🎓 MS in AI & Data Engineering
A 36‑credit, STEM‑designated programme designed for engineers, policy professionals and technologists who architect the data foundations of artificial intelligence — with a strong ethical and governance perspective unique to the Fellowship.
data for public trust · AI policy · ethical data engineering
core AI & data engineering modules
All students complete 7 core courses (21 credits) that blend modern data engineering with AI systems and policy-aware design.
Data Engineering Concepts & Lifecycle
Data acquisition, reduction, integration, quality, and mutability. Foundations of the data engineering lifecycle [citation:2].
Scalable Data Infrastructures
Distributed storage, query processing, parallelism, cloud platforms (AWS/GCP). Batch and stream processing [citation:3].
Machine Learning & MLOps Foundations
Supervised learning, neural networks, model lifecycle, deployment and monitoring pipelines [citation:1][citation:4].
Data Engineering for AI Systems
Designing intelligent data ecosystems: feature stores, data lineage, real‑time integration for AI/ML [citation:9].
AI Governance, Ethics & Data Policy
Responsible data engineering, privacy enhancing tech, GDPR/regulatory compliance, algorithmic fairness [citation:4][citation:6].
Cloud & Enterprise Data Platforms
Cloud-native analytics, BigQuery, Snowflake, data mesh; deploying scalable data workflows [citation:7].
Applied Generative AI & LLMOps
LLM pipelines, RAG, fine‑tuning, prompt orchestration, and monitoring in production [citation:4][citation:7].
elective modules (choose 4, 12 credits)
Advanced Data Engineering Technologies
Table representation, semantic annotation, graph analysis, data integration at scale [citation:3].
ML Systems & Architecture
Hardware/software co-design, GPU/TPU optimisation, model serving.
Time Series & Geospatial AI
Forecasting, spatio‑temporal data, GIS integration [citation:7].
Natural Language Processing with LLMs
Transformers, BERT, GPT architectures, parameter‑efficient tuning.
specialization tracks
students select one 12‑credit specialization (4 advanced courses) to tailor the degree toward their career goals.
⚙️ Data Engineering & MLOps track
focus: production-grade pipelines, model deployment, data observability.
- Data Engineering Technologies – distributed processing, performance [citation:3]
- MLOps: Model Lifecycle Management – CI/CD for ML, monitoring, drift [citation:4]
- Streaming Data & Real‑time AI – Kafka, Flink, Spark streaming
- Enterprise Cloud Analytics – GCP/Azure, infrastructure as code [citation:7]
🧠 AI Engineering & LLMs track
focus: building applications with foundation models, RAG, and agentic systems.
- Applied Generative AI for Enterprises – LLM patterns, fine‑tuning [citation:4]
- Designing LLM Applications – RAG, agents, evaluation [citation:4]
- Deep Learning & Computer Vision – CNNs, transformers, multimodal
- AI Agent Design & Deployment – AutoGPT, LangGraph, safety [citation:6]
🏛️ Policy, Governance & Responsible AI track
focus: data ethics, AI regulation, public sector technology leadership.
- AI Governance, Ethics & Sustainability – risk frameworks, OMB/EU AI Act [citation:4]
- Data Governance, Privacy & Law – GDPR, CCPA, privacy engineering [citation:6]
- International Data Policy & Digital Trade – cross‑border data flows
- Technology & Global Development – inclusive AI, data justice
programme structure (18 months – full‑time track)
- 📘 Data Engineering Concepts [citation:2]
- 📘 Machine Learning Foundations
- 📘 Python for Data & AI
- ➕ Policy Bootcamp (0‑credit)
- 📘 Scalable Data Infrastructures
- 📘 AI Governance & Ethics
- 📘 Cloud & Enterprise Platforms
- 📘 Elective 1
- 📘 Data Engineering for AI Systems [citation:9]
- 📘 Applied GenAI / LLMOps
- 📘 Elective 2
- 📘 Elective 3
- 📘 Capstone / Thesis Project
- 📘 Elective 4
- 📘 Professional seminar
detailed syllabus example: “Data Engineering Concepts”
course code: GPTE 6201 · 3 credits · pre‑req: Python, SQL fundamentals
course description: Explores the data engineering lifecycle — from data acquisition to visualization. Covers data modelling, integration, quality, security, and the role of data engineering in AI systems [citation:2].
📚 weekly topics:
- Data acquisition & reduction
- Understanding shape of data
- Data modelling & NoSQL storage
- Data integration & ETL/ELT
- Data profiling, quality & cleaning
- Data security & dissemination
- Querying & optimization
- Data analytics (ML/DL)
- Data visualisation & serving
- Data mutability & robustness
recommended text: Reis & Housley, "Fundamentals of Data Engineering" (O‘Reilly) [citation:2].
“Data Engineering Technologies” – modules at a glance [citation:3]
- storage for scalability (file systems, indexing)
- query processing & parallelism
- batch, interactive, streaming platforms
- graph DB & graph analytics
- semantic table interpretation
- entity alignment & data integration
📌 capstone / thesis option
All students complete a 3‑credit capstone (or 6‑credit thesis) with real‑world data engineering projects — often sponsored by Fellowship partners in government, NGO, and tech policy. Emphasises responsible AI and secure data pipelines [citation:6][citation:10].