A data-driven analysis of gender disparities in STEM

Abstract

This thesis explores gender disparities in STEM tertiary education across selected EU member states using harmonized Eurostat datasets processed in Python and visualized through Microsoft Power BI dashboards. The study applies descriptive comparative analysis across field-level graduation data, longitudinal trend analysis, and scatter plot comparison, incorporating the Gender Equality Index as a contextual variable. By examining both graduation and enrollment data across multiple STEM disciplines, the research addresses whether observed disparities reflect field-specific structural patterns or broader national equality conditions. The findings contribute to the ongoing EU policy discussion on gender balance in technical education by providing longitudinal, field-disaggregated results for a strategically selected comparative sample. The study is relevant to researchers, policymakers, and educational institutions seeking data-driven insights into the mechanisms sustaining gender gaps in ICT and Engineering at the tertiary level. The results suggest that understanding and addressing these disparities may require discipline-level analysis and intervention rather than aggregate STEM-wide approaches.