University of Adelaide

What you'll study

The programme combines core training in statistical foundations, machine learning and programming with applied units in databases, big data technologies and data visualisation. Typical core topics include probability and statistical inference, supervised and unsupervised learning, optimisation methods, and responsible data practice and ethics.

• Programming and software for data science: practical work in Python/R, version control and reproducible workflows.
• Statistical methods and inference: regression, Bayesian methods, experimental design and model assessment.
• Machine learning and AI: supervised/unsupervised techniques, deep learning fundamentals and model deