Data visualisation plays two key roles:
1. Communicating results clearly to a general audience.
2. Organizing a view of data that suggests a new hypothesis or a next step in a project.
It’s no surprise that most people prefer visuals to large tables of numbers. That’s why clearly labeled plots with meaningful interpretation always make it to the front of academic papers.
This post looks at the 10 visualisations you can bring to bear on your data — whether you want to convince the wider world of your theories or crack open your own project and take the next step:
https://www.datasciencecentral.com/profiles/blogs/10-visualizations-every-data-scientist-should-know
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