Curriculum
22 subjects across six stages, 960 taught hours plus 90 hours of on-the-job training. Click any subject for its full module breakdown, labs, work scenario and assessment.
Stage A · Foundations
Weeks 1–6 · 240 h · Gate 1 — Foundations examinationProgramming, data, mathematics, databases, systems and the software discipline every AI role depends on.
Programming Foundations with Python
7 modules · Writes tested, modular, idiomatic Python for data and AI workloads.
Software Engineering, Git & Collaboration
4 modules · Collaborates on a shared codebase to professional standards.
Data Handling, Wrangling & Visualisation
4 modules · Turns raw, imperfect data into a trustworthy analysis table.
Mathematics & Statistics for AI
5 modules · Reasons quantitatively about models and evidence.
Databases & SQL for AI Systems
4 modules · Models, queries and tunes relational data for AI workloads.
Linux, Networking & Cloud Fundamentals
4 modules · Operates confidently on the systems AI workloads run on.
AI & Big Data Analytics — Industry Landscape
3 modules · Places AI work in industry and occupational context.
Product Engineering & SDLC Basics
4 modules · Works inside a delivery process and writes the documents it requires.
Stage B · Core AI
Weeks 7–12 · 240 h · Gate 2 — NOS 8121 / 8122 practicalClassical machine learning, deep learning, transformers, and the two statutory subjects that carry half the qualification weight.
Classical Machine Learning
6 modules · Frames, builds and validates supervised and unsupervised models without leakage.
Model Evaluation & Performance Engineering
5 modules · Measures, profiles and optimises a model to fit real system constraints, and documents the trade-off.
Deep Learning
5 modules · Trains, diagnoses and adapts neural networks.
Transformers & Representation Learning
3 modules · Explains and implements the architecture underlying modern language models.
Software Code Development for Model Deployment
5 modules · Turns a model into specified, tested, documented, deployable software.
Stage C · Applied GenAI
Weeks 13–18 · 240 h · Gate 3 — RAG/agent system with evaluationsLanguage models, retrieval-augmented generation, agents, evaluation and responsible AI — the layer employers hire for.
Large Language Models & Prompt Engineering
5 modules · Builds reliable, schema-valid applications on top of language models.
Retrieval-Augmented Generation
6 modules · Builds and measures a grounded retrieval system end to end.
AI Agents & Tool Orchestration
5 modules · Builds controllable, recoverable agents that act safely on real systems.
LLM Evaluation, Observability & Cost
6 modules · Proves whether an AI system is good enough to ship, with evidence.
Responsible AI, Security & Data Governance
5 modules · Ships AI that withstands attack, protects data and satisfies governance.
Stage D · Specialisation
Weeks 19–22 · 160 h · Gate 4 — deployed track projectOne of six role tracks, taken to production depth.
Stage E · Capstone & OJT
Weeks 23–24 · 170 h · Gate 5 — capstone defenceAn owned, defended, end-to-end system plus 90 hours of supervised industry work.
Stage P · Professional (parallel)
Weeks 1–24 · 90 h · Continuous evaluationEmployability, workplace conduct, inclusion and sustainability — delivered alongside technical stages.