As an undergraduate student at Morehouse College, Kevin Womack double majored in mathematics and computer science. To study two such demanding fields was doubly difficult, he says. But, crediting his mother (an engineer) and his father (a computer scientist), Womack says that quantitative reasoning came naturally to him and he was undaunted by the workload. Now a student in the master’s degree program at the Data Science Institute at Columbia University, Womack excels in his coursework and is an advocate for increased diversity in data science. Here, he discusses his background, his career goals, and his commitment to diversity.
Tell us about your time as a student at Morehouse College in Atlanta.
Choosing to study at a historically Black college was truly the best and most important decision I have made in my life thus far. Morehouse allowed me to be in rooms and places that I would’ve never dreamed of. Their expectation of excellence helped me find my way to Columbia. And I’m just getting started.
How did you become interested in data science?
I found that the exact meeting point of math and computer science was data science, so I fell into the field rather naturally. Most importantly, I knew wanted to get involved in a field where I could work with and help people. I realized I didn’t want to be a software engineer and sit in front of a screen all day. I knew I wanted to connect with people and the stories their data reveal about them.
Why did you choose Columbia’s Data Science Institute for graduate study?
I wanted to be at an institution that would truly challenge me and put me at the forefront of growing areas of research in data science. Columbia promised the most rigorous and innovative curriculum on the planet, and this was something that I just had to see for myself. Additionally, you can’t beat the opportunity to study in New York City at an Ivy League institution. I knew the educational experience here would be like no other.
How will increasing diversity in data science improve the field?
I was just talking to my dad about this. What makes data science so interesting is that it’s about people. All the data we work with comes from people and their varied experiences. The data tells their stories. So if one group of people is totally left to analyze the data, potentially certain people’s stories will be left out or told inaccurately. That’s why I’m a big advocate for increased participation in data science by all groups and especially people of color. A perfect example is ProPublica’s study of algorithmic bias against blacks by way of their criminal risk scores. Law officials use risk prediction algorithms to inform decisions about which prisoners are most likely to be recidivists after prison. But ProPublica found the algorithms were written so as to inaccurately identify Black offenders as future criminals. The data analysts who wrote these algorithms were not diverse and they didn’t collect and evaluate data accurately. So they didn’t get at the holistic truth, which is what data should be used for. We actually covered this injustice in our statistics course with Vincent Dorie last semester.
How do you work to increase diversity?
Of course, I attend conferences on diversity in tech and STEM and I network within my community in an effort to seek opportunities for collaboration and education. But the most important thing I can do as an aspiring data scientist is to just be in the room. Representation is key when it comes to promoting diversity. My studying data science at Columbia shows other young black men and students of color that they can come here and succeed, too. It shows they won’t be alone. Before I left Atlanta, I became involved with the Atlanta University Center (AUC) Data Science Initiative, which seeks to increase the participation of the member institutions in developing research and education in the field of data science. I look forward to seeing how this initiative can leverage the resources and staff at Columbia to get more students like me to apply and join the master’s program.
You have had three internships at Google and were offered a full-time role. Care to share about that experience?
In many ways it was a dream job to work at Google, and truthfully some days I miss the perks like free food and massages. However, I was working on projects not connected to people or communities I care about. I felt like a small piece of a bigger machine — not doing work that inspired or challenged me. I didn’t accept the offer and ended up applying to graduate school. I feel with absolute certainty that I made the right choice to come to Columbia and further my education.
What has been a highlight of the DSI experience for you?
There’s one class I loved, Exploratory Data Analysis and Visualization, taught by Joyce Robbins. She’s a sociologist by training and her journey into data science is important. As I mentioned, you can’t study data well without knowing people, and as a sociologist Professor Robbins has studied people deeply. For her class, I was part of a team that developed an exploratory and interactive profile of the DSI class of 2020. It’s a data visualization that shows exactly where the students come from, what they are interested in, and how diverse the class is. In one section of the project, we explored the breakdown of our cohort by gender with a social network graph (shout out to Patrick Stanton!). Every node in the visualization represents a student, and you can clearly observe social clustering by gender. It’s a really fun tool to explore.
What do you hope to do after you complete your master’s degree in data science?
I want to solve interesting problems that impact my community. It gets me really excited to think I can use data in that way — to effect positive change in a group I care about. I recently had lunch with one of my cousins and she mentioned a movement called Data for Black Lives, which uses data in the fight against police brutality against Black people. That’s a great example of a project I’d love to work on because it’s seeking to inspire positive change on a greater scale.
What advice do you have for students applying to graduate school for data science?
Know yourself and your interests and imagine the future you hope to have. For students of color, especially in STEM and engineering, know that there are others like you and we are here, even though you may not see us. You may feel isolated at first, but remain open and you’ll find that making friends will be the least challenging part of your journey, especially in New York City.
— Robert Florida