Monash University Master of Data Science

What is Monash University Master of Data Science?

Monash University Master of Data Science is a 2 years, full-time master’s based at Clayton and focused on statistics, data engineering and machine learning. The standard route uses 96 credit points. The official international guide lists a 2026 annual fee baseline of AUD 55,700, approximately INR 38.3 lakh, per 48 points.

Its pathway combines computing foundations with statistical data modelling, large-scale processing, visualisation and a substantial industry or research project. The page plans for the longest published full-time entry level so a family does not accidentally budget only for an advanced-standing route. Monash says tuition changes by commencement year and can be adjusted each January, so the 2026 figure isn’t a fixed 2027 quote.

How much does Monash Master of Data Science cost?

The planning total is AUD 190,900, approximately INR 131.1 lakh. It combines the 2026 international tuition baseline, Monash’s published Melbourne living range, the AUD 2,500 Student visa charge from 1 July 2026 and an estimated OSHC allowance. Flights and housing deposits remain outside it.

OSHC is mandatory, but the final premium depends on provider, visa length and family composition. Flights, deposits, annual tuition increases and personal contingency remain outside this total. Australian dollar items use AUD 1 equal to INR 68.6849, the 9 September 2026 historical rate. The rupee total changes with the exchange rate.

“living costs can vary enormously”Monash University cost of living guide

Monash estimates AUD 30,000 to AUD 45,000 a year for a single international student. This plan uses the midpoint. The official guide says visa-length OSHC is required before the CoE can be issued.

Tuition uses AUD 55,700 per 48 credit points for 2026 and multiplies it across the 96-point route. Monash explicitly says the 2027 fee will differ and later years can rise, so AUD 111,400 isn’t a price guarantee.

Living costs use AUD 37,500 a year, the midpoint of Monash’s AUD 30,000 to AUD 45,000 range. Housing choice creates the largest movement, with shared accommodation and a private rental producing very different cash requirements.

At the dated rate, every AUD 10,000 is about INR 6.9 lakh. A five percent rupee fall adds roughly five percent to every unpaid Australian dollar item. Education loans should therefore include currency and annual-fee headroom.

Can an Indian applicant meet Monash Master of Data Science entry rules?

The course-specific screen is a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. Monash assesses an Indian qualification for Australian equivalence, so an applicant should not convert the published Monash percentage into a universal Indian cut-off.

An Indian transcript should make the a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics test easy to audit. It shouldn’t force an assessor to infer subject coverage. When titles differ, attach unit descriptions that show topics, assessed work and credit weight rather than expecting the course name to explain everything.

English Level A means IELTS 6.5 overall with no component below 6.0. PTE Academic uses 58 overall and 50 in each skill. Monash may accept other evidence, but the live rule and the offer letter control the decision.

The central risk is pathway assumption. A student who budgets for advanced standing before assessment can be short by half a year or more of tuition and living costs. Written credit confirmation belongs beside the offer.

Admission planning for FIT5212 Data analysis for semi-structured data begins by testing the published screen of a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. For Data analysis for semi-structured data, the transcript should identify preparation in statistics, data engineering and machine learning, assessed work and credit weight. This admissions evidence helps separate genuine prerequisite coverage from a similar-sounding course title. Reading Data analysis for semi-structured data beside Malicious AI identifies the sequence an assessor may expect.

Admission planning for FIT5230 Malicious AI begins by testing the published screen of a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. For Malicious AI, the transcript should identify preparation in statistics, data engineering and machine learning, assessed work and credit weight. This admissions evidence helps separate genuine prerequisite coverage from a similar-sounding course title. Reading Malicious AI beside Introduction to bioinformatics identifies the sequence an assessor may expect.

Admission planning for BMS5021 Introduction to bioinformatics begins by testing the published screen of a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. For Introduction to bioinformatics, the transcript should identify preparation in statistics, data engineering and machine learning, assessed work and credit weight. This admissions evidence helps separate genuine prerequisite coverage from a similar-sounding course title. Reading Introduction to bioinformatics beside Industry experience studio project identifies the sequence an assessor may expect.

Admission planning for FIT5120 Industry experience studio project begins by testing the published screen of a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. For Industry experience studio project, the transcript should identify preparation in statistics, data engineering and machine learning, assessed work and credit weight. This admissions evidence helps separate genuine prerequisite coverage from a similar-sounding course title. Reading Industry experience studio project beside Professional practice identifies the sequence an assessor may expect.

