Description
COURSE CONTENT:
Note: this is not an introduction to Python, and basic Python skills are
a prerequisite.
Good data visualisation is of key importance in research for initially
exploring and analysing your data, and later for communicating your
results to various audiences; however, in many disciplines, the basics
of good visualisation are not taught. In this course, we use the
programming language Python as a tool to introduce you to some key
concepts of good data visualisation to support your research. We will
create a selection of common plot types for different data types using
popular Python libraries and open datasets.
In this one-day course, we will discuss:
- Ten rules for good scientific data visualisation;
- Tailoring your graphics to your audience, presentation medium, and
message, including creating publication-quality figures;
- Improving accessibility of your visualisations;
- Avoiding misleading visualisations and unintentional
communication;
- Delving into Python documentation
- Using external visualisation libraries such as Matplotlib and
Seaborn;
- Writing modular and reusable visualisation scripts.
Prerequisites:
- Basic experience in Python (e.g. have attended SWD1a or are
comfortable with the topics covered in the course:
https://arctraining.github.io/python-novice-inflammation/ ).
Suitability:
Research postgraduate students and above from all research domains with
some Python experience. Please see the prerequisite section for an
indication of required Python knowledge.
DURATION: 1-day in person cluster-based workshop.
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