For the last year, I’ve watched just about every COVID-19 briefing by the Scottish Government, most of which are delivered by First Minister Nicola Sturgeon. Earlier on in the pandemic these were daily updates, lately it seems like once a week. The more often they happen, the worse you know things are going… If I’ve chatted with you about COVID, you have probably heard me say that I am very impressed by the way she delivers these updates.
An opportunity to teach, an opportunity to give back…
If you’ve seen one of my data science education talks or attended one of my workshops in the last few years, you’ve probably heard me talk about the unvotes package in R.
This package provides the voting history of countries in the United Nations General Assembly, along with information such as date, description, and topics for each vote.
I love using data from this package in my teaching, especially on day one of class, because the data are rich while being accessible.
Last weekend Maria Tackett and I gave an introduction to R workshop as part of the 2021 ENAR Fostering Diversity in Biostatistics Workshop for high school and undergraduate students. Our goal was to give them a taster for exploring and visualizing data with R and, hopefully, leave them wanting to learn more.
We only had 75 minutes for the workshop and a totally beginner crowd. We knew that they would be a mix of undergraduate and high school students, but didn’t know much else about them as we prepared for the workshop.
Last week I attended the Toronto Workshop on Reproducibility where I had to the pleasure of giving one of the keynotes.
When I was asked to give a keynote for this event on teaching, I had the idea of reflecting on almost 9 years of teaching with introductory statistics and data science through the lens of reproducibility. I would have said “teaching with R Markdown”, but looking back through my notes, this wasn’t true as the rmarkdown package has not been around for that long – turns out I started teaching with it when it was just knitr.
On Sunday morning I came across a tweet by NPR’s Lulu Garcia-Navarro morning asking people when they knew things were going to be different due to COVID. Whenever I read replies to a tweet like this I’m always tempted to scrape all the replies and take a look at the data to see if anything interesting emerges.
Over the past few years I’ve been working on moving from a mindset of end-of-semester project to semester-long project. Inevitably students end up doing lots of work as the deadline approaches at the end of the semester (and I can’t blame them, that’s how I work around deadlines too, and how just about anyone I know works), but creating opportunities for them to get started on their projects earlier in the semester is very important.
This post was contributed by Lee Suddaby and Zeno Kujawa, second year students at the University of Edinburgh majoring in Mathematics and Data Science, respectively.
Over the university summer break, we (Zeno and Lee) were busy making preparations for moving more of our Introduction to Data Science course from being human-graded to computer-graded. We both took this course in the Fall of 2019, as part of our first-year studies at the University of Edinburgh, and this is where we first learned R.
On May 15th and 20th the third Preparing for Careers in Teaching Statistics and Data Science Workshop was held. 37 graduate students and recent PhDs gathered (remotely of course) to learn from Allan Rossman (Cal Poly), Mine Çetinkaya-Rundel (University of Edinburgh, Duke, RStudio), Jo Hardin (Pomona), Beth Chance (Cal Poly), Lucy D’Agostino McGowan (Wake Forest), and Ulrike Genschel (Iowa State).
As recent, current, and future chairs of the American Statistical Association (ASA) Section on Statistics and Data Science Education, we have sent the following letter to Ron Wasserstein (Executive Director of ASA) and Bhramar Mukherjee (COPSS Chair) and requested that they share it with the COPSS Executive Committee.
