Learners ensure they have the necessary Python background before starting the programme. This includes basic programming concepts, control flow, data structures, functions and Git workflows, either through a foundations course or a proficiency assessment.

Learners follow a nine‑week bridge module if they need reinforcement in Python, statistics, or leadership awareness. The module offers around 50 hours of targeted learning with weekly group mentoring to prepare for the professional master’s curriculum.

Learners move from business problems to data insights through exploratory data analysis, advanced visualisation and feature engineering. They start a self‑leadership inquiry into values, purpose and ethics, and learn core machine‑learning methods including regression, tree‑based models, SVMs, clustering, dimensionality reduction, interpretability and transparency.

Learners deepen their interpersonal leadership in group and organisational settings while studying deep learning and computer vision. They also work with large language models, covering NLP, transformer architectures, Hugging Face workflows and Retrieval Augmented Generation.

Learners focus on organisational leadership and designing interventions that create impact. They study responsible AI topics such as fairness, privacy and accountability, and learn ML Ops practices including cloud deployment, A/B testing, monitoring model performance, detecting data drift and retraining models.

Learners complete a work‑based Action Research project that addresses a real professional challenge. Supervision runs alongside the programme, and the final report and VIVAs demonstrate mastery of applied AI, leadership and practitioner research in a real organisational context.