Master's · Computing
M.S. Data Science & AI
Master's degree36 semester credit hours · 12 coursesAmerican-style curriculum · cohort-based, facilitator-led
The M.S. moves from applied machine learning into the engineering and judgement that senior data work requires: building and evaluating models honestly, deploying them responsibly, and leading data work inside organisations.
It is designed for graduates of computing, mathematics or adjacent fields, and for professionals whose work has grown into data. Cohorts keep the pace; a graduate project produces real, shown work.
What the curriculum covers
Indicative areas of study; the full course-by-course catalogue will be published before enrolment opens.
- Applied machine learning and deep learning
- Data engineering at scale
- Large language models and applied AI systems
- Experiment design, evaluation and statistics
- MLOps: deploying and maintaining models
- AI governance, safety and ethics
Capstone
A substantial applied project, built inside the cohort and defended before faculty.
- A real dataset and question, carried from framing to deployed result
- Written and oral defence in English
How you will study
Every course is cohort-based and facilitator-led.
- You join a cohort — a class that moves through the programme together
- A trained facilitator leads a structured weekly rhythm of sessions, exercises and homework
- Course content, assessments and rubrics are designed by credentialed professors
- Materials are downloadable in advance, so an interrupted connection does not cost you the week
Admission
- A bachelor's degree from an appropriately accredited institution
- English proficiency sufficient for graduate study
- No GMAT or GRE