Curtin University

What you'll study

This master's brings together advanced pure and applied mathematics, statistics and computational techniques. You will study core topics that build rigorous analytical foundations and specialist electives that reflect applications in industry and research.

• Core and advanced theory: real and complex analysis, functional analysis, partial differential equations and advanced linear algebra.
• Applied mathematics and modelling: mathematical modelling, dynamical systems, numerical methods for differential equations, optimisation and control theory.
• Probability and statistics: probability theory, stochastic processes, statistical inference, time series and Bayesian methods.
• Computational mathematics: scientific computing, numerical linear algebra, high‑performance computing methods and machine learning for mathematical problems.
• Research project or thesis: a substantial supervised research project or minor/major thesis that integrates theory, computation and application in an area such as applied analysis, computational modelling, stochastic modelling or data analytics.
• Research methods and professional skills: training in research design, scientific communication, and the use of mathematical software (e.g. MATLAB, Python/R) and computing infrastructure.

Units are usually taken as a combination of coursework and a research component. Students select electives to tailor the degree towards pure mathematics, computational science or data‑driven applications. The programme emphasises problem solving, proof techniques, model formulation and implementation on modern computing platforms.

Entry requirements

Applicants are normally expected to hold a recognised bachelor’s degree with substantial mathematics content or a relevant quantitative degree with strong performance in mathematical subjects. Equivalent qualifications with appropriate preparation and professional experience may be considered. For those without a directly related undergraduate degree, admission may be conditional on completion of bridging or preparatory coursework.

International applicants must meet Curtin’s English language requirements for postgraduate study; evidence of proficiency (for example, an international English test) is required where applicable. Additional application materials typically include academic transcripts and a curriculum vitae; applicants for the research pathway should also provide a research statement and may be asked to identify a potential supervisor or research area.

Career prospects

Graduates move into roles that require high‑level quantitative, analytical and computational skills. Common career paths include:

• Data scientist, data analyst or machine learning engineer in technology and services sectors.
• Quantitative analyst, modeller or risk analyst in finance, insurance and investment firms.
• Mathematical modeller or computational scientist for engineering, mining, energy and environmental consultancies.
• Statistician, biostatistician or analyst in government, health and research organisations.
• Actuarial analyst (often combined with professional actuarial examinations) and roles in pension and insurance sectors.
• Further academic research leading to a PhD and careers in research and higher‑education teaching.

The programme emphasises transferable skills valued by employers: rigorous problem solving, advanced numerical implementation, statistical reasoning and the ability to communicate complex quantitative results.

Why study at Curtin University

Curtin offers a research‑led environment with strengths in applied mathematics, computational science and interdisciplinary collaboration. Students benefit from supervised research projects alongside academics engaged in industry‑facing research, and have access to modern computing resources and software used in professional practice.

The university has active links with local and international industry partners, which provides pathways for project collaborations, placements and real‑world case studies. Curtin’s flexible programme structure allows students to specialise according to career goals — whether those are in pure or applied mathematics, computational modelling or data analytics — and prepares graduates for both professional and research careers.