Admission planning for FIT5122 Professional practice begins by testing the published screen of a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. For Professional practice, the transcript should identify preparation in statistics, data engineering and machine learning, assessed work and credit weight. This admissions evidence helps separate genuine prerequisite coverage from a similar-sounding course title. Reading Professional practice beside Masters thesis part 1 identifies the sequence an assessor may expect.

Admission planning for FIT5126 Masters thesis part 1 begins by testing the published screen of a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. For Masters thesis part 1, the transcript should identify preparation in statistics, data engineering and machine learning, assessed work and credit weight. This admissions evidence helps separate genuine prerequisite coverage from a similar-sounding course title. Reading Masters thesis part 1 beside Masters thesis part 2 identifies the sequence an assessor may expect.

Admission planning for FIT5127 Masters thesis part 2 begins by testing the published screen of a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. For Masters thesis part 2, the transcript should identify preparation in statistics, data engineering and machine learning, assessed work and credit weight. This admissions evidence helps separate genuine prerequisite coverage from a similar-sounding course title. Reading Masters thesis part 2 beside Masters thesis final identifies the sequence an assessor may expect.

Admission planning for FIT5128 Masters thesis final begins by testing the published screen of a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. For Masters thesis final, the transcript should identify preparation in statistics, data engineering and machine learning, assessed work and credit weight. This admissions evidence helps separate genuine prerequisite coverage from a similar-sounding course title. Reading Masters thesis final beside Introduction to databases identifies the sequence an assessor may expect.

Admission planning for FIT9132 Introduction to databases begins by testing the published screen of a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. For Introduction to databases, the transcript should identify preparation in statistics, data engineering and machine learning, assessed work and credit weight. This admissions evidence helps separate genuine prerequisite coverage from a similar-sounding course title. Reading Introduction to databases beside Introduction to Python programming identifies the sequence an assessor may expect.

Admission planning for FIT9136 Introduction to Python programming begins by testing the published screen of a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. For Introduction to Python programming, the transcript should identify preparation in statistics, data engineering and machine learning, assessed work and credit weight. This admissions evidence helps separate genuine prerequisite coverage from a similar-sounding course title. Reading Introduction to Python programming beside Introduction to computer architecture and networks identifies the sequence an assessor may expect.

Admission planning for FIT9137 Introduction to computer architecture and networks begins by testing the published screen of a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. For Introduction to computer architecture and networks, the transcript should identify preparation in statistics, data engineering and machine learning, assessed work and credit weight. This admissions evidence helps separate genuine prerequisite coverage from a similar-sounding course title. Reading Introduction to computer architecture and networks beside Mathematical foundations for data science identifies the sequence an assessor may expect.

Admission planning for MAT9004 Mathematical foundations for data science begins by testing the published screen of a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. For Mathematical foundations for data science, the transcript should identify preparation in statistics, data engineering and machine learning, assessed work and credit weight. This admissions evidence helps separate genuine prerequisite coverage from a similar-sounding course title. Reading Mathematical foundations for data science beside Project management identifies the sequence an assessor may expect.

Admission planning for FIT5057 Project management begins by testing the published screen of a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics. For Project management, the transcript should identify preparation in statistics, data engineering and machine learning, assessed work and credit weight. This admissions evidence helps separate genuine prerequisite coverage from a similar-sounding course title. Reading Project management beside IT research methods identifies the sequence an assessor may expect.

Where a Master of Data Science application can fail

1. The exact pathway rule

   “a recognised bachelor degree at 60%, with the 1.5-year route requiring a cognate quantitative or IT degree plus programming, databases, algorithms, systems and mathematics”Monash course entry rule

   The admissions team applies this to the complete record.

2. A Monash percentage is not an Indian conversion table

   The published 60% or 65% is on Monash’s grading scale. Institution recognition, qualification level, marks and subject evidence are assessed together.

3. Advanced standing changes the financial plan

   A shorter Master of Data Science pathway should enter the budget only after Monash confirms it. The conservative plan here covers all 96 points.

How should an Indian applicant apply for Monash Master of Data Science?

Apply through the Monash international application route using course code C6004. Most course pages do not publish a fixed international closing date, so the plan is sequence-led and the live page must be checked before submission. Allow time to assemble subject evidence.

Select C6004, upload verified academic and English evidence, complete every course supplement, monitor the application, accept the exact offer and arrange OSHC before CoE issue.

The allowances are preparation estimates, not deadlines or processing times published by Monash University. Start dates remain unconfirmed.

