Stephanie D.H. Evergreen's Blog, page 2

Office Hours Public Session Q & A

Pull up a chair, ! You’re registered for the Office Hours Public Session. It’ll be Monday March 31 at 1pm Eastern Time. Convert to your time zone So, send me your most pressing question by March 27. Also be sure to note if you’ll be attending live. You’ll be able to unmute and ask your […]

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Visualizing Responsibly

Statistics can lie and charts can aid and abet. To do our audience justice, we have to visualize data responsibly. Avoid these traps.

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Launch a Data Viz Revolution

When you know strong data visualization is crucial to your team's success but you have a boss stuck in Windows 95, you need to manage up. You need sneaky ways to launch a data viz revolution at work.

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Color is a Connector

When you use color as a connector between the different points of your data story, you ease the effort it would take your audience to follow along. You lower the mental hurdles so they can come with you.

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Data Visualization in Presentations

When you know your graph's destination is an in-person presentation, incorporate these design tips so your work is visible from the last row.

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10 Ways to Make Your Graphs More Accessible

Of course you can make data visualization that's accessible to people with various disabilities. Take these 10 do-able actions to start.

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The Data Visualization Checklist

The Data Visualization Checklist outlines what to do with each tiny part of your graph so it tells your story.

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Ditch Your Chart Legends

When we make our audiences seek-and-find to match the legends & the content, we hurt cognition. Brains want this 🤞🏼, not this ✌🏼.

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Where to Start and End Your Y-Axis

The Y-Axis Debate is one of the most hotly discussed among cool data nerds, like you and me and our friends. Going out for drinks with people like us is either a blast or a bore, depending on your nerd level. So let me clarify the parameters of the debate, including where nerds mainly agree, […]

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Assign a Color System for Graphs

You see the constellation where your audience only sees a random smattering of stars. What makes sense to us (who have been so steeped in the data we’re dreaming about it) will not be readily obvious to an outside viewer (even if it’s someone who cares quite a lot about the results). We can help […]

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