University of Melbourne

What you'll study

This programme combines core analytical methods, practical data engineering and business-focused decision-making. You will study statistical modelling, machine learning, optimisation, data management and visualisation, and the application of these techniques to solve real business problems.

• Core topics: foundations of business analytics, statistical inference for business, predictive modelling and machine learning for business, optimisation and prescriptive analytics.
• Data skills: database design and management, data wrangling, big-data technologies, and advanced visualisation using languages and tools such as Python, R and SQL.
• Context and practice: business strategy for analytics, ethical and governance issues in data science, and translating analytics insights for decision-makers.
• Capstone/industry project: an applied practicum or industry project working on a live business problem with an external partner, integrating technical and commercial skills.
• Electives and specialisations: options typically allow deeper study in areas such as marketing analytics, financial analytics, operations and supply-chain analytics, or healthcare analytics.

Entry requirements

Applicants are expected to hold a completed bachelor degree (or equivalent) from a recognised institution. Admission is competitive and prioritises applicants with strong quantitative and analytical preparation.

• Academic background: a good undergraduate degree in a quantitative discipline such as mathematics, statistics, engineering, computer science, economics or commerce, or demonstrable equivalently strong quantitative coursework.
• Technical skills: prior exposure to calculus, linear algebra and statistics is typically required; familiarity with programming (for example Python or R) and databases is highly recommended.
• Supporting evidence: applicants may be asked to provide academic transcripts, a statement of purpose outlining analytical experience and goals, and references.
• English language: applicants whose prior study was not in English must meet the university's English language requirements.
• Bridging or preparatory study: some applicants without the preferred quantitative background may be admitted on condition of completing preparatory coursework to build necessary skills before commencing advanced units.

Career prospects

Graduates are prepared for analytics roles that combine technical data capabilities with business insight. Common career paths include data scientist, business analyst, analytics consultant, data engineer, machine learning engineer and product or operations analyst.

• Employment sectors: consulting firms, financial services, technology companies, retail and e-commerce, healthcare and pharmaceuticals, government and not-for-profit organisations.
• Typical responsibilities: building predictive models, developing data pipelines, translating analytics into strategy and performance improvement, and delivering analytics-driven decision support.
• Further study and progression: graduates may move into senior analytics leadership, specialised research roles, or related fields such as quantitative finance and operational research.

Why study at University of Melbourne

The University of Melbourne offers this programme within a research-intensive environment with strong connections to industry. Students benefit from practical industry projects, access to research centres and multidisciplinary teaching that links analytics techniques to business problems.

• Industry engagement: structured capstone projects and industry partnerships provide direct workplace experience and networking opportunities with employers.
• Academic strengths: teaching draws on current research in machine learning, statistics and optimisation, delivered by academic staff with applied analytics expertise.
• Location and ecosystem: situated in Melbourne, the university offers proximity to a diverse economy and a growing technology and analytics community, aiding internship and employment opportunities.
• Student support: career services, workshops on professional skills, and mentoring help students prepare for technical interviews and transition into analytics roles.