Do’s and Don’ts for Data Visualization

Making data visualizations is easy — but making effective visualizations is what will separate you.

Making data visualizations is an essential skill to have in the quest to becoming a well-rounded data scientist. With so many different types and styles to choose from, it can be easy sometimes to try and do too much when creating your visualizations. Unlike a lot of tasks in data science where you know your code will lead to a correct answer, there is no real “right” or “wrong” answer on how to display your data. But, with that being said, there are definitely some good habits you’ll want to develop in data visualization, along with some habits that you’ll want to avoid. In this post, I will go over some of the good and bad habits that I’ve learned and feel are the most important to share!

Do #1 — Use Preattentive Attributes

https://public-media.interaction-design.org/images/uploads/1eb0c10e1ee204a5e267621211b30d97.jpg

Do #2 — Choose the Right Visualization

Don’t #1 — Don’t Get Your Numbers Wrong

Don’t #2 — Don’t Use 3-Dimensional Data Visualizations

https://pythonprogramming.net/static/images/pandas/3D-Matplotlib-Example.png

For someone who has no experience working in data science — which is very much possible, if not likely, when you’re giving a non-technical presentation—reading something like the graph above is going to be confusing and frustrating, no matter how well you may explain it to them. Stick to 2D visualizations and make everything as clear as possible on the screen.

Dont #3 — NO PIE CHARTS

https://evolytics.com/wp-content/uploads/2014/10/Should-I-use-a-pie-chart.png

Thank you for reading! I hope this helps you in building solid data viz habits or breaking some bad ones.

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