Yeah, yeah, data science is a science and an art: But what does that really mean and why does it matter? | CDS Guest Editorial by Andrea Jones-Rooy

This entry is a part of the NYU Center for Data Science blog’s recurring guest editorial series. Andrea Jones-Rooy, Ph.D., is the Director of Undergraduate Studies and a Visiting Associate Professor at CDS. They are a data scientist and standup comedian.

Andrea Jones-Rooy, Ph.D.

If you’ve ever taken one of my data science courses, had the misfortune of running into me at a party, and/or heard me rant and rave during a standup comedy set in Greenwich Village, you already know by now that I cannot stop talking about how data science is both a science and an art. But what does this really mean, and why is it so important?

Let’s start with the science part. This part may seem clear (it’s right there in the name after all), but we often neglect the science part of data science in our enthusiasm about the data. To be fair, there is a lot of data out there, and this is super exciting. But data alone can’t get us very far. Science is how we go from staring at a bunch of numbers and letters to actually better understanding our world. Science is also not some abstract idea: it’s a series of steps we all agree to take.

If you’ve made it to the CDS blog, you probably already know all about the scientific method, but just in case, the steps we scientists take are: Observe something in the world, ask a question about it, think up a plausible explanation, turn that explanation into something that can be evaluated with evidence, find that evidence, test how well your idea explains it, and then do it all again (and again). One of the hardest parts of science is being willing to be wrong: we need to agree to update our views in light of new evidence, which may mean (eventually) abandoning a theory we really love. And one of the most important parts in science is what I call “Step 0” of the scientific method: being curious about the world in the first place.

Art shows up in each of these steps. Being curious about the world means wondering why things are the way they are and imagining other ways they might be. Artists do this all the time: they might build an alternative world in a science fiction novel or help a community collectively process the trauma of war through a one-act play where things end differently. Artists are also keen observers of the world: it’s how they paint naturescapes and still-lifes, and how they write screenplays that resonate with audiences.

Asking questions is also second-nature to artists — for example, any time a comedian says, “What’s the deal with …?” followed by a punchline, they’re not just forming a question, they’re offering a theory. And every joke itself is a hypothesis that a crowd of strangers will find something funny. (By the way, nothing is more effective for hypothesis rejection than silence.) In fact, every time an artist puts something out in the world — whether a dance performance or a sculpture or a YouTube video — they’re testing their idea that others will find value in what they’ve created. And a good artist, like a good scientist, adapts from the evidence, furthers their craft, and tries again.

When we think of art, we tend to picture pottery wheels, paint-splattered smocks, and canvases covered in happy little trees. When we think of science, we tend to think of beakers, safety goggles, and lab coats. But what I’ve just named from both worlds are the physical tools we use. Underneath both of them, we use creativity, imagination, and gut instincts, as well as rigor and precision.

So if you’re a scientist stuck on a problem, channel your inner artist: be curious, think big, and wonder “what if?”. And if you’re an artist stuck on a project, channel your inner scientist: test your hypothesis, share your art with the world, be willing to be wrong, and then try again.

By Andrea Jones-Rooy



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Official account of the Center for Data Science at NYU, home of the Undergraduate, Master’s, and Ph.D. programs in Data Science.