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What 100 Students and One All-Nighter Taught Us About Our Platform
Last weekend, I had the chance to watch something pretty rare: nearly 100 students working through the night because they were genuinely excited about a data problem.
We partnered with the University of North Carolina Charlotte for the first annual "From Events to Outcomes" Datathon, and honestly, it exceeded every expectation I had going in. Twenty-three teams competed through the weekend, digging into real application data to identify what actually drives success on a software platform, real analytical work, not a classroom exercise.
What struck me most was watching the students use Zerve in ways I hadn't fully anticipated. Event judge and professor, Dr. Michael Schuckers, told me “using Zerve students were able to expand their capabilities during the time frame of the competition.” That kind of feedback lands differently when you've spent months building something and then get to see students at midnight, fueled by caffeine, finding their own paths through the data.
The winning team, Meghana Sathi, Anisha Nannapaneni, Akhi Chappidi, and Chandra Siddharth Geddam, focused their analysis on platform user retention. What stood out about their presentation was how concrete it was. They came in with specific ideas about what was driving drop-off and what we could actually do about it. Our product team has already pulled up their slides twice this week. They each walked away with Meta Ray-Ban glasses, which felt like a fitting prize for a team that quite literally found a new way to look at the data.
There's something useful about handing your platform to people who have no idea what the "right" way to use it is supposed to be. They just go find the answers. And sometimes they surface things your own team walked past a dozen times without noticing. This weekend gave us a lot of that, and I'm genuinely hoping we can figure out how to do it again. If you run a data science program and this sounds interesting, drop us a line.
Frequently Asked Questions
What is a datathon and how is it different from a hackathon?
A datathon is a competitive event where teams use data analysis to solve a real business problem, typically within a set time window. Where hackathons focus on building software, datathons center on extracting insights from existing datasets and turning those findings into actionable recommendations. The UNC Charlotte "From Events to Outcomes" Datathon challenged student teams to analyze application data and identify the drivers of success on a software platform.
How do university datathons benefit students studying data science?
Datathons give data science students hands-on experience working with real datasets under time pressure, which mirrors what professional data analysts and scientists actually face on the job. Participants practice the full workflow: data exploration, hypothesis testing, visualization, and presenting findings to a non-technical audience. Events like this also connect students directly with companies building the tools they will use in their careers.
What is Zerve and how is it used in data science education?
Zerve is a data science platform that helps analysts and data scientists explore, analyze, and share data more efficiently. In educational settings, Zerve gives students access to a production-grade environment where they can work with real data without the setup overhead that typically slows down coursework. UNC Charlotte's School of Data Science incorporated Zerve into the datathon to give students exposure to modern data tooling as part of a competitive, applied learning experience.
What kinds of problems do datathon teams typically analyze?
Datathon problems are usually drawn from real business challenges, such as identifying user retention drivers, optimizing product adoption, or finding patterns in operational data. The strongest teams move beyond descriptive analysis and deliver specific, prioritized recommendations that stakeholders can act on. At the UNC Charlotte datathon, the winning team focused on platform user retention and presented concrete strategies for improving adoption rates.
How can universities and data science programs partner with Zerve for student competitions?
Zerve works with academic institutions to bring real-world data challenges into the classroom and competitive formats like datathons. These partnerships give students access to professional tooling and expose them to the kinds of analytical problems they will encounter in industry. Programs interested in hosting a datathon or integrating Zerve into their curriculum can contact the Zerve team directly to explore what a collaboration might look like.
