Curtin University

What you'll study

The program focuses on core predictive analytics skills needed to derive insights and forecasts from data. Typical study areas include:

• Data preparation and management: data cleaning, feature engineering, handling missing data and working with structured and unstructured datasets.
• Statistical foundations for prediction: regression, classification, model selection and evaluation metrics.
• Machine learning and predictive modelling: supervised and unsupervised methods such as tree-based models, ensemble methods, clustering and basic neural networks.
• Programming and tools: practical use of languages and libraries commonly used in industry (for example Python or R) and use of databases and query tools.
• Data visualisation and communication: techniques for presenting predictive results and communicating uncertainty to stakeholders.
• Ethics and governance: considerations around data privacy, bias, reproducibility and responsible deployment of predictive models.

The Graduate Certificate is structured as a compact suite of postgraduate units that can be taken face-to-face or via flexible delivery modes, depending on availability. Assessment typically combines practical assignments, case studies and project work that mirror real-world datasets and business problems.

Entry requirements

• Australian bachelor degree or recognised equivalent in a relevant discipline (for example computing, mathematics, engineering, statistics, economics) OR substantial professional experience in a quantitative role may be considered.
• Applicants without a directly related degree but with significant relevant work experience should provide evidence of their experience and may be required to demonstrate prior quantitative skills.
• English language proficiency demonstrated by an accepted test or prior study in English if your previous qualifications were not taught in English.
• Meeting Curtin's admission policies and any program-specific prerequisites is required; contact Curtin’s admissions team for advice on eligibility and pathways if you are unsure.

Career prospects

Graduates gain practical skills that are immediately applicable across many sectors. Common roles pursued by alumni include:

• Data analyst or predictive analyst — developing and evaluating models to support decision-making.
• Business analyst or analytics consultant — translating predictive insights into business strategy.
• Machine learning practitioner (entry-level) — implementing models and pipelines in production settings.
• Industry-specific analytics roles — for example in finance, mining, health, marketing and government where forecasting and risk modelling are required.

The qualification is also used as a stepping-stone into further postgraduate study in data science, analytics or related research areas for those wanting deeper technical expertise.

Why study at Curtin University

• Applied, industry-focused teaching: Curtin emphasises practical learning with real-world case studies and assessments designed to build workplace skills.
• Flexible study modes: options for on-campus or online study suit working professionals seeking to upskill without pausing their careers.
• Access to expertise and resources: students benefit from experienced academic staff with industry connections and access to computing facilities, software and cloud-based tools used in analytics practice.
• Strong industry links: partnerships with local and national employers support networking, project opportunities and pathways to employment across sectors active in analytics.

For detailed entry guidance, unit availability and delivery modes, contact Curtin University’s course advisers or visit the Curtin website.