Jordan Ellenberg’s parked in a chair at the front of the room in Sterling Hall, gesturing earnestly as 16 freshmen furrow their brows and listen to his observations on one of their classmates’ works.
Ellenberg’s a professor of mathematics, so you’d expect he’d be offering feedback on a complex equation or a geometric proof. Instead, he’s complimenting a student’s use of narrative technique.
“The story elements are really what’s working here,” he says, offering the student, who has penned an essay about how complicated the online college admissions process has become, a smile and a thumbs-up gesture. “They’re helping the piece have impact.”
Welcome to Writing and Data, the First-Year Interest Group (FIG) Ellenberg developed and debuted this semester. As the name suggests, it’s all about teaching students the skills needed to write compelling stories that are based on the literal mountains of quantitative data being generated in every field of study. He wanted to call the course “Making Words Count,” and that double-edged pun likely comes closer to his intent.
Plenty of English and Creative Writing classes offer the type of model Ellenberg is using — student writes essay, student receives constructive criticism from classmates and instructor, student’s writing improves. But the fact that it’s a mathematics professor who’s doing the teaching and the essay arguments all must be supported by numbers and data sets adds an additional dimension.
In college, Ellenberg thought he’d end up being a novelist, so he took oodles of creative writing courses and workshops.
“The experiment of this course is to bring that workshop style, which works really well for short fiction, to the study of the short expository piece with lots of data in it,” says Ellenberg. “I had no idea whether this would work. But so far, it really has!”
Most of the students here are computer and data science majors, with a smattering of humanities majors mixed in for good measure. The point of the UW–Madison FIG experience is to have a smaller set of students take a cluster of classes together, creating a sense of community in an otherwise vast campus experience.
Ellenberg is uniquely positioned to teach a course like this. Between the books he has written, like 2021’s Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy and Everything Else and the data-driven essays he’s written about Homer Simpson, March Madness and electoral gerrymandering for outlets like The New York Times, The Wall Street Journal and Slate Magazine, he’s made a name for himself as a mathematician who can communicate effectively about the science he studies. (His students are aware of his books; his articles, not so much.)
“I somehow accidentally learned how to write about quantitative topics for a general magazine and newspaper audience,” says Ellenberg. “I figured it out by trial and error and made tons of mistakes. So now I’m trying to figure out the right way to teach this, because there’s a huge hunger from both editors and readers for writing that can handle data intelligently and intelligibly.”
It takes time, and it’s not always an easy thing. One of Ellenberg’s students has written an essay trying to use the somewhat arcane concept of Wins Above Replacement (WAR) to argue that average baseball players — or at least the ones not named Shohei Ohtani — are often at a disadvantage in Major League Baseball’s contract arbitration process.
After a couple of read-throughs, it’s clear the other students in the class, most of whom are not baseball fans, are largely mystified by it. Ellenberg, a baseball aficionado, sees another teaching moment.
“Remember to think about the audience for your piece,” he tells the class. “Think about what your audience already knows — and what it doesn’t.”
The students seem to appreciate Ellenberg’s supportive but strategic approach to critiquing their work. He points out the little things they’re doing right — a key word choice here, a graceful juxtaposition of data there — as he navigates them through the parts that still need refining. What he’s really angling to do is hone their reading skills.
“You can’t write unless you can edit and you can’t edit unless you can really read,” he argues. “Not just let your eyes pass over the words and pick up the meaning, but read.”
Amanda Barnes, a freshman who’s planning to major in data science, doesn’t necessarily think she’ll end up in a writing-based job, but she’s sure she’ll need the skills she’s learning in her FIG.
“I’m going to have to write using numbers at some point,” Barnes says. “If I learn how to do it now, I’ll be in much better shape when I’m faced with it later.”
Alysia Chou, a computer science major, is hoping to improve both her English and writing skills.
Ellenberg’s hoping to give his students the skills to be able to talk about whatever they’re studying — whether it’s data science, anthropology or English literature — to audiences beyond their own academic bubbles.
“Data is too important for knowledge about it to be locked up among people who understand the wonderful, efficient, but hard-to-learn shorthand people in quantitative fields use among ourselves,” he says.
