Graduate · Data Science
Master of Data Science
RMIT University, Melbourne, VIC, Australia
About this program
RMIT's data science master's spends its first year deliberately narrow - solid computer science and statistics foundations - before opening into advanced electives and an industry or research project in year two. That sequencing is a response to a common complaint about data science master's programs elsewhere: students arriving from non-CS backgrounds (commerce, biology, social science) who get thrown into advanced machine learning units without the linear algebra or programming grounding to keep up. RMIT's structure front-loads that grounding instead.
The 192-credit-point, two-year full-time program includes a Graduate Diploma exit point at 96 credit points for students who need to leave partway through with something to show for it - a genuine safety net rather than a marketing line, since a lot of comparable programs offer no formal mid-point qualification at all. Case studies threaded through the coursework bring in practising data scientists to walk through legal, ethical and policy questions - algorithmic bias, data privacy law - alongside the purely technical content, which matters given how much scrutiny data science work now attracts.
Delivery mixes lectures, tutorials, and heavy practical lab time, with the year-two project functioning as the main portfolio piece graduates take into job interviews.
Cost & duration
English test scores
Course structure
3 terms of coursework
- Year 1: Programming for Data Science, Statistics for Data Science, Database Systems, Data Preprocessing and Wrangling, Introduction to Machine Learning, Data Visualisation
- Graduate Diploma exit point available at 96 credit points
- Year 2: Advanced Machine Learning, Big Data Processing, electives (e.g. Natural Language Processing, Deep Learning), Data Ethics and Policy, capstone industry or research project
Admissions
Bachelor's degree in a quantitative or IT-related discipline