University of Adelaide

What you'll study

This programme combines core mathematical foundations with a concentrated sequence of statistical topics and computational practice. Early study focuses on calculus, linear algebra and introductory probability, building a rigorous grounding in mathematical thinking. Progressing units introduce mathematical statistics, statistical inference, regression and multivariate analysis, and probability theory. Applied and computational units cover statistical computing (R, Python), data cleaning and visualisation, simulation and Monte Carlo methods, and machine learning fundamentals.

Advanced and elective topics commonly available include time series analysis, Bayesian statistics, experimental design, survival analysis, stochastic processes and applied modelling. Mathematical courses such as real analysis and discrete mathematics are offered to strengthen theory, and students can usually take electives from related areas (computer science, economics, actuarial studies, or biological sciences) to tailor the degree to specific interests. Many students complete a capstone research project, industry-linked practicum or an optional honours year to prepare for research or postgraduate study.

Entry requirements

Admission is normally based on completion of secondary school qualifications or an equivalent recognised qualification. Applicants are expected to demonstrate strong achievement in mathematics — where possible, the programme assumes prior study of high-level secondary mathematics (for example specialist mathematics or equivalent). Competence in English at the university's required standard is also necessary for entry.

Specific pathways and prerequisites vary by applicant type (domestic, international, mature-age, or those with previous tertiary study), and the University provides alternative entry schemes and bridging units for students who need to build mathematics background. Prospective students should consult the University of Adelaide’s admissions pages for the detailed criteria that apply to their situation.

Career prospects

Graduates are equipped for roles that require quantitative analysis, statistical modelling and data literacy. Typical career destinations include:

• Statistician or data analyst in government, health, education, environment and research organisations
• Data scientist, business analyst or quantitative analyst in finance, consulting and technology sectors
• Biostatistician or epidemiological analyst in medical research and pharmaceuticals
• Actuarial analyst (often requiring additional professional study), risk analyst and insurance roles
• Research assistant or pathway to postgraduate study (honours, masters or PhD) in statistics, mathematics or data science

Employers value the degree’s combination of theoretical rigour and practical computing skills; graduates often pursue further professional accreditation or postgraduate specialisation to access senior analytic and research roles.

Why study at University of Adelaide

The University of Adelaide’s School of Mathematical Sciences is research-active and offers small-to-medium class sizes that balance theory with applied projects and computing practice. The School has links with industry and government agencies across South Australia, providing opportunities for internships, collaborative projects and industry-informed capstones.

Students benefit from access to modern computing facilities, statistical software and support networks for mathematics and statistics learning. Cross-disciplinary study options make it straightforward to combine statistics with fields such as computer science, economics, public health or environmental science, helping graduates apply quantitative skills in diverse workplaces.