TikTok, lockdown, and introduction to R

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.

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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.

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GitHub workflow for data science project proposals

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.

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Data science tutorials with learnr and gradethis

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.

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Much has been written in statistics and data science education literature about pedagogical tools and approaches to provide a practical computational foundation for students. However a common friction point for getting students (and faculty) started with computing is installation and setup. If you’ve heard me talk about teaching R, you’ve probably heard me mention the following day one dilemma: Option 1 😰 Option 2 😎 1.

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Citizen Statistician

Learning to swim in the data deluge