A full-stack data degree combining statistical reasoning, analytics, data engineering, experimentation, machine learning and decision communication. Students master the common data-science core and then choose advanced topics that match their intended industry or research direction.
360+ credits (minimum award threshold)1 matching listed module optionPrerequisite-driven progression
Award requirements
Required Core: Complete all 18 · 180 credits.
Advanced Choice: Choose at least 6 from 12 listed options · 60 credits.
Elective: Choose at least 3 from 24 listed options · 30 credits.
Applied Practice: Complete all 3 · 30 credits.
Capstone: Complete all 2 · 60 credits.
32 completed modules in the standard pathway. Additional approved study is permitted; listed options are not all compulsory.
Capstone expectation: An end-to-end data product, causal study or applied research project with reproducible data lineage, justified methods, evaluation, stakeholder-facing outputs and a formal defence.
Self-paced. Individual prerequisites, year/semester assignments and fixed durations are not specified in this catalogue. Cross-faculty selections require approval.
Explore modules ↗
DAT-302 / Advanced choice
Bayesian Modelling
Credits
10
Programme requirement
Optional selection
Prerequisites
Prerequisites apply. Individual module prerequisite links and minimum marks are not specified in this catalogue.
Entry, enrolment and progression remain subject to academic review. Recorded qualification levels describe the academic design and do not independently establish accreditation or jurisdiction-specific recognition.
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AIAIU Academic Catalogue
Qualifications, modules and electives. Practical study and capstones, where recorded. Explore the credits, prerequisites and possibilities before you apply.