Students may sit in the same lecture rooms, study the same degree field and live in the same cities, but their financial situation can be very different. For domestic and international students in Australia, affordability is shaped by more than tuition alone. Rent, living costs, work-hour limits and income potential all interact with student type.
This report compares selected Master of Data Science courses in Melbourne, Sydney and Brisbane .Master of Data Science was used as a comparable case study because similar postgraduate data-related programs are offered across the selected cities and are commonly marketed to both domestic and international students. Whereas these cities were chosen because they are major student destinations and have large rental markets. The focus is not simply to show that international students pay more. That is already obvious. The stronger question is how tuition, rent, living costs and capped work income combine to create different affordability pressures for students following a similar study pathway.
The analysis uses a scenario-based approach. It combines selected university fee data, Domain rental data, Study Australia living-cost estimates, Fair Work minimum-wage information and Australian Government international student work-rights information. The results should not be read as exact personal budgets. Instead, they provide a consistent comparison of affordability across student types, universities and cities.
The five charts build the story step by step. The first chart compares tuition fees. The second chart adds rent pressure. The third chart shows how work hours affect possible income. The fourth chart combines tuition and living costs into a total annual burden. The final chart compares international students with the lowest available domestic scenario at the same university.
Together, the charts show that affordability is not caused by one factor alone. The pressure comes from the combined effect of fees, housing costs and limited income.
This report uses a scenario-based affordability model. Annual tuition fees were collected from official university course pages for selected Master of Data Science or equivalent programs in Melbourne, Sydney and Brisbane. Rental pressure uses Domain’s March 2026 median weekly unit rent figures. Living costs use Study Australia’s shared-house living-cost estimate. Income scenarios are calculated using the national minimum wage and selected weekly work-hour assumptions, including the 24-hour international student term-time work limit.
The main calculation used in the report is:
Annual cost = annual tuition + estimated annual living cost
The income benchmark is:
24-hour annual gross income = 24 hours per week × national minimum wage × 52 weeks
The results are designed to compare scenarios consistently. They should not be interpreted as exact personal budgets for every student.
The first chart compares annual Master of Data Science tuition fees for selected universities in Melbourne, Sydney and Brisbane. It separates domestic CSP, domestic full-fee or estimated fees, and international fees because these are not the same type of payment.
The pattern is clear. International tuition is generally higher than domestic study scenarios across the selected universities. However, the size of the gap is not identical everywhere. Some universities show a smaller difference between international and domestic full-fee scenarios, while others show a much larger gap when compared with domestic CSP fees.
This matters because tuition is the starting point of the affordability difference. Before rent, food, transport or other living expenses are even added, student type already changes the financial position of the student.
Tuition is only one part of the story. Students also need somewhere to live, and rent can take up a large share of student income. The second chart compares median weekly unit rent in Melbourne, Brisbane and Sydney with estimated gross weekly income from working 24 hours at the national minimum wage.
The 24-hour benchmark is important because it reflects the international student term-time work limit. The chart shows that median weekly unit rent can absorb most or more than all of this gross weekly income. In Sydney and Brisbane, the rent amount is higher than the 24-hour income benchmark. In Melbourne, it is almost equal.
This means that even before tuition or other living costs are considered, housing can already consume a major part of capped student earnings.
The third chart shows estimated gross annual income under three work-hour scenarios: 15 hours per week, 24 hours per week and 38 hours per week. This helps explain why the work-hour limit matters.
At the national minimum wage, the 24-hour student visa cap produces an annual gross income of about $31,000. A full-time benchmark is much higher, but international students cannot rely on full-time work during study periods. The income shown is also before tax, so it is an optimistic estimate rather than guaranteed take-home income.
This chart shows that student affordability is not only about how much things cost. It is also about how much income students can realistically access while studying.
The fourth chart combines annual tuition with Study Australia’s estimated annual living costs. The stacked bars separate tuition from living costs, while the dashed line shows estimated annual gross income from working 24 hours per week at the national minimum wage.
This chart makes the affordability pressure more visible. Living costs alone are already close to the annual income benchmark in many cases. Once tuition is added, total annual costs move well beyond what capped minimum-wage income can cover.
International scenarios are especially exposed because they combine higher tuition with the same city-level living costs. However, the chart also shows that some domestic full-fee scenarios are still expensive. This is why the comparison needs to separate domestic CSP and domestic full-fee situations instead of treating all domestic students as one group.
The final chart compares the international total annual cost with the lowest available domestic total annual cost at the same university. The gap is shown in dollars and converted into equivalent weeks of gross income from 24 hours of work per week.
This chart gives the clearest answer to the overall story. The international affordability gap is not the same across universities. Some universities show a much larger gap between international and domestic students, while others show a smaller difference. This means that the financial split is not only about the city a student lives in. It is also shaped by the fee structure attached to student type at the same institution.
The gap is largest where the domestic comparison is a CSP place and the international fee is high. This does not mean one university is universally less affordable than another; it means the difference between domestic and international fee structures is larger under this specific scenario. The chart is therefore best read as a same-campus affordability gap, not a general university ranking.
