Amelia McNamara

Photo courtesy Mark Brown

Amelia McNamara

University of St Thomas, Assistant Professor, Department of Computer & Information Sciences.

My work is focused on learning what makes it easier to do and to understand statistics, and my research interests include statistics education, statistical computing, data visualization, and spatial statistics.

Recent notable work of mine includes

  • Speaking R, a keynote presentation at the 2020 useR! 2020 conference. In this talk, I advocate for speaking code aloud, particularly as a pedagogical strategy. I offer some suggestions about how to vocalize R code. You can watch the talk on Youtube and follow along with the slides.
  • Key attributes of a modern statistical computing tool, a paper that builds on the work of John Tukey and Rolf Biehler by providing a list of 10 attributes necessary for a modern tool for data analysis and statistics. Attributes are provided as a way to assess existing tools, or to inspire tool creators. Read online at The American Statistician, Vol. 73, Issue 4., or as a pre-print. I've also written a blog post that summarizes some of the salient details.
  • Wrangling categorical data in R, a paper co-authored with Nick Horton. This paper describes some common mistakes data analysts make when working with categorical data (factors) in R. The paper was published jointly in The American Statistician, Vol. 72, Issue 1 and as a pre-print in the Practical Data Science for Stats collection on PeerJ.
  • Going back a few years, my dissertation was entitled Bridging the Gap Between Tools for Learning and for Doing Statistics, and you can read more about it on my summary page.

For a more detailed look at my recent work, see my writings and presentations.

I have research projects in progress about the impact of R syntax on learning and teaching, the Modifiable Areal Unit Problem in spatial statistics, and the ways in which data analysts check their work. I employ several undergraduate research assistants and I am always willing to work with students on research projects.

More about me

Over the years, my educational career has included elements typically associated with the right brain (design foundations, college English major) as well as the left brain (math was my other undergraduate major, and my PhD is in statistics with a focus on computation).

However, I dislike the tendency to pigeonhole projects and people by the dichotomy of the right and left brain. Instead, I prefer to focus on projects that use a more holistic approach. In both my research and my teaching, I try to balance quantitative rigor with excellent communication.


At the University of St Thomas, I regularly teach STAT 220: Statistics I (introduction to statistics), STAT 320: Applied Regression Analysis, and STAT 336: Data Communication and Visualization. If you are interested in seeing materials from these courses (syllabi, assignments, etc) please email me. I keep meaning to post them online here but there's never enough time! (Update: 336 is up!)

At Smith College, I taught Multiple Regression (Spring 2016, Fall 2016) and Introduction to Probability and Statistics (Fall 2015, Spring 2017, Fall 2017), Communicating with Data (Fall 2017), and Data Journalism (Spring 2018).

At both Smith and St Thomas, I have had students author Wikipedia pages as part of my course. I've written about some of the frustration with this assignment on my blog, and also have a list of all the successful pages my students have written.

As a graduate student at UCLA, I had the opportunity to develop and teach a data visualization course. I was given the opportunity to develop this course as part of the Collegium of University Teaching Fellows program at UCLA. During my time at UCLA, I also served as a Teaching Fellow. I taught discussion sections for three upper-division statistics classes (101a, 102b and 101c).

For three years, I was also a graduate student researcher on the Mobilize project, which developed a year-long data science curriculum for high school students called Introduction to Data Science. The IDS curriculum has been used in 15 school districts and has had almost 10,000 students take it. The curriculum includes participatory sensing and computational analysis in R and RStudio. My work with Mobilize is discussed here, and has been a source of inspiration for my ongoing research into computational tools for novices.

Curriculum vitae

My CV is available here, although like many academics I don't always do the best job of keeping it up-to-date. If you're curious about how I TeXed it up, you can view a version of the code on GitHub.

Contact me

Reach out to me electronically: Twitter LinkedIn GitHub