Start with academic mapping because the Master of Data Science pathway determines both workload and cost. The application should state the exact course code, intended campus and intake. It shouldn’t rely on a broad subject label.

The Genuine Student response should explain why statistics, data engineering and machine learning follows from prior study and career evidence. Home Affairs asks four responses with a 150-word limit each and expects supporting evidence for the claims made.

After an offer, confirm duration, tuition deposit, conditions and credit in writing. Monash requires visa-length OSHC or approved evidence before issuing the CoE, and the CoE is needed for the Student visa application.

What jobs can follow Monash Master of Data Science?

Plausible directions include data science, analytics engineering, machine learning, data consulting and quantitative research. These are curriculum-based role families, not placement promises. An eligible Indian master’s graduate may receive a three-year Post-Higher Education Work stay under current Australia-India arrangements. Visa eligibility is assessed separately.

No programme-specific salary or placement rate is used for Master of Data Science. Role direction depends on skills, portfolio quality, prior experience, labour demand and employer visa decisions.

The strongest employment evidence is a course project tied directly to data science. It should expose the method, result and individual contribution rather than ask a reviewer to infer ability from Monash’s name or a list of software tools.

Students should choose units that build a coherent line from statistics, data engineering and machine learning to the final project, paper or placement. A scattered elective set won’t leave a clear claim to the role families listed above.

The Temporary Graduate visa is a time-limited work route, not employer sponsorship or permanent residence. Applicants must meet age, completion, health, character, insurance and application timing rules that apply when they graduate.

For the published directions of data science, analytics engineering, machine learning, data consulting and quantitative research, FIT5125 IT research methods contributes most clearly to analytics engineering. Employers can inspect a portfolio built from IT research methods; they can’t infer individual skill from the degree name. The assessment should record decisions, results and personal responsibility. A portfolio connecting IT research methods to Introduction to data science gives that evidence a programme-specific sequence.

For the published directions of data science, analytics engineering, machine learning, data consulting and quantitative research, FIT5145 Introduction to data science contributes most clearly to machine learning. Employers can inspect a portfolio built from Introduction to data science; they can’t infer individual skill from the degree name. The assessment should record decisions, results and personal responsibility. A portfolio connecting Introduction to data science to Data exploration and visualisation gives that evidence a programme-specific sequence.

For the published directions of data science, analytics engineering, machine learning, data consulting and quantitative research, FIT5147 Data exploration and visualisation contributes most clearly to data consulting. Employers can inspect a portfolio built from Data exploration and visualisation; they can’t infer individual skill from the degree name. The assessment should record decisions, results and personal responsibility. A portfolio connecting Data exploration and visualisation to Data wrangling gives that evidence a programme-specific sequence.

For the published directions of data science, analytics engineering, machine learning, data consulting and quantitative research, FIT5196 Data wrangling contributes most clearly to quantitative research. Employers can inspect a portfolio built from Data wrangling; they can’t infer individual skill from the degree name. The assessment should record decisions, results and personal responsibility. A portfolio connecting Data wrangling to Statistical data modelling gives that evidence a programme-specific sequence.

For the published directions of data science, analytics engineering, machine learning, data consulting and quantitative research, FIT5197 Statistical data modelling contributes most clearly to data science. Employers can inspect a portfolio built from Statistical data modelling; they can’t infer individual skill from the degree name. The assessment should record decisions, results and personal responsibility. A portfolio connecting Statistical data modelling to Data processing for big data gives that evidence a programme-specific sequence.

For the published directions of data science, analytics engineering, machine learning, data consulting and quantitative research, FIT5202 Data processing for big data contributes most clearly to analytics engineering. Employers can inspect a portfolio built from Data processing for big data; they can’t infer individual skill from the degree name. The assessment should record decisions, results and personal responsibility. A portfolio connecting Data processing for big data to Applied data analysis gives that evidence a programme-specific sequence.

For the published directions of data science, analytics engineering, machine learning, data consulting and quantitative research, FIT5149 Applied data analysis contributes most clearly to machine learning. Employers can inspect a portfolio built from Applied data analysis; they can’t infer individual skill from the degree name. The assessment should record decisions, results and personal responsibility. A portfolio connecting Applied data analysis to Machine learning gives that evidence a programme-specific sequence.

For the published directions of data science, analytics engineering, machine learning, data consulting and quantitative research, FIT5201 Machine learning contributes most clearly to data consulting. Employers can inspect a portfolio built from Machine learning; they can’t infer individual skill from the degree name. The assessment should record decisions, results and personal responsibility. A portfolio connecting Machine learning to Data analysis for semi-structured data gives that evidence a programme-specific sequence.

