Curtin University

What you'll study

This graduate diploma focuses on the core tools and methods used to create predictive models from real-world data. The curriculum emphasises applied techniques, programming skills and the interpretation of model outputs for decision making.

• Statistical foundations and inference — probability, estimation, hypothesis testing and model diagnostics to support reliable predictive modelling.
• Programming for data science — practical training in languages commonly used for analytics such as Python and R, including data wrangling and reproducible workflows.
• Predictive modelling and machine learning — supervised learning methods (regression, classification), ensemble methods, model selection and validation techniques.
• Time series and forecasting — approaches for temporal data including ARIMA, exponential smoothing and modern forecasting frameworks.
• Data mining and big data technologies — techniques for extracting patterns from large and complex datasets, and an introduction to relevant data platforms and tools.
• Data visualisation and communication — presenting model results effectively for stakeholders, dashboard design and story-telling with data.
• Ethics, privacy and governance — legal and ethical considerations when using personal and sensitive data for predictive purposes.
• Applied project or capstone — an industry-linked or simulated project where students develop a predictive solution from problem definition through to evaluation and communication of results.

Entry requirements

Applicants are expected to hold a recognised bachelor degree or equivalent qualification. Degrees in quantitative disciplines such as mathematics, statistics, computer science, engineering, information technology, economics or related fields are preferred. Applicants with substantial relevant professional experience in analytics, programming or a related area may be considered.

International applicants must meet Curtin's English language requirements for postgraduate coursework; for many candidates this equates to a recognised test score at the postgraduate threshold. Additional prerequisites or bridging units may be required if an applicant's prior study did not include sufficient quantitative content.

Career prospects

Graduates are prepared for roles that use predictive methods to support operational or strategic decisions. Typical positions include:

• Data Analyst or Business Intelligence Analyst
• Predictive Modeller or Quantitative Analyst
• Machine Learning Practitioner (entry-level applied role)
• Data Scientist (as a pathway to deeper specialist roles with further experience or study)
• Risk Analyst, Forecasting Specialist or Operations Analyst in sectors such as finance, health, resources, retail and government

Skills developed in the programme—programming, model development, evaluation and stakeholder communication—are also valuable for consultancy roles and for professionals seeking to embed analytics into business processes.

Why study at Curtin University

Curtin has a strong practical and industry-oriented approach to data and analytics education. The university works with industry partners to keep curriculum aligned with current tools and workplace expectations, providing opportunities for applied projects and networking. Teaching is delivered by academic staff with research and professional experience in data science, predictive analytics and related disciplines, and students benefit from Curtin's computing and data facilities as well as its links across business, government and research sectors.

The program is suitable for those seeking a career change into analytics or for professionals wanting to upskill quickly with a focused, practice-based postgraduate credential.