Rootstrap Blog

Category: Data Visualization

Total 2 Posts

Data Samples and error visualization techniques

Why we should choose representative samples with error in mind when we build data visualizations. A brief overview of uncertain bar charts and uncertain ranked lists.

The type of data samples that populate our visualizations can add uncertainty to our results. Some common data displays like bar and pie charts work better than others for making that uncertainty understandable. This article explores how to understand our data samples and create the most suitable graphs for visualizing what they represent.
In general, the goals of data science are to understand data and generate predictive models that help us make better decisions. For a more thorough overview of data visualization, see “Data visualization and The Truthful Art.”

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Data Visualization and The Truthful Art

An amazing book about data visualization that I can’t recommend enough is The Truthful Art by Alberto Cairo.

In The Truthful Art, Cairo explains the principles of good data visualization. He describes five qualities that should be your foundation when you work with data visualization: truthful, functional, beautiful, insightful, and enlightening. Cairo also gives some great examples of biased and dishonest visualization.

Before I dive into the “Five Qualities of Great Visualizations,” there’s another related concept that I want to cover: data-ink ratio, introduced by Edward Tufte in The Visual Display of Quantitative Information.

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