Curriculum Design and Evaluation of Data Science Track for Management Students

The growing demand for data science competencies across professional fields creates a need for curricula that enable management students to develop analytical skills while navigating increasingly complex technological environments. This study examines the design and evaluation of a three-year Data Science track embedded within an undergraduate management program. The curriculum integrates statistics, programming, data management, visualization, artificial intelligence, and project-based learning within a progressively structured, multi-tool environment. A multi-cohort, multi-method design combined longitudinal academic performance data from two independent cohorts with graduates’ attitudes toward data science, career adaptability, course perceptions, and qualitative feedback. Academic performance showed cohort-specific patterns: variability decreased progressively across the three years in Cohort 1, whereas in Cohort 2 it decreased in the second year but increased in the third year. Graduates reported high perceived utility and interest in data science, relatively high career adaptability, and positive perceptions of applied and project-oriented courses, although self-efficacy varied in technically demanding areas. Qualitative findings highlighted authentic projects, progressive learning, and multi-tool exposure, alongside challenges related to technical complexity. The study provides program-level evidence for designing accessible yet rigorous data science curricula and highlights the potential value of progressive curricular integration and applied multi-tool learning for management students.