“Data visualization for analysis & discovery”

Oct. 2, 2018 from 4:30 to 5:20 p.m. — Physics/Astronomy Auditorium, room A118

Zan Armstrong, Data Visualization Designer and Engineer, Google

[Watch a recording of this seminar on YouTube here after it occurs.]


Choosing the visual form for a visualization is a decision about what aspects of the data matter most. Highlight or ignore outliers? Look at values, differences, or changes? Compare to 0, median, or mean?

In analysis we risk missing key insights by failing to notice important features of our data, yet we often use default parameters and charts without realizing what we’re not seeing.

In this talk I will demonstrate how to translate your questions about your data into chart parameters, keeping in mind your context, goals, and constraints. Instead of relying on the common “viz rules” about what not to do, I will instead provide a set of guidelines for what you should do. I’ll use examples to illustrate seemingly simple but powerful techniques like using color intentionally, creating ‘small multiples’ of charts that vary visual form or data, putting related things near each other, and optimizing for your time, energy, and attention.

This talk is relevant for all tools. So, please come regardless of which visualization tool you use.


A photo of Zan ArmstrongZan Armstrong is a data visualization engineer and designer. As part of Google’s Accelerated Science team, Zan creates custom visualizations for analysts and scientists to enable them to make new discoveries. With a background in data analysis, she is especially fascinated by identifying what characteristics of the data might be most important and then creating ways to reveal those characteristics visually.

She has also won an Information is Beautiful Award for work published in Scientific American and the metagenomics analysis tool she worked on with Stamen Design was part of SF Moma’s Designed in California exhibit. In 2014 IEEE’s InfoVis published her research on Visualizing Statistical Mix Effects and Simpson’s Paradox. Zan’s primary tools include Javascript/D3, R, and Python.