2D data, microscopy data, photographs, micrographs
- Visualisation of neuroscience data and anatomical data
Learning methods:
- Lectures every two weeks with a new concept each time
- Contact teaching sessions every second week (alternating with lectures) deepening the understanding of the topic of the lectur
- Exercises that probe your understanding of the topic as well as advancing your visualisation skills with R and Python
- Exercise sessions with two demonstrations each time - this is where you come to learn the practical skills of data visualisation
- Reflection of your learning experience through a journal
- Project work on a demanding topic done as a group exercise
- Home examination which leads to a final report concludes the course
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