From Theory to Practice: The First Data Mining Hackathon for Fourth-Year Students

December 7 2025

On December 7, 2025, a hackathon for fourth-year students of the Department of Theory and Technology of Programming took place as part of the “Fundamentals of Data Mining” course. The event incorporated elements of product analytics. The aim of the event was to strengthen students’ practical skills through working with real data under conditions close to professional analytical practice. This approach extended learning beyond traditional lectures and immersed students in an environment resembling a real IT project.

Challenges and Format

Participants worked with a real business dataset under time constraints. The hackathon format provided full flexibility in choosing tools and approaches — from data preprocessing to building predictive models and delivering final presentations. This enabled students to experience the dynamics of analytical work, where both technical solutions and the ability to make prompt, well-reasoned decisions are essential.

Teamwork

Effective team collaboration was a key success factor. Students distributed roles, supported one another, and adapted to changes, developing skills in cooperation, communication, and responsibility.

Hackathon Winners

The winning team demonstrated the best combination of technical expertise and strong team performance. We congratulate the winners:

  • Yevheniia Bazhmaieva
  • Vladyslav Pavlenko
  • Zlata Vasylenko
  • Dmytro Ostapenko
  • Olha Melnykova
  • Oksana Panchyshyn

Department Tradition

Hackathons have long been an integral part of the department’s educational environment and are regularly organized in collaboration with industry partners, including Hackathon Expert Group.

At the same time, this event marked the first integration of the hackathon format directly into an academic course. The format was designed with consideration of modern industry practices, particularly those used by Genesis, allowing the educational process to be closely aligned with real-world data analysis workflows.