Data Visualisation

Data visualisation is a great way to quickly see what is going on in the data. There are many different ways to visualise data, but some basic principles that always apply are: - Is the graph easily readable? - Is this the appropriate graph for the type of data / information that I am trying to display? - Is the graph accurately representing the data? - What story am I trying to tell? Is the story clear from the graph? - Have I given the reader all the information they need to interpret this graph at a glance? Are the axes labeled, and if relevant, is there a legend and title?

This webiste shows some of the most frequently used types of graphs, and what data they are most suitable for. Below, we give a quick summary:

  • Show the distribution of a single continuous variable: histograms
  • Show the number of observations in a category: donut plots, tree plots, bar graphs
  • Show averages or counts by category: bar graph
  • Show average and distribution by category: box plot
  • Show the relationship between two variables: scatterplots, line graphs
  • Show changes over time: line graph (or a bubble chart if you have several variables)

Tutorials

To get you introduced to data visualisation, complete the following tutorial(s):

Compulsory: Go through the DataCamp course Introduction to Data Visualisation with Python.

Optional: Complete Kaggle’s Data Visualisation: From Non-Coder to Coder.

Advanced: Making interactive graphs with Plotly Here is a great walk-through of different types of plots in Plotly with Cufflinks.

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