Recent Talks

Last updated: 2022-07-23

Some recent online talks can be found here.

“Toronto Data Workshop” (2021): Aimee Schwab-McCoy

• The University of Toronto Data Workshop brings weekly speakers to consider best practices in data science. Each semester, members of the data science education community share their experiences in a teaching-focused panel. During this session I discussed the development of the Data Science Program at Creighton University and gave some “tips from the trenches” for data scientists in understaffed programs.

“UNL x-DBER Conference” (2021): Aimee Schwab-McCoy

• Hosted by the University of Nebraska-Lincoln’s Center for Science, Mathematics, and Computer Education, the x-DBER Conference brought together researchers in discipline-based education research (DBER) from across the globe. During this conference, I presented work from a 2019 Journal of Statistics Education article discussing trends in statistics education across the university, and discussed how DBER investigators can benefit from looking beyond their own departments.

“useR!2020: Moving backwards with R” (2020): Aimee Schwab-McCoy

• This talk was part of the 2020 virtual useR! symposium in July 2020. In this video I describe how backward course design can be effectively used to design purposeful data science, statistics, and R learning experiences.

“Data Science 101: Is a consensus curriculum on the horizon” (2020): Aimee Schwab-McCoy, Catherine Baker, and Rebecca Gasper

• This talk is based on a forthcoming Journal of Statistics Education article describing the findings of a survey of data science educators in mathematics, computer science, and statistics, and was part of the UC Berkeley Workshop on Data Science Education in July 2020.

“Data Science Education in 2020: Computing, Curricula, and Challenges for the Next 10 Years” (2020): Aimee Schwab-McCoy, Catherine Baker, and Rebecca Gasper.

• This talk was based on the same forthcoming JSE article, and given at the Symposium on Data Science and Statistics in June 2020.

“Using Subsemble-Style Estimation to Model Spatial Parameters in GLMMs with Non-Disjoint Samples” (2020): Aimee Schwab-McCoy and Mark May.

• This poster presents the results of using subsemble estimation to fit spatial generalized linear mixed models in R.

For a full list of what I’ve been up to lately, check out my CV here.