We are excited to host Andrew Gelman, who will be speaking about tradeoffs in statistical graphics. "Infographics" often have the goal of aesthetic appeal to draw a casual viewer in deeper, while "statistical graphics" often have the goal to reveal patterns for viewers who are already interested in the problem. Andrew will illustrate by giving examples of choices in graphics he has made over the years.
Andrew Gelman is a renowned professor of statistics and political science at Columbia University, a prolific author, and a popular blogger. His books include Bayesian Data Analysis, Teaching Statistics: A Bag of Tricks, Data Analysis using Regression and Multilevel/Hierarchical Models (which has the most understandable description of causal inference I've ever read), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do, and A Quantitative Tour of the Social Sciences. His blog, "Statistical Modeling, Causal Inference, and Social Science" is, in my opinion, the best statistics blog out there and an invaluable resource for data scientists.