A master’s degree for a data-driven world
A 36-credit M.Sc. connecting Data Science, Artificial Intelligence and Data Engineering with real problems—structured to work alongside a professional life.
The capability gap
Many organisations already have dashboards, cloud platforms and AI tools, yet still need people who can connect business questions, data quality, models and deployable systems.
Frame the decision before choosing data or models.
02Build evidence that can be examined—not merely impressive output.
03Design systems that others can use, operate and extend.
Learning that becomes capability
DIAI uses outcome-based, case-based and project-based learning with authentic assessment—from framing the question to delivering a defensible system or research result.
Move from fragmented data to recommendations people can understand and act on.
Frame business questions, audit data quality, analyse statistically, design metrics and visualisations, and communicate without hiding uncertainty or limitations.
Move from a predictive model to evidence showing when it is useful—and where it is not.
Build baselines, features and models systematically; audit leakage, imbalance, calibration and error patterns; compare approaches and communicate responsibly.
Move from “it runs on my machine” to data systems other teams can trust, operate and extend.
Design data contracts and architecture; build pipelines with validation, tests, version control, orchestration, CI/CD, observability and failure handling.
Credit Banking · Modular learning
Each pathway combines nine 1-credit OBEM modules: a shared data foundation followed by analytics, machine learning or data operations. The modular structure makes outcomes visible and sequenceable.
Banking, transfer or recognition of credit remains subject to admission, assessment, time-limit and university regulations. Confirm official requirements before planning.
DIA 60401 · 60402 · 60403
DIA 60501 · 60502 · 60503
DIA 61201 · 61202 · 61203
DIA 60401 · 60402 · 60403
DIA 60501 · 60502 · 60503
DIA 60601 · 60602 · 60603
DIA 60401 · 60402 · 60403
DIA 60601 · 60602 · 60603
DIA 62101 · 62102 · 62103
Example coursework
These examples illustrate work aligned with the learning outcomes; they do not guarantee that identical briefs will run every semester.
Start with imperfect business data, prepare and analyse it, build a dashboard, then present a strategic recommendation with explicit data limitations.
Compare a baseline with machine-learning models; audit leakage, imbalance and calibration; then defend the model-selection decision.
Design a multi-source pipeline with validation, logging, automated tests and a deployment workflow suitable for operational use.
Frame a problem, select a method, build a prototype, evaluate it and communicate the findings through a thesis or independent study.
Who this may suit
The program welcomes domain perspectives from different fields, while graduate study still requires time, consistency and readiness for analytical work.
Two ways to complete the degree
Plan 1 · Academic
For learners who want to develop a research question, methodology, analysis and new knowledge rigorously.
Required 15 · Electives 9 · Thesis 12Plan 2 · Professional
For learners who want to develop and evaluate a practical system, prototype or solution in a real context.
Required 15 · Electives 15 · Independent study 6Take the next step
