I’d like to start off by reading part of what the individual that nominated you said. He/she said: “Professor Settle is not only an inspiring professor, but also a woman in Data Science who is a mentor for women who are interested in pursuing opportunities in this male-dominated field. She is incredibly knowledgeable, admirable, and a strong woman who has pursued her passions with poise and confidence. This far in my life, I have never encountered such a dedicated and sincere woman who wishes to inspire other women.”
Could you tell me a bit more about your work in Data Science and academia?
I’ve been interested in helping students with their data analytics skills since I got here six years ago. For a long time, it was just helping individual students figure out which courses they should take given their interests. But now we have a formal Data Science program with a minor and a self-designed major. We’re brand new, and so it’s exciting to be building this from the ground up. Right now I serve as the Director of Undergraduate Studies. When you’re new, you’re working to develop the curriculum, but there’s so much that’s important that happens outside of the classroom as well. Developing the right support structures, out-of-classroom opportunities, internships, teaching fellows that can offer office hours – these are all just as important. For me personally, it was not easy to learn to code and I struggled with it a lot at first. I was only able to be successful because I had two very good friends in grad school who spent hours and hours patiently working with me. That’s always made me want to pay it forward. I think, especially for women, that sometimes if someone don’t think of herself as a math person, it can be easy to put up a mental block in what she thinks she can accomplish. I don’t want anyone to believe that they’re not capable of learning these data science skills if they’re motivated by the puzzles and want to be able to tackle them.
What was your reason for wanting to learn how to code?
I did it because I needed to. I didn’t learn until my first year of grad school. People don’t realize the extent to which political science is a quantitative field, and I needed to learn how to analyze the data that I had collected. There are other statistical programs that are more user-friendly, but my dissertation advisor used R and everyone around me was using R. The figures that you can make with R are just infinitely better than those from other software programs. And so I said, if I’m going to be real and I’m going to do this right, I’m going to need to learn how to use R to analyze my data. So it was really out of necessity, I knew that if I wanted to stay in the program and be successful, then I’d have to learn it.
Have there been any specific challenges that you’ve faced as a woman in the Data Science field?
I think this is a problem in several different disciplines, but at least within the realm of political data science, the people who are interested in methodology itself are disproportionately male. There’s this one political methodology conference that I attend, and it’s so skewed male. There’s something about the gender ratio within a group in that there’s a tipping point, where if there’s too many men compared to women in the group, the whole dynamic can change. This isn’t because the men themselves are doing anything intentionally, but it’s just human nature and interaction. There have been a lot of times where I’ve been the only woman in the room, and I’ve had to learn how to assert myself and not be intimidated by that dynamic. That was a big part of grad school — realizing that it’s okay to be the only woman and that I can’t let it hold me back in being my genuine self. As I’ve gotten older, I want to be bringing more women behind me and supporting them. I’ve tried to initiate more opportunities where women feel comfortable asking for help, because I think that was one of the biggest challenges for me. I was fortunate to have great friends and a great advisor, but I think it can still be hard to ask for help as a woman if you feel like the gender dynamic is stacked against you.
Is the Data Science minor program that we have at William & Mary still male-dominated?
No, not at all! That’s one of the really exciting things about the program – I’m pretty sure that we’re close to parity in women. There are more male faculty involved, but that’s just indicative of the broader trends within these disciplines.
You mentioned that two of your friends helped you a lot in graduate school – were they also female?
No, they were both male, but they were just truly patient people. They were friends first, and classmates second.
Is there a specific individual that you think you’ve helped through mentoring him/her while at William & Mary?
Yes, she’s actually visiting now. Her name is Meg Schwenzfeier, and she’s a 2014 graduate. My first year was her junior year, and another faculty member had introduced us when I was here for my job interview. Meg had taken a computer science class in high school, but she hadn’t really coded or programmed much since then. I encouraged her to learn R, since she’s incredibly bright, and I wanted her to help me with some projects and be able to use these data science skills towards her own honors thesis. Within the first two months of learning R, she just totally bypassed me. After undergrad, she went to work for a progressive consulting firm that helped Democratic candidates run experiments to test the efficacy of their campaign messages. She then moved to the Clinton campaign, and was in a very high-level data analytics role for someone her age. Now she’s in the first year of her PhD program at Harvard. She’s so smart, and way past me at this point, so I was involved just a bit in the beginning of her journey. It was great to feel like I helped her jumpstart her involvement in data science and analytics.
Definitely, and I’m sure having a female role model in a field that she was interested in pursuing really helped. My last question is: what is a piece of advice that you would give to female students interested in pursuing Data Science?
You always need to remember what got you interested in the first place. What is the problem in the real world, or what gets you excited to actually work on a problem and solve it? Maybe even write it down somewhere. So when you get frustrated, and you’re banging your head against the wall and ripping your hair out over this coding, you can remind yourself that you’re going to be part of an effective solution to a problem. Remembering your motivation is such a good incentive to push forward. Secondly, ask for help early and ask for help often. One of the things that we’re trying to do in the Data Science program at William & Mary is build a system of peer mentorship. You should always have multiple avenues to ask for help, and I think that if you ask for help before you’re totally at your wit’s end, you’re more likely to keep pushing forward. Lastly, shed any preconceptions you may have about who a data scientist is or what they look like or what their background is. The thing that data scientists have in common is this quest to use data to study problems and find solutions, and so as long as we focus on that, there’s nothing that can hold us back.