University of Adelaide

What you'll study

The degree builds a strong core in pure and applied mathematics in the first three years, followed by an honours year focused on independent research and advanced data science topics. Core mathematical areas typically include calculus and analysis, linear algebra, probability and mathematical statistics, and mathematical modelling. Data science-specific topics cover programming for data analysis (commonly Python and R), statistical inference, machine learning, data mining, databases and data visualisation.

• Foundations: calculus, real analysis, linear algebra and discrete mathematics
• Probability & statistics: probability theory, statistical inference, regression and time series analysis
• Computation: programming for mathematics, numerical methods, algorithms and data structures
• Data science: machine learning, data mining, big data concepts, databases and data engineering
• Advanced mathematics: optimisation, stochastic processes, mathematical modelling and applied analysis
• Honours project: a substantial supervised research or applied project that develops research skills and demonstrates mastery of a specialised data science topic

Students can expect a mix of lectures, tutorials, computer lab work and a research-led honours thesis. Electives may allow deeper study in areas such as computational statistics, scientific computing, or domain-specific applications (finance, bioinformatics, engineering).

Entry requirements

Admission typically requires completion of secondary school or an equivalent qualification with strong achievement in mathematics. Applicants are expected to have completed advanced mathematics subjects at senior secondary level (or demonstrate equivalent university-level preparation). International applicants must meet the University of Adelaide's English language requirements and present comparable academic credentials.

• Academic: strong performance in mathematics-rich senior subjects or equivalent tertiary studies; competitive entry standards apply for honours pathways.
• English proficiency: demonstrated by recognised tests or prior studies in English if required.
• Alternative pathways: applicants with relevant tertiary study, recognised diplomas, or professional experience may be considered through alternative entry routes.

Selection can also consider a combination of academic record, prerequisite units and any relevant coursework or experience. Prospective students should consult the University of Adelaide's admissions pages for specific entry guidance and documentation requirements.

Career prospects

Graduates are prepared for roles that require both strong quantitative reasoning and practical data-handling skills. Typical career paths include:

• Data scientist or data analyst — building predictive models, analysing large datasets and communicating insights
• Machine learning engineer or applied statistician — developing and deploying statistical and ML solutions
• Quantitative analyst or modeller — working in finance, insurance or risk management using mathematical models
• Business intelligence and data engineering roles — designing data pipelines, databases and visualisation dashboards
• Research and further study — honours graduates are well positioned for doctoral study or research roles in universities, research institutes or industry R&D

Employers include technology companies, financial institutions, government agencies, consulting firms, and research organisations. The honours year provides a competitive advantage for research positions and specialised industry roles.

Why study at University of Adelaide

The University of Adelaide offers a mathematics curriculum emphasising both theoretical rigour and practical computation, taught by academics active in research. The university's centres and research groups provide opportunities to engage with applied data science projects, and students can access facilities such as high performance computing and data science labs.

• Research-led teaching and supervision for honours projects
• Opportunities for industry engagement, internships and collaborative projects
• Access to computing resources and specialist software used in data science and analytics
• A supportive environment for progression to postgraduate research or professional practice

Studying at the University of Adelaide places students in a city with an active tech and research community, making it possible to build professional networks while completing a rigorous, mathematically grounded data science education.