SICSS-Howard/Mathematica 2022 Participant Talks about Why Black Representation in Data Science Training Matters
This blog post is part of the 2022 series “The Future of Computational Social Science is Black” about SICSS-Howard/Mathematica, the first Summer Institute in Computational Social Science held at a Historically Black College or University. To learn more about SICSS-H/M’s inaugural start, read last year’s blog “Welcome SICSS-Howard/Mathematica 2021” or our first blog “Uncovering new keys to countering anti-Black racism and inequity using computational social science.” If you are interested in applying to participate in SICSS-H/M 2023, check out our website.
My name is Daniel Lobo and I'm a third-year PhD student in sociology at UC Berkeley interested in culture, stratification, and computational social science. I had the privilege of participating in the 2022 Summer Institute in Computational Social Science sponsored by Howard University and Mathematica (SICSS-Howard/Mathematica), the first and only site hosted at an HBCU, the only site with an explicit focus on anti-Black racism and inequity, and the only site sponsored by Mathematica. Going in, I knew this would be an educational experience, but I couldn't have predicted how inspired I would be by the end. In fact, it was probably the most transformative virtual experience I've had in our post-COVID world. Our cohort, spread across the globe, learned together, laughed together, cried together, and even danced together (thanks, DJ ROC!). Here are four learnings I'm walking away with after my unforgettable experience at SICSS-Howard/Mathematica 2022.
Cultivating a community of collaborators is important and inspiring, especially in the world of computational social science
This may seem obvious to many. Having started graduate school at the height of a global pandemic in fall 2020, however, means I have not had as much of an opportunity to develop the skill of finding potential research collaborators. I didn't realize this was an area for improvement until arriving at SICSS-Howard/Mathematica. When we were asked to brainstorm research ideas in small groups, I admit that I was skeptical. What if I can't articulate my ideas in a way that others outside of my field will understand? What if no one shares my research interests? What if we never come to an agreement on questions in time? Trusting the process taught me that just chatting with other scholars who are interested in data science can generate a lot of new and creative ideas, even if your substantive interests are not as aligned. Now, I'm working on a funding proposal (thanks, Mathematica!) with another SICSS-Howard/Mathematica alum for an idea I came up with during the institute.
To be a student of data science is to be a teacher of data science
I had the privilege of coming into SICSS-Howard/Mathematica with some background in data science. By taking part in a research project last year, I gained experience conducting survey analysis using R. I also completed some coursework in computational social science. But by no means did I come into SICSS-Howard/Mathematica feeling like a data science expert. On the contrary, I still felt like I had so much to learn. Indeed, I picked up some new skills in the form of using APIs (still dreaming of a LinkedIn API), using the RedditExtractoR package, and conducting surveys on Amazon mTurk. However, one of the moments I remember most vividly was when Andrea Adams, one of my final project teammates, stopped me mid R explanation to say: “You're a really good teacher, Daniel.” There will always be knowledge to be gained in the vast, interdisciplinary field of data science. But there is also always knowledge to be shared in data science, even if you don't feel like the most prepared teacher.
Black representation in data science training matters
This was a two-week data science training institute with a focus on anti-Black racism that had multiple Black speakers every day. It is striking how rare that is. To know that there are Black researchers and professionals interrogating big data collection methods, working to minimize algorithmic bias, and ensuring that the metaverse does not simply reproduce the racial inequality of our physical world is to know that I, too, can do such work. We should not underestimate the power of Black role models, in computational social science or otherwise. Instead, we should appreciate what it means to learn data science in a Black-centered environment as a Black scholar. It is a lot to ask a student to put themselves outside of their comfort zone to learn a new skill when they are already outside of their comfort zone at a PWI. Yet this is what is asked of most Black data scientists in training daily. In this way, SICSS-Howard/Mathematica was a gift—a learning environment that eliminated this double discomfort through a culture where we all saw each other’s strengths and willingness to learn, rather than each other’s deficiencies. And it was a culture that kept Blackness front, center, and all around us, from the faces that spoke, to the topics of conversation that moved us, to the names of the BIPOC artists, scholars, and athletes that adorned our Zoom breakout rooms each day. It is hard to put into words how much it all mattered, but it did.
Do not underestimate the power of a leader's charisma for driving institutional change
Weber reminds sociologists that charisma is a powerful source of authority. But Weber never met Naniette Coleman. If we are to create sustained change in computational social science, I believe the leaders who execute programs like SICSS-Howard/Mathematica are as important as the mission of the program itself. Naniette's infectiously positive, upbeat, and hilarious personality set the tone for each day of the program and had us ready to LEARN. Personally, it helped me with getting out of bed at 6:00 am each morning. More important is Naniette's unrelenting focus on the experiences of those at the edges of the room. The gift of SICSS-Howard/Mathematica was born out of Naniette's experience of marginalization and not belonging. Her ability to turn a negative into a positive, to create a space where people like her do belong, is not something that can be easily replicated. If we are committed to training a more diverse generation of computational social scientists, we should prop up charismatic leaders like Naniette however we can, to reproduce transformative experiences like the one I had at SICSS-Howard/Mathematica. Thank you to every student, instructor, and speaker who was a part of it.
For more information about SICSS-Howard/Mathematica, check out our website, follow us on Twitter, like us on Facebook, and join our email list. Apply now!
About the author
Daniel Lobo is a PhD student in the Sociology Department at the University of California, Berkeley. Daniel also holds a BA in Social Studies, with high honors, from Harvard College. As a cultural sociologist, he is interested in understanding the cultural processes of schools and firms that lead to the reproduction of inequality in education and the labor market. Daniel was a 2022 participant in SICSS-Howard/Mathematica.