Learning That Hits Different
We don’t often think of data as personal. Charts, spreadsheets, code—these are things that happen in labs or startups, not classrooms full of curious undergrads wondering how to make sense of the world.
But in May 2024, something different happened at Howard University. Over the course of three weeks, a group of undergraduate students—many of them self-described non-math people—stepped into a classroom and discovered just how powerful data can be when it starts with your story, your questions, your community.
The program was called Q4Q: the Quantitative Bootcamp for Qualitative Social Scientists, and it was designed to do something simple but transformative: to give social science students the tools and space to explore computational thinking, not as a technical add-on, but as a new way of seeing and shaping the world.
What Is Inclusive Computational Pedagogy?
Inclusive computational pedagogy invites students to bring their full selves into the learning process—their cultures, questions, histories, and dreams. It builds on the belief that knowledge is not something we passively receive, but something we create together.
In this approach, students use math, data, and technology not just to solve abstract problems, but to explore the issues that shape their lives and communities. It values lived experience, encourages collaboration, and redefines what it means to engage deeply with quantitative work.
It’s not about turning every student into a data scientist. It’s about opening new pathways: into conversations, careers, and futures that have too often been closed off—and making sure every student sees themselves as a builder of knowledge, not just a consumer of it.
Where This Is Already Happening: Q4Q at Howard
In its first year, Q4Q brought together a small cohort of Howard students from social science disciplines—students interested in questions of race, health, housing, music, and justice. Over three weeks, they met daily to build skills in Python, explore data sets, and design projects that connected their learning to the world around them.
Their inquiries were as personal as they were critical: examining harm experienced by vulnerable youth, exploring how music shapes collective identity, and recovering the economic histories erased by racial violence. Each project reflected a commitment to using data not just as a technical tool, but as a way to understand, challenge, and imagine.
Student Spotlight: Quantifying Carceral Harm
Research Focus: A social science major interested in justice systems and youth incarceration
Tool Used: National Youth in Custody Survey (NYCS), statistical analysis in Python
Methods: Data cleaning, subsetting by demographic groups, exploratory comparisons using proportions and visualization
Framing: The student used a public federal dataset to explore disparities in how incarcerated youth report their treatment by staff, with particular attention to how identities shape those experiences.
“Before this, I had never used code to work with real-world data—especially something this serious. I learned how to translate a question into a dataset, and how to clean and reshape it until it could start to answer me.”
— Q4Q Participant
Student Spotlight: Text Mining and Black Cultural Expression
Research Focus: A humanities major exploring music, identity, and collective memory
Tool Used: Voyant Tools (text mining and visualization platform)
Methods: Corpus creation from song lyrics, topic modeling, frequency analysis, word collocations
Framing: The student used computational text analysis to explore patterns across the discography of a major contemporary hip-hop artist, applying these tools to examine themes of struggle, transformation, and cultural narrative.
“I was surprised how much structure I could find in something like music. The tools helped me see patterns that even I, as a fan, hadn’t noticed before. It made me think differently about what counts as data.”
— Q4Q Participant
Student Spotlight: Quantifying Historical Losses
Research Focus: A history major focused on race, gender, and economic displacement
Tool Used: Archival records + Python for estimation and visualization
Methods: Structured data extraction from primary sources, estimation of historical property values, basic modeling
Framing: The student developed a method for estimating the economic toll of racial violence on Black women business owners during a 20th-century racial massacre, using computational tools to quantify narratives often left out of dominant histories.
“Working with messy historical records helped me see that data work isn’t just about numbers—it’s about care, and deciding what story you want the data to help you tell.”
— Q4Q Participant
Why This Matters
Q4Q is just one program, but it points toward a broader shift in how we think about education in a data-driven world. It suggests that:
Students don’t need to be in a STEM major to do meaningful quantitative work.
Learning sticks when it’s tied to identity, purpose, and community.
With the right environment, students can do far more than we typically expect.
This matters not just for students, but for the future of research, policymaking, and problem-solving in our society. When we widen the circle of who gets to ask questions—and who has the tools to explore them—we all benefit.
A Different Kind of Learning
By the end of Q4Q, students weren’t just more skilled with data—they were more confident, more connected, and more curious.
“Three weeks ago I would not have had the skill set to even upload a data set, and now I can upload, clean, and troubleshoot using Python.” — Q4Q Participant
They saw data as something they could wield, not just interpret. They saw themselves as thinkers and builders.
“Even though it was hard work, I am proud of my final project. I think it is representative of the kind of scholar I am becoming—one who knows they can learn what they do not know, one that lets others help me, and keeps the bigger picture in mind about how far I’ve come.” — Q4Q Participant
They started to imagine futures where they could do this kind of work—not in spite of who they are, but because of it.
Programs like Q4Q remind us what’s possible when we treat students as collaborators in learning—where each student's experiences, questions, and insights help build new ways of seeing and solving the challenges around them.
And if that’s not what education is for, what is?