The chart directly supports the idea of “same campus, different cost.” Two students may study at the same university, but the international student can face a much larger annual cost burden.
The main finding is that affordability pressure is produced by the interaction of tuition, rent, living costs and capped income, not by any single cost alone. International students usually face higher tuition, but the larger issue is how tuition combines with rent, living costs and limited work income.
For domestic students, Commonwealth Supported Places can reduce the annual burden substantially. However, domestic full-fee scenarios can still create serious affordability pressure. For international students, higher tuition combines with the same city-level living costs and a capped term-time income benchmark. This creates a larger financial gap, even when students are studying the same degree field in the same city.
The strongest finding is that capped minimum-wage income does not fully cover the annual cost scenarios shown in this report. Rent already takes up a large share of weekly income, living costs remain substantial, and tuition determines how far each scenario moves beyond the income benchmark. Same campus does not mean same cost.
##Limitations
This report uses selected Master of Data Science courses in Melbourne, Sydney and Brisbane. It is not a complete ranking of all Australian universities, all courses or all student experiences. University fees can change each year, and domestic fee structures are not identical across institutions.
The income estimates are based on gross minimum-wage earnings. They do not account for tax, unpaid breaks, casual loading differences, award rates, job availability or individual work patterns. The living-cost estimates are based on Study Australia’s shared-house scenario and may not match every student’s actual spending. Rent also varies by suburb, housing quality, lease type and whether students share a room or property.
Despite these limitations, the comparison is useful because the same assumptions are applied across all scenarios. The purpose is not to predict one student’s exact budget, but to show how affordability pressure changes when tuition, living costs, rent and work-hour limits are considered together.
The analysis uses selected open and publicly available sources. University tuition fees were collected from individual university course pages for selected Master of Data Science or equivalent programs. Rental data uses Domain’s March 2026 median weekly unit rent figures for selected capital cities. Living-cost estimates use Study Australia’s cost-of-living calculator, based on the shared-house scenario. Income scenarios are calculated using the national minimum wage and selected weekly work-hour assumptions.
Because the data sources use different structures, some values were standardised into annual estimates to support comparison. The results should be interpreted as scenario-based affordability comparisons rather than exact student budgets.
The five visualisations were developed in R and published through RPubs. Each chart includes basic interactivity through hover tooltips, which provide details such as university, fee category, tuition, living cost, rent, income benchmark or affordability gap.
Charts 1, 2, 4 and 5 are multivariate. Chart 1 combines city, university, student type, fee category and tuition. Chart 2 combines city, rent, income benchmark, rent share of income and annual rent growth. Chart 4 combines city, university, student type, fee category, tuition, living cost and income benchmark. Chart 5 combines city, university, domestic fee category, international cost, domestic comparison cost and work-week equivalent gap.
I acknowledge the use of OpenAI’s ChatGPT to support grammar, wording, code troubleshooting and report structure. All data selection, interpretation, final visualisation decisions and final written content were reviewed and edited by me. The charts were created in R using the cited data sources.
Australian Government Department of Education. (2026). The rights of international students at work. Retrieved from https://www.education.gov.au/international-education/support-international-students/rights-international-students-work
Australian Government Department of Education. (2026). Funding clusters and indexed rates. Retrieved from https://www.education.gov.au/higher-education-loan-program/resources/2026-indexed-rates
Domain. (2026). Rental report: March quarter 2026. Retrieved from https://www.domain.com.au/research/rental-report/march-2026/
Fair Work Ombudsman. (2026). Minimum wages. Retrieved from https://www.fairwork.gov.au/pay-and-wages/minimum-wages
Monash University. (2026). Data Science - C6004. Retrieved from https://www.monash.edu/study/courses/find-a-course/data-science-c6004
OpenAI. (2026). ChatGPT. Retrieved from https://chatgpt.com/
Queensland University of Technology. (2026). Master of Data Science. Retrieved from https://www.qut.edu.au/courses/master-of-data-science
RMIT University. (2026). Master of Data Science. Retrieved from https://www.rmit.edu.au/study-with-us/levels-of-study/postgraduate-study/masters-by-coursework/master-of-data-science-mc267
Study Australia. (2025). Cost of living calculator. Retrieved from https://costofliving.studyaustralia.gov.au/
The University of Melbourne. (2026). Master of Data Science. Retrieved from https://study.unimelb.edu.au/find/courses/graduate/master-of-data-science/
The University of Queensland. (2026). Master of Data Science. Retrieved from https://study.uq.edu.au/study-options/programs/master-data-science-5660
The University of Sydney. (2026). Master of Data Science. Retrieved from https://www.sydney.edu.au/courses/courses/pc/master-of-data-science0.html
UNSW Sydney. (2026). Master of Data Science and Decisions. Retrieved from https://www.unsw.edu.au/study/postgraduate/master-of-data-science-and-decisions