Career and Portfolio Development in Data Science Apprentices

Traditional mathematics programmes have often emphasised subject knowledge over employability and soft skills, despite growing recognition from employers and professional bodies of the importance of these competencies, especially considering rapid workplace changes driven by AI. This paper explores the use of authentic assessment to support such skills through real-world tasks, focusing on a teaching block within the Data Scientist Degree Apprenticeship at the University of Nottingham. Designed to prepare final-year apprentices for their careers and the End Point Assessment (EPA), the block emphasised skills development, portfolio building, and readiness for a professional discussion. Survey data from two cohorts revealed that while the teaching block was positively received initially, confidence in EPA preparedness declined over time, particularly among apprentices earlier in their careers. These findings suggest a need for sustained support and have broader implications for embedding employability-focused, authentic assessment strategies in both degree apprenticeships and traditional mathematics curricula.