Graduate · Data Science
Master of Data Science and Innovation
University of Technology Sydney, Sydney, NSW, Australia
About this program
The Master of Data Science and Innovation is UTS's transdisciplinary flagship in this space: 96 credit points split across a core (44cp), specified data-science optional subjects (32cp) and genuinely open electives (20cp) that can be drawn from almost anywhere in the university. The pitch is less "learn to code models" and more "learn to run data science as an organisational capability," with subjects like Data Science for Innovation and Innovation Labs sitting alongside the expected statistics and machine learning content.
First year covers the technical spine (statistical thinking, machine learning algorithms, data science foundations), while second year shifts toward visualisation, narrative and an Innovation Labs subject where students work on real problems brought in by industry or government partners, often in small interdisciplinary teams rather than solo.
Because a third of the credit points are genuinely free electives, students frequently use the degree to pair data science technical skills with a domain they already know (health, law, design, business), which UTS positions as the actual differentiator versus a narrower straight machine-learning master's.
Cost & duration
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Course structure
6 terms of coursework
- Duration: 2 years full-time, 96 credit points
- Year 1: Data Science for Innovation, Statistical Thinking for Data Science, Machine Learning Algorithms and Applications, 3 electives
- Year 2: Data Visualisation and Narratives, Innovation Labs (industry-partnered team project), 4 electives
- Structure: 44cp core, 32cp specified data science optional subjects, 20cp open electives (any faculty)
- Capstone: Innovation Labs project with external industry or government partner
- No separate thesis requirement
- project-based capstone instead
Admissions
Relevant bachelor's degree; duration (1-2 years) depends on prior qualifications and relevant work experience
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