Curtin University

What you'll study

This degree builds core foundations in mathematics and statistics, programming and computational thinking, then applies those foundations to data-focused topics. Early years typically cover calculus and linear algebra, probability and statistics, introduction to programming (Python and/or R), databases and data structures.

• Core data science topics: data wrangling, exploratory data analysis, statistical inference, supervised and unsupervised machine learning, model evaluation and validation.
• Computing and systems: algorithms, database design and SQL, software engineering principles, cloud computing basics and scalable data processing.
• Applied and contextual units: data visualisation, ethics and data governance, domain applications such as business analytics, health informatics or mining analytics.
• Capstone and project work: an industry-linked or research-led capstone project in the final year that emphasises an end-to-end data science workflow from problem definition through to deployment and communication of results.
• Electives and flexibility: options to take electives or a minor in areas such as artificial intelligence, cyber security, geographic information systems, or a second discipline within the sciences.

The degree mixes lectures, practical computer-lab sessions, workshops and project-based assessments to develop both theoretical understanding and hands-on skills with tools and libraries commonly used in industry.

Entry requirements

Applicants are generally expected to have completed secondary school qualifications equivalent to the Australian Year 12. Successful applicants will typically have studied mathematics to a level that demonstrates competence in algebra and calculus (for example, Mathematics Methods or an equivalent). Curtin also accepts a range of international qualifications and recognised vocational pathways.

• Academic: completion of a recognised secondary qualification at the required level for undergraduate admission; competitive applicants present strong results in mathematics-related subjects.
• English language: evidence of English proficiency is required for applicants whose prior education was not delivered in English (for example, recognised tests or equivalent institutional evidence).
• Pathways: applicants without direct entry qualifications may be eligible via Curtin College, enabling programs, or recognised diplomas and bridging courses.

Specific entry thresholds, unit prerequisites and recognised international equivalents vary by applicant background; prospective students should consult Curtin's admissions information or contact the university for personalised guidance.

Career prospects

Graduates develop a mix of analytical, programming and communication skills that are in demand across many sectors. Typical roles include data scientist, data analyst, machine learning engineer, data engineer, business analyst and analytics consultant. Graduates also move into specialist roles in areas such as health analytics, finance and fintech, resources and mining analytics, government policy analytics and research.

• Work in industry teams using statistical models and machine learning to support decision-making.
• Design and maintain data pipelines, databases and scalable processing systems.
• Communicate insights to non-technical stakeholders and contribute to data-driven strategy.
• Pursue further study or research in data science, artificial intelligence or related computational fields.

Why study at Curtin University

Curtin emphasises practical, industry-relevant learning with opportunities for project work and industry engagement. The university provides modern computing facilities, access to data science software and cloud resources, and connections with industry partners, particularly in Perth's strong resources, energy and health sectors. Students benefit from a flexible curriculum that allows cross-disciplinary electives and opportunities to undertake industry placements, internships or collaborative research projects.

Curtin's approach focuses on building work-ready skills—programming, statistical reasoning, data management and communication—so graduates are prepared to contribute in data-driven teams from early in their careers.