For the second year in a row, Skyline College students took part in the Community College DataJam. In this semester-long challenge, students work in teams to turn real-world data into meaningful analysis. This year, Skyline fielded five interdisciplinary teams, and two took home top honors among 18 projects from three participating colleges.
Since January, students from across campus met weekly with mentors—graduate and undergraduate students from San Francisco State University and the University of Pittsburgh—to explore topics they cared about, develop research questions, find and clean data, and apply coding and data analysis techniques. At the May 1 finale, all teams presented their findings to a panel of judges, and Math Professor and MESA Co-Director Denise Hum, who coordinates Skyline’s teams, gave remarks on how DataJam supports community college students.
“DataJam gave us real projects, real skills, and real growth,” Hum said. “Students learned to frame questions, test assumptions, and work together to tell the story behind the numbers. But most importantly, they got to see themselves as data scientists—whether or not they had prior coding experience or data analysis.” That impact was clear across all five teams.
The Wednesday Morning Team, which took first place, investigated “Healthcare Access and Chronic Disease in California.” Their analysis found a strong link between limited access to healthcare and poor management of chronic illnesses like diabetes and heart disease. They employed exploratory data analysis, correlation analysis, and predictive modeling to demonstrate that more frequent doctor visits were associated with earlier diagnoses and improved outcomes. “It was working with data and seeing myself in the realm of data science,” said one teammate. Another team member, Ashley Gutierrez, shared, “DataJam was the first time I was pushed to think creatively through the many challenges my team and I encountered. Presenting to the judges made me realize how important it is to clearly explain not just how we coded something, but why it matters.” Amapola Garcia, also on the winning team, noted that while she had worked with code and datasets before, “DataJam offered an opportunity to dive deeper into a problem my team wanted to answer. I always cherish working with a team and bouncing ideas off one another.” The Friday Morning Team placed second with their project, “Public Transit and Air Quality in San Francisco.” They set out to test whether increased public transportation use correlates with
improved air quality. While COVID-era data introduced significant outliers, the team observed small trends and concluded that more robust data would be needed for stronger conclusions. Diya Tandel reflected, “The project pushed us out of our comfort zone and challenged us to use all our skills—coding, research, and handling large datasets.” Her teammate, Ekaterina Alekseenko, appreciated the balance of independence and support: “We had so much freedom to choose our topic and methods. I learned a lot and was able to practice thinking like a true data scientist.” Teammate Travis Wellman added, “Participating in DataJam gave me valuable experience working in a self-directed team. I’m grateful for the opportunity to learn with peers and apply statistics and Python to real problems.”
Students from other teams also found the experience transformative. Andrew Calderon of the Wednesday Afternoon Team shared, “I never once thought I would participate in anything like DataJam. However, after returning to school to change my career path, I realized I needed to step out of my comfort zone. Taking real-world data, questioning it, and finding something worth investigating was a first for me—and it made me wish I had done projects like this earlier in my academic career.”
Many students were new to coding or had never worked on a team-based research project. Jillian Lampaya of the Thursday Team said, “I knew I would be intimidated by the coding, but DataJam was more than that. Working in a team where everyone was learning together helped me develop both technical and communication skills.” Qian Zhao echoed this, reflecting on the deeper lessons of data analysis, “At first, our hypothesis—that food insecurity and income were inversely related—seemed obvious. But then the data surprised us. We learned to look deeper and discovered that environmental factors, like drought, may have played a role. It really sparked my interest in finding the stories behind the numbers.”
For students like Noah Mondragon Arcos, who entered with little experience, the structure and support were key. “It helped ease my nerves. I didn’t know anyone at first, but the way the program set up teams and mentors made it a lot less overwhelming.” And for Zishan Li, the experience offered a glimpse into the collaborative nature of real-world data work: “Our mentor gave us so much great advice—not just about the data but how to present it clearly. I’m so proud of our project, and I highly recommend DataJam to anyone curious about data science.”
The success of Skyline College’s teams at DataJam reflects more than just competition results—it showcases the power of hands-on experiential learning, collaboration, student-led inquiry, and mentorship. Through real-world data projects, students not only built technical and analytical skills but also gained confidence, a sense of community, and a clearer vision of how they might use data to make a difference in the world.
Special thanks to Dr. Judy Cameron and Alex Bauer from The DataJam, mentors Chike Ujuagu and Malleeswari Jagabattuni from San Francisco State University, and mentors Yuexi Yang and Devon Smith from the University of Pittsburgh.
