Lately, I’ve been thinking a lot about the difference between amateurs and experts. In particular, I’ve been wrestling with our use of the term “data science” on this site. To me, the term denotes a level of expertise that I don’t feel comfortable claiming yet. Through a series of conversations with my Data Chefs colleagues, who have experienced similar discomfort at times, I’ve learned a great deal.
My colleagues and I have discussed the need to actively combat impostor syndrome, an accomplished person’s fear that s/he is a fraud who will eventually be exposed. Left unchecked, impostor syndrome can stifle creativity and momentum, especially among women and people of color. Still, I’ve learned that I feel much better if I don’t seek to claim an identity as a “data scientist,” but instead think of myself as someone who’s “doing data science” (albeit at a beginner’s pace).
As mentioned in the last post, most of us know some incredible home cooks who didn’t go to fancy culinary schools or study under distinguished Michelin-rated chefs. These amateur chefs have made dishes hundreds of times, perfecting them, first through trial and error, and eventually through skill and intuition. For that reason, I’d put their knowledge up against that of most formally recognized experts.
If you’re like me, an amateur struggling to take stock of your data science abilities and accomplishments, the relevant question to ask yourself isn’t the equivalent of “Do I consider myself a chef?” It’s “Am I learning how to cook?” If the answer to that is “Yes,” then, eventually, you’ll be able to ask yourself the only question that matters: “Can I cook?”