MS in Data Science - Seminar Series

12:30PM - 2:00PM
On-Campus Event - SFH Downtown

Zan Armstrong is a data visualization specialist with a background in data analysis. Through her work, she empowers people to more effectively use visualization to better understand whatever data is most important to them.

Zan's experience includes contributing to scientific discoveries as a member of Google Research's Applied Sciences team, tracking covid in wastewater for California's state and county public health officials and the public, creating interactive visualization tools for researchers at Yale, Stanford, and Berkeley, and as a data analyst forecasting revenue at Google. Zan's work has been published in Scientific American and exhibited in the art museums SF Moma, Cooper Hewitt, and Ars Electronica. She has published data visualization research in IEEE InfoVis, and spoken at conferences including OpenVis Conf, Outlier, and SciPy. More at


In data science, we create and look at charts all the time. It's the primary way that we interact with our data, and make sense of it. Yet, all too often, investing in data visualization is seen as a "nice to have", something to make things "prettier", or something to be done only at the end of the research when preparing for a presentation or publication. While data visualization is an important part of communicating results, it is a critical tool for analyzing data as well. Changing how we (literally) look at our data can be the difference between discovering or overlooking the key insight.One challenge is that it's not clear how to do it better. And, it can feel like a whole different skill set, disconnected from algorithms or our knowledge of what's important about the data.In this talk, data visualization specialist Zan Armstrong will introduce 3 guiding principles which will empower you to more effectively use data visualization *in combination with your own domain expertise* to better understand your own data. These are:

  • making the important visible
  • three simple flexible "tools": many small charts, make color meaningful, and order matters
  • demonstrating how you can embrace the complexity of your data rather than aggregate it away.

These are principles that you can put into practice today, using whatever software you are currently using to create charts. Be inspired to invest more in how you look at your data, and learn how to do it more effectively.