Python in Finance Education: Mapping Algorithmic Literacy Across Global Universities

Abstract This case study investigates how and why Python and AI/ML are being integrated into finance curricula across global higher education systems. Using a comparative, multi-scalar case study design spanning eleven regions and fifty-five institutions, the study triangulates program documentation with professional benchmarks to distinguish symbolic from substantive adoption. The analysis demonstrates that diffusion is heterogeneous rather than linear, produced by the interaction of labor-market signals, accreditation logics, national policy agendas, and local capacity. Frontier systems most prominently the United States and China/Hong Kong embed Python within core modules and laboratories, treating algorithmic literacy as baseline graduate competence. Near-frontier systems including parts of Europe, the United Kingdom, and Australia exhibit partial but institutionalized integration, while the GCC, South America, and Africa show fragmented, elective-driven patterns constrained by infrastructure and faculty readiness. Conceptually, the study reframes Python as curricular infrastructure rather than an ancillary tool; empirically, it provides a rubric for assessing depth, scope, and authenticity of integration; methodologically, it advances a case-centered approach attentive to geographic and institutional variation. The findings imply that durable reform requires program-level learning outcomes, staged pathways for heterogeneous cohorts, alignment with professional standards, and targeted capacity building to mitigate stratification in graduate employability. The study concludes that Python’s rise is reshaping the jurisdiction of finance education and that the distribution of capability will depend on the coherence of curriculum design, governance, and resources.