University of Adelaide

What you'll study

This degree builds core mathematical knowledge alongside applied data science techniques. Early units develop calculus, linear algebra, discrete mathematics and introductory probability, while mid-to-late stage units cover statistical inference, regression, multivariate analysis, optimisation and numerical methods. Practical data science topics include programming for data analysis (commonly taught with Python and R), databases and data management, machine learning, data mining, visualisation, and ethics of data use.

Students typically complete a sequence of quantitative modelling and computational units and have opportunities for project-based learning. The program often culminates in a capstone or honours-equivalent research project where you apply mathematical modelling and data-science methods to a real-world problem. Elective options let you tailor the degree toward areas such as finance, bioinformatics, imaging, or geographic information systems.

Entry requirements

Admittance requires completion of secondary school or an equivalent qualification. Applicants are expected to demonstrate strong achievement in mathematics—normally at an advanced level in the final year of secondary study (for example, Specialist/Stage 2 Mathematics or an equivalent). International applicants can meet requirements through recognised international qualifications such as the IB or equivalent national high-school programs.

All applicants whose first language is not English must meet the University of Adelaide's English language proficiency standards (for example, an accepted IELTS, TOEFL or other approved test). The university also accepts applications through alternative pathways including recognised diplomas, articulation agreements and mature-age admission, and offers advice and bridging options for students without the specified maths background.

Career prospects

Graduates have a wide range of employment opportunities across industries that rely on quantitative analysis and data-driven decision making. Common roles include:

• Data scientist or data analyst in technology, finance, health and government sectors
• Statistician or quantitative modeller in research organisations and industry
• Business intelligence and analytics specialist in corporate settings
• Machine learning engineer or predictive modeller for product and service development
• Further study pathways into honours, research degrees or specialised postgraduate programs (e.g. applied statistics, data science, actuarial science)

Graduates are equipped to work with large datasets, build predictive models, design experiments and communicate quantitative findings to stakeholders, making them valuable in roles that bridge technical and business domains.

Why study at University of Adelaide

The University of Adelaide offers a strong mathematical sciences teaching and research environment, with staff experienced in applied statistics, machine learning and computational mathematics. Students benefit from practical learning opportunities such as project work, collaboration with industry partners and access to computational resources.

Studying in Adelaide provides connections to local research institutes, health and government agencies and a growing tech and defence sector, supporting internships and graduate employment. The degree also provides clear pathways to honours and postgraduate study at the university for students wishing to pursue advanced research or specialised professional qualifications.