Integration of Data Science methods into project management teaching as a factor in improving the quality of managerial training

The digitalization of management activities creates a demand for professionals who simultaneously command project management methods and data analysis tools. Existing managerial training programs typically separate these competencies into distinct courses, reducing graduates’ readiness for evidence-based project management. The purpose of the study presented in the article is to evaluate the impact of integrating Data Science (DS) methods into a project management course on students’ project competencies. A quasiexperimental design was applied: the experimental group (n = 44) studied a course with predictive analytics, data visualization, and algorithmic risk assessment modules; the control group (n = 43) followed the traditional curriculum. Dependent variables: project solution quality (rubric 0–25, ICC = 0.89), data literacy (test 0–20), managerial decision quality (case test 0–15). Results: the experimental group demonstrated statistically significant superiority across all three indicators (r = 0.28–0.42, p < 0.01). The maximum effect was found in data literacy (r = 0.42). A significant correlation was found between data literacy and project solution quality (rs = 0.52, p < 0.001). The findings support an integrated teaching model in which Data Science tools are embedded directly into the project logic of the course rather than taught in isolation.

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