The times have changed, and our curriculum is not keeping up.
In the various majors offered by our Department of Biology, I’m convinced we’re not providing our students the most useful set of quantitative skills. After browsing the catalogs of a variety of other universities, I think we’re not alone.
Our curriculum has shortcomings when it comes to statistics, experimental design, and data visualization, interpretation, and management. I would guess that most of our faculty teaching upper division undergraduate courses would say that that things would be a lot better if our lower division students were provided with more opportunities to increase statistical, experimental, and data literacy.
We can’t add more units to our major, because reasons*. To add coursework related to data literacy, we’d have to cut something. I’d be all up for cutting calculus.
Our majors need to complete a semester of calculus, and some take it in semester right before they graduate. They also need to complete a year of physics, though nearly everybody takes the physics sequence without a calculus prerequisite. So, even though they need calculus to get a B.S. in Biology, they aren’t expected to apply it to anything in biology. I’m betting that most students aren’t using calculus much after they graduate.
I think it’s fair to say that, after graduation, many of our students would be using statistical and data science skills up the wazoo. With calculus, for a lot of them, not so much.
The irony here is that all of our majors do take a required course in statistics, taught by the math department. I am sure the students in this class are learning statistical theory and are getting three units’ worth of education out of the experience. Though in our biology curriculum, using this class as a prerequisite doesn’t change how we can teach our upper division courses. We’ve discussed collaborating with the math department to make sure that this course meets the needs of our students better (including, perhaps, sections designed just for our majors). However, I don’t think a single 3-unit class is going to give our majors all of the data science skills that they should be getting to go with a bachelor’s degree nowadays. We’ve got to do a lot more. (We have been having these discussions, but since I’m away on sabbatical, for all I know things are already happening, I’m just making a point to not pay attention. Whatever we do, it shouldn’t come without deliberative planning.)
I don’t want to jump on a “everybody must code!” bandwagon, but if I had to choose between requiring students to know basic differential and integral calculus, and being familiar with statistics and familiarity with (say) R, I vote for the latter. I do think that understanding calculus is fundamental to a contemporary understanding of how the natural world works. But I think understanding statistics is even more fundamental.
Has your department upped its game with data science? If so, how much of it is in your own department, how much of it involves courses/faculty from math or computer science? Did this come at a cost to other parts of the curriculum, and if so, which parts?
Update 28 March 2017: One year ago, I read and enjoyed a post by Stephen Heard, on nearly the same topic. It was so good, it was absorbed into my subconscious, so much that I was compelled to write this here blog post. But he said it first, and I think might have said it better. (After all, he did write a book on writing.) He had to point this out in the comments here, which shouldn’t have been necessary! I’m sorry, Steve.
*(We used to require an introductory computer science class, but when the chancellor’s office was asking departments to shed units from their majors, we removed this one. (It might sound like a loss, but students didn’t noticeably emerge from the course with new skills.)