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What is data visualisation?

Data visualisation is a way to communicate data through clear, interpretable diagrams and charts, which convert raw data into readable forms.

Data visualisation is a way to communicate data through clear, interpretable diagrams and charts. To make this happen, you convert raw data into readable forms.

There are two ways to use visualisations:

  1. to explain our data to a layperson
  2. to allow us to understand our data better.

We call these two different outputs explanatory visualisations and exploratory visualisations respectively.

With an explanatory visualisation, we are not only presenting the data but also drawing attention to its important parts, and sometimes even providing a narrative along with it. We also have the opportunity to present the raw data in a different layout that is analogous to its real-world counterpart.

In a nutshell, explanatory visualisation helps in communicating specific data and explain the story through the data to the audience.

Ethical visualisation

As data analysts, we must always choose the most ethical way to perform and present our research. Though, sometimes we may be asked to highlight data in a biased way. This may sound like it’s unethical, but it usually means highlighting the differences you want to compare.

For example:

Let’s say you are performing data analysis for an Internet Service Provider (ISP) called Our ISP. You want to show a comparison of speed in Mbps with other ISPs, your competitors. In this case, your main competition is Big ISP and you may choose to display your download speeds in a chart like this:

Graphic shows a bar chart.Heading: Average download speed. Y-axis from bottom to the top: 0 10 20 30 40 50 60 70 80 90 100. X-axis from left to right has five bars: Our ISP, ISP B, ISP C, Big ISP and ISP E. The Our ISP bar goes all the way up to 100. The ISP B bar goes all the way up to around 95. The ISP C bar goes all the way up to around 55. The Big ISP bar goes all the way up to around 20. The ISP E bar goes all the way up to around 5.

While this chart is not hiding any information, the message you end up conveying is that Our ISP is much faster (and better) than Big ISP.

We’ll look at some more examples of good (bad and ugly) data visualisations in the subsequent steps. This means knowing which data to include, which to exclude, and how to draw attention to the right parts, as well as demonstrate its relevance.

Over to you

Looking back in your professional experience, do you have any instances where you (or someone you know) unconsciously might have made a bias in presenting your data visually?

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Data Visualisation with Python: Matplotlib and Visual Analysis

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