In June I attended an ACM workshop focused on how the ACM can facilitate sharing elements of a data science curriculum across institutions. This is my recap.
Hi! I’m an Assistant Professor of Statistics in the Department of Mathematics and Statistics at Carleton College. I teach statistics at all levels of the curriculum and am passionate about communicating the beauty and power of modern statistical methods to my students. My research focuses on incorporating realistic computation and visualization into the classroom, exploring the potential of visual inference, developing better visualizations to explore complex models, and developing useful and useable R packages.
PhD in Statistics, 2013
Iowa State University
MS in Statistics, 2009
Iowa State University
BA in Mathematics/Statistics, 2007
Luther College
During winter 2018 I am teaching the following courses at Carleton:
Past courses that I have taught at Carleton:
Wed, Dec 5, 2018, Invited Statistical Sciences Seminar Series
Thu, Nov 1, 2018, Macalester College MSCS Seminar
Fri, Jan 12, 2018, The 2018 Liberal Arts Data Science Workshop
Mon, Jul 31, 2017, 2017 Joint Statistical Meetings
Mon, May 15, 2017, United States Conference on Teaching Statistics
Tutorials and case studies teaching data-scientific concepts in statistics courses.
Providing an easy way to bootstrap nested linear-mixed effects models using either the parametric, residual, cases, CGR (semi-parametric), or random effects block (REB) bootstrap fit using either lme4 or nlme.
Extending ggplot2 to provide a complete implementation of Q-Q plots.
In June I attended an ACM workshop focused on how the ACM can facilitate sharing elements of a data science curriculum across institutions. This is my recap.
The analyses that get me excited are not Google crunching a terabyte of web ad data in order to optimize revenue… [but rather] the biologists who are absolutely passionate about this one swampfly and now they can use R and they can understand it.
The Upshot takes a look at who’s in and who’s out of the first Republican debate taking into account sampling variability.