Re-posted with permission from the author's blog.
This Fall, I am teaching graduate-level biostatistics. I have not had the good fortune of teaching many graduate-level offerings, and I am really excited to do so. A team of top-notch big data scientists are hosted at NCEAS. They have recently formed a really exciting collaborative-learning collective entitled ecodatascience. I was also aware of the mission of Software Carpentry but had not reviewed the materials. The ecodatascience collective recently hosted a carpentry workshop, and I attended. I am a parent and use common sense media as a tool to decide on appropriate content. As a tribute to that tool and the efforts of the ecodatascience instructors, here is a brief common sense review.
ecodatascience software carpentry workshop
What You Need to Know
You need to know that the materials, approach, and teaching provided through software carpentry are a perfect example of contemporary, pragmatic, practice-what-you-teach instruction. Basic coding skills, common tools, workflows, and the culture of open science were clearly communicated throughout the two days of instruction and discussion, and this is a clear 5/5 rating. Contemporary ecology should be collaborative, transparent, and reproducible. It is not always easy to embody this. The use of GitHub and RStudio facilitated a very clear signal of collaboration and documented workflows.
All instructors were positive role models, and both men and women participated in direct instruction and facilitation on both days. This is also a perfect rating. Contemporary ecology is not about fixed scientific products nor an elite, limited-diversity set of participants within the scientific process. This workshop was a refreshing look at how teaching and collaboration have changed. There were also no slide decks. Instructors worked directly from RStudio, GitHub Desktop app, the web, and gh-pages pushed to the browser. It worked perfectly. I think this would be an ideal approach to teaching biostatistics.
Statistics are not the same as data wrangling or coding. However, data science (wrangling & manipulation, workflows, meta-data, open data, & collaborative analysis tools) should be clearly explained and differentiated from statistical analyses in every statistics course and at least primer level instruction provided in data science. I have witnessed significant confusion from established, senior scientists on the difference between data science/management and statistics, and it is thus critical that we communicate to students the importance and relationship between both now if we want to promote data literacy within society.
There was no sex, drinking, or violence during the course :). Language was an appropriate mix of technical and colloquial so I gave it a positive rating, i.e. I view 1 star as positive as you want some colloquial but not too much in teaching precise data science or statistics. Finally, I rated consumerism at 3/5, and I view this an excellent rating. The instructors did not overstate the value of these open science tools – but they could have and I wanted them to! It would be fantastic to encourage everyone to adopt these tools, but I recognize the challenges to making them work in all contexts including teaching at the undergraduate or even graduate level in some scientific domains.
Bottom line for me – no slide decks for biostats course, I will use GitHub and push content out, and I will share repo with students. We will spend one third of the course on data science and how this connects to statistics, one third on connecting data to basic analyses and documented workflows, and the final component will include several advanced statistical analyses that the graduate students identify as critical to their respective thesis research projects.
I would strongly recommend that you attend a workshop model similar to the work of Software Carpentry and the ecodatascience collective. I think the best learning happens in these contexts. The more closely that advanced, smaller courses emulate the workshop model, the more likely that students will engage in active research similarly. I am also keen to start one of these collectives within my department, but I suspect that it is better lead by more junior scientists.
Net rating of workshop is 5 stars.
Age at 14+ (kind of a joke), but it is a proxy for competency needed. This workshop model is best pitched to those that can follow and read instructions well and are comfortable with a little drift in being lead through steps without a simplified slide deck.