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Introduction to Data Visualisation, with Python

DOI

This course is designed to introduce concepts relating to good data visualisation, utilising packages available for Python.

This course should be useful for anyone who works with data and needs to visualise results. While the term “scientific data visualisation” is used in this documentation due to the sources we have collated, the subject matter and example datasets should be applicable to wider research areas than STEM.

This course focuses on the production of static visualisations, and not interactive dashboards.

Objectives

This course should help you to:

  • Become familiar with best practice with regards to scientific data visualisation
  • Build a toolkit of resources to help you create objective, informative plots
  • Use Python and Python libraries such as Matplotlib and Seaborn to create aesthetically pleasing graphics
  • Be aware of some common issues when it comes to data visualisation

This course uses Python as a tool to create graphics. We use packages such as Numpy, Pandas, Matplotlib, and Seaborn in the interactive coding sessions to collaboratively build different example plots, looking at the entire workflow step by step, including downloading and manipulating the starting data into useable formats. We use a free Google-hosted equivalent of a Jupyter Notebook, called Google Colab to avoid compatibility issues with people using different machines; however we strongly recommend working locally on your machine with actual research data to avoid issues with regards to data security. While this course is an introduction to data visualisation in Python, it is expected that you have some basic experience with Python and understand the data types such as lists, numpy arrays, dictionaries, and have some basic experience with the packages numpy and pandas.

Objectives

To take this course, please ensure:

  • That you have some basic Python experience, either through taking our SWD1a course, or self-taught

  • That you have a Google account to use Google Colab

See our old course notes here