For the published directions of data science, analytics engineering, machine learning, data consulting and quantitative research, FIT5212 Data analysis for semi-structured data contributes most clearly to quantitative research. Employers can inspect a portfolio built from Data analysis for semi-structured data; they can’t infer individual skill from the degree name. The assessment should record decisions, results and personal responsibility. A portfolio connecting Data analysis for semi-structured data to Malicious AI gives that evidence a programme-specific sequence.

For the published directions of data science, analytics engineering, machine learning, data consulting and quantitative research, FIT5230 Malicious AI contributes most clearly to data science. Employers can inspect a portfolio built from Malicious AI; they can’t infer individual skill from the degree name. The assessment should record decisions, results and personal responsibility. A portfolio connecting Malicious AI to Introduction to bioinformatics gives that evidence a programme-specific sequence.

For the published directions of data science, analytics engineering, machine learning, data consulting and quantitative research, BMS5021 Introduction to bioinformatics contributes most clearly to analytics engineering. Employers can inspect a portfolio built from Introduction to bioinformatics; they can’t infer individual skill from the degree name. The assessment should record decisions, results and personal responsibility. A portfolio connecting Introduction to bioinformatics to Industry experience studio project gives that evidence a programme-specific sequence.

For the published directions of data science, analytics engineering, machine learning, data consulting and quantitative research, FIT5120 Industry experience studio project contributes most clearly to machine learning. Employers can inspect a portfolio built from Industry experience studio project; they can’t infer individual skill from the degree name. The assessment should record decisions, results and personal responsibility. A portfolio connecting Industry experience studio project to Professional practice gives that evidence a programme-specific sequence.

How is Monash ranked for Master of Data Science?

Monash is 31st overall in QS 2027. The closest published subject signal for this course is Data Science and AI at #48 globally in QS 2025. Rankings compare institutions or broad subjects and do not predict admission, teaching fit, salary or migration outcomes.

Who is Monash Master of Data Science actually for?

It suits applicants who clear the exact pathway screen, can turn statistics, data engineering and machine learning into assessed evidence and can fund INR 131.1 lakh without assumed earnings. It is a weak fit when a guaranteed job, scholarship or advanced-standing decision is essential to affordability.

Good fit joins preparation, output and finance. The transcript supports the early units, the later curriculum produces an inspectable assessment matched to a chosen career direction, and the cash plan survives the full published route.

A negative decision is sensible when the pathway screen conflicts with the applicant’s evidence or when the desired role has little connection to statistics, data engineering and machine learning. Reputation doesn’t remove either problem.

Applicants from India are automatically considered for eligible Monash international study grants, currently described in AUD 5,000, 10,000 or 15,000 tiers. The INR 131.1 lakh plan assumes no award until it appears in writing.

The fit decision can include FIT5122 Professional practice for a 2-year Clayton plan priced from AUD 55,700 per 48 points. The unit earns its place when it advances statistics, data engineering and machine learning towards data consulting. A plan dependent on unconfirmed credit or employment is not financially resilient. The cost case for Professional practice is stronger when Masters thesis part 1 supports the same role direction.

The fit decision can include FIT5126 Masters thesis part 1 for a 2-year Clayton plan priced from AUD 55,700 per 48 points. The unit earns its place when it advances statistics, data engineering and machine learning towards quantitative research. A plan dependent on unconfirmed credit or employment is not financially resilient. The cost case for Masters thesis part 1 is stronger when Masters thesis part 2 supports the same role direction.

The fit decision can include FIT5127 Masters thesis part 2 for a 2-year Clayton plan priced from AUD 55,700 per 48 points. The unit earns its place when it advances statistics, data engineering and machine learning towards data science. A plan dependent on unconfirmed credit or employment is not financially resilient. The cost case for Masters thesis part 2 is stronger when Masters thesis final supports the same role direction.

The fit decision can include FIT5128 Masters thesis final for a 2-year Clayton plan priced from AUD 55,700 per 48 points. The unit earns its place when it advances statistics, data engineering and machine learning towards analytics engineering. A plan dependent on unconfirmed credit or employment is not financially resilient. The cost case for Masters thesis final is stronger when Introduction to databases supports the same role direction.

The fit decision can include FIT9132 Introduction to databases for a 2-year Clayton plan priced from AUD 55,700 per 48 points. The unit earns its place when it advances statistics, data engineering and machine learning towards machine learning. A plan dependent on unconfirmed credit or employment is not financially resilient. The cost case for Introduction to databases is stronger when Introduction to Python programming supports the same role direction.

The fit decision can include FIT9136 Introduction to Python programming for a 2-year Clayton plan priced from AUD 55,700 per 48 points. The unit earns its place when it advances statistics, data engineering and machine learning towards data consulting. A plan dependent on unconfirmed credit or employment is not financially resilient. The cost case for Introduction to Python programming is stronger when Introduction to computer architecture and networks supports the same role direction.

The fit decision can include FIT9137 Introduction to computer architecture and networks for a 2-year Clayton plan priced from AUD 55,700 per 48 points. The unit earns its place when it advances statistics, data engineering and machine learning towards quantitative research. A plan dependent on unconfirmed credit or employment is not financially resilient. The cost case for Introduction to computer architecture and networks is stronger when Mathematical foundations for data science supports the same role direction.

The fit decision can include MAT9004 Mathematical foundations for data science for a 2-year Clayton plan priced from AUD 55,700 per 48 points. The unit earns its place when it advances statistics, data engineering and machine learning towards data science. A plan dependent on unconfirmed credit or employment is not financially resilient. The cost case for Mathematical foundations for data science is stronger when Project management supports the same role direction.

The fit decision can include FIT5057 Project management for a 2-year Clayton plan priced from AUD 55,700 per 48 points. The unit earns its place when it advances statistics, data engineering and machine learning towards analytics engineering. A plan dependent on unconfirmed credit or employment is not financially resilient. The cost case for Project management is stronger when IT research methods supports the same role direction.

The fit decision can include FIT5125 IT research methods for a 2-year Clayton plan priced from AUD 55,700 per 48 points. The unit earns its place when it advances statistics, data engineering and machine learning towards machine learning. A plan dependent on unconfirmed credit or employment is not financially resilient. The cost case for IT research methods is stronger when Introduction to data science supports the same role direction.

The fit decision can include FIT5145 Introduction to data science for a 2-year Clayton plan priced from AUD 55,700 per 48 points. The unit earns its place when it advances statistics, data engineering and machine learning towards data consulting. A plan dependent on unconfirmed credit or employment is not financially resilient. The cost case for Introduction to data science is stronger when Data exploration and visualisation supports the same role direction.

The fit decision can include FIT5147 Data exploration and visualisation for a 2-year Clayton plan priced from AUD 55,700 per 48 points. The unit earns its place when it advances statistics, data engineering and machine learning towards quantitative research. A plan dependent on unconfirmed credit or employment is not financially resilient. The cost case for Data exploration and visualisation is stronger when Data wrangling supports the same role direction.

What does Monash Master of Data Science cover?

The current Monash Handbook publishes the units below across foundation, core, specialist, project and elective choices. The table includes every coded unit named in the course requirements, not a sample shortlist. It is a catalogue rather than one student’s study plan.

The note “Published core, option or project unit” applies to 21 components in this table.

• The conservative route contains 96 credit points over 2 years full time.
• Most units carry 6 points unless the Handbook states otherwise.
• Elective availability, prerequisites, sequencing and campus delivery remain controlled by the current Handbook and timetable.

The structure should be read as a set of routes rather than a promise that every listed option can fit one study plan. Prerequisites, specialisation rules and the choice between industry and research components narrow the actual enrolment set.

A coherent Master of Data Science plan begins with the target output and works backwards. Units should combine into a defensible final project for the chosen role direction while satisfying all compulsory points and progression rules.

Should an Indian applicant shortlist Monash Master of Data Science?

Shortlist Master of Data Science when prior study clears its exact academic screen and the planned output is a defensible analysis with provenance, validation, uncertainty and decision consequences made explicit. The full-programme budget here is INR 131.1 lakh with no scholarship or part-time earnings assumed.

The decision should turn on fit between prior evidence and statistics, data engineering and machine learning. Monash publishes a flexible route, but the degree doesn't create value unless unit choices lead to a coherent assessed output that an employer or later researcher can inspect.

The strongest reason not to apply is specific. A broad science or business degree does not automatically earn advanced standing. The shorter route depends on specific computing and mathematics content, not on the applicant's job title. A ranking can't repair that mismatch or make an unaffordable plan safe.

Sources

These sources support the programme, admission, cost, experience and immigration information used on this page.

Sources checked on September 11, 2026. This page uses February and July 2027 only as a planning cycle. The university has not published dates for that cycle.