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Why is data visualisation valuable?

Video and article dicussing the value of data visualisation.
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(electronic intro music) One, eight, 27, 64, 125.
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What do these numbers represent? When I place these numbers like this, it’s hard to tell what they represent or if they’re important. These numbers could be the numbers obtained from analysing an electrical circuit, but you wouldn’t be able to make a conclusion about your experiment clearly, without visually representing this data. Graphing data is especially important in a business context, as it allows companies with very little time to make decisive data-driven decisions on key decisions within the company. Python provides an abundance of graphing tools with capabilities only improving from here. Over 10 years ago, the only graphing capability was Matplotlib, which worked fairly well, but didn’t create graphs that were visually appealing.
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More recently modules such as Seaborn have been developed that create beautiful graphs that can represent data in a compact and concise manner. Having a robust set of tools that are widely adaptable is an important part of any career these days. Mastering the use of Python to create data representations will be an immensely useful skill to you now and in the future.

The video explored some key examples and comparisons that demonstrate the importance of data visualisations. Data visualisation facilitates meaningful and effective data conversations. Therefore, as a skill, data visualisation is a ‘must-have’ for a data analyst.

Data visualisation is the process of communicating information through charts, diagrams, schematics, and graphics. Therefore, the purpose of data visualisation is to transform raw, uninterpreted data into actionable knowledge and insights. In other words, visualisations present data in a human-readable format.

Reflect

After watching the video, think of an incident in real life when you were misguided or misled because it was not presented visually. Share your thoughts and experience in the comments. Reflect and respond to your fellow learners.

So far, we have engaged with the importance of data visualisation. Next, let’s learn the process of data visualisation.

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Introduction to Data Analytics with Python

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