TEACHING AND PRESENTING ABOUT DATA AND VIZ

conference presentations

Joint Statistical Meeting Aug 2016, Chicago - Invited Speaker on the Recent Advances in Information Visualization panel, sharing research previously published in Proceedings of IEEE InfoVis

OpenVis Conf Apr 2016, Boston - Everything is Seasonal - video

Abstract: People, and our data, are heavily influenced by our regular hourly, daily, weekly, seasonal and annual patterns, as well as by typical (holidays, weather variation) and one-off (natural disasters, electrical outages, war, etc) aberrations to these patterns.

Time series analysis must take seasonality and seasonal variation into consideration, but so should any analysis comparing data at two different points in time.  For example, frustrated with how much worse the traffic has gotten from July to November?  Worried it's just going to keep getting worse?  Consider that most commuters take a week's vacation during the 8 weeks of summer, but almost nobody takes vacation in early November.  Based on that alone, you'd expect there to be around 14% more cars on the road in early November than in July.

This talk uses examples to illustrate 6 key recommendations for thinking about the role of seasonality in your data, both to avoid (sometimes surprising) pitfalls and to reveal insights that are too often aggregated away.

IEEE InfoVis Nov 2014, Paris - Visualizing Statistical Mix Effects and Simpson's Paradox - paper

Abstract: We discuss how “mix effects” can surprise users of visualizations and potentially lead them to incorrect conclusions. This statistical issue (also known as “omitted variable bias” or, in extreme cases, as “Simpson's paradox”) is widespread and can affect any visualization in which the quantity of interest is an aggregated value such as a weighted sum or average. Our first contribution is to document how mix effects can be a serious issue for visualizations, and we analyze how mix effects can cause problems in a variety of popular visualization techniques, from bar charts to treemaps. Our second contribution is a new technique, the “comet chart,” that is meant to ameliorate some of these issues.

 

teaching & Workshops

Elijah Meeks' Complex Data Visualization with D3 weekend workshop - taught 3-hour Drawing with Data: Creating Custom Visualizations workshop session

Lick-Wilmerding High School in San Francisco - 10th grade class - guest teacher introducing data visualization

BB&N grade school in Boston - 2nd grade class - guest teacher introducing data visualization

Google's internal training program - Taught several half-day data visualization courses for Googlers

 

speaker series & bay area meetups

Metis San Francisco Data Science Meetup, April 2016 - Panel Discussion: Creating Custom, But Generalizable, Visualizations

Bay Area D3 Meetup, July 2015 - Map Matching - video

SF Data Mining Meetup, June 2015 - Your Data Doesn't Mean What You Think It Does - slide deck

SF D3 Meetup - Math to D3, Feb 2015 - video

UC Davis SIAM Mathematics in Industry Speaker Series, May 2014 - Visualizing Data for Analysis and Data-Driven Questions

Abstract: In my 5 years analyzing data on Google's "revenue team," I discovered that data visualization could, and should, be a critical part of effective analysis.  In this talk I'll demonstrate why it's important to look beyond top-line metrics and how great visualizations help make sense of tens, hundreds, thousands, millions, or billions of data points at a time.  I will also share the two most important techniques I've learned for creating effective data visualizations and tell a bit of the story of how I got from abstract math research as an undergrad to teaching high schoolers to Google analyst to freelance data visualization.

 

guest expert

USF's Digital Literacy Course for undergraduates - shared my experiences as a data visualization professional

TechChange's Technology for Data Visualization online course - interviewed as a guest expert, with a focus on sharing different views on common data