Astronomer Meredith Rawls was in an astronomy master’s program at San Diego State University in 2008 when her professor threw a curveball. “We’re going to need to do some coding,” he said to her class. “Do you know how to do that?”
Not really, the students said.
And so he taught them—at lunch, working around their regular class schedule. But what he meant by “coding” was Fortran, a language IBM developed in the 1950s. Later, working on her PhD at New Mexico State, Rawls decided her official training wasn’t going to cut it. She set out to learn a more modern language called Python, which she saw other astronomers switching to. “It’s going to suck,” she remembers telling herself, “but I’m just going to do it.”
And so she started teaching herself, and signed up for a workshop called SciCoder. “I basically lost the better part of a year of standard research productivity time largely due to that choice, to switch my tools,” she says, “but I don’t think I could have succeeded without that, either.”
That’s probably true. Rawls’s educational experience is still typical: Fledgling astronomers take maybe one course in coding and then informally learn whatever language their leaders happen to use, because those are the ones the leaders know how to teach. They usually don’t take meaningful courses in modern coding, data science, or their best practices.
But today’s astronomers don’t just need to know how stars form and black holes burst. They also need knowledge of how to pry that information from the many terabytes of data that will stream from next-generation telescopes like the Large Synoptic Survey Telescope and the Square Kilometer Array. So they’re largely teaching themselves—using a suite of open-source training tools, focused workshops, and fellowship programs aims to help and actually prepare astronomers for the universe they’re entering.
Back when telescopes produced less data,…