Regular consultations between the department leadership and our graduates are ongoing. On December 30, a feedback session was held with alumni from both the scientific and professional Master’s programmes in "Data Science and Mathematical Modelling" ("Machine Learning and Mathematical Modelling" prior to 2025). The meeting featured active contributions from the respective Programme Guarantors, Department Professors Oleg Chertov and Igor Orynyak; Head of the Department Danylo Tavrov; and Associate Professor Violeta Tretynyk. The student body was represented by the Student Council President of the Faculty, Maksym Sokolnytskyi, alongside scientific Master alumni Kirill Danylenko and Anton Tsybulnyk, and professional Master alumni Mariia Pinda and Anna Mashyr.

The alumni shared valuable industry and academic insights regarding the long-term structuring of the postgraduate programmes, as well as the targeted pedagogical methodologies deployed across core modules. The following key recommendations were brought forward by the alumni panel:

• Within advanced quantitative modules—such as Modelling of Complex Systems and Numerical Methods in Mathematical Physics—incorporate brief, foundational review material covering core physical principles to better bridge the gap between abstract mathematical models and physical systems.

• Preserve and actively strengthen the Project Management and Startup Project Development course components, ensuring they remain integral to the curriculum for both the professional and research-focused tracks.

• Integrate industry-standard data-stewardship practices into existing data-centric modules, specifically focusing on hands-on skills with the PyDantic library or equivalent data validation frameworks.

Overall, the graduates of both Master’s programmes reflected highly positively on their educational journey within the department and commended the exceptional quality and rigor of the curricula.