Curtin University

What you'll study

This professionally oriented masters combines core theory in statistics and machine learning with practical training in data engineering, programming and model deployment. Typical topics include statistical inference and regression, supervised and unsupervised machine learning, time-series and forecasting methods, big data processing, model validation and evaluation, and ethics and governance in analytics.

The course structure normally comprises a set of compulsory core units to establish foundational skills, a selection of elective units that allow specialisation (for example in business analytics, text analytics, or advanced machine learning), and a culminating capstone project or industry placement where you apply predictive techniques to a real dataset. Practical work emphasises programming in languages and tools commonly used in industry (such as Python and R), use of databases and cloud or high-performance computing resources, and communicating results to technical and non-technical stakeholders.

Typical modules

• Statistical Modelling and Inference
• Machine Learning and Data Mining
• Big Data Analytics and Data Engineering
• Time Series Analysis and Forecasting
• Data Visualisation and Communication
• Model Deployment, Monitoring and Governance
• Capstone Project or Industry Practicum

Entry requirements

Applicants are normally expected to hold an undergraduate degree in a relevant discipline such as computer science, information technology, mathematics, statistics, engineering, economics or a cognate business degree with substantial quantitative content. A strong quantitative or programming background is generally required. Applicants without a directly related degree but with relevant professional experience in analytics or data-intensive roles may be considered on a case-by-case basis.

Domestic and international applicants must demonstrate English language proficiency in line with Curtin's postgraduate requirements. Additional requirements can include submission of academic transcripts and a CV; referees or a personal statement may be requested for applicants seeking consideration on the basis of work experience.

Career prospects

Graduates are prepared for roles that design and deliver predictive solutions across industries. Common career paths include data scientist, predictive modeller, machine learning engineer, analytics consultant, business intelligence analyst, risk modeller and operations analyst. Employers span sectors such as finance, health, mining and resources, government, telecommunications and retail, where predictive analytics is used to improve decision-making, personalise services, manage risk and optimise operations.

The program's practical focus and industry-facing projects help develop the technical proficiency and communication skills employers seek, including the ability to translate modelling results into business recommendations and operational deployments.

Why study at Curtin University

Curtin offers a practice-led approach with strong links to industry and research in data science and computing. The university provides access to computing infrastructure and learning environments that support applied analytics work, and many units emphasise up-to-date tools and workflows used in professional settings. Curtin's collaborations with industry partners and research groups create opportunities for real-world projects, industry placement and networking.

Students benefit from multidisciplinary teaching that integrates statistical rigour, software engineering and business context, preparing graduates to work effectively in cross-functional teams. Flexible delivery options, including on-campus and supported online study, make the program accessible to a range of learners and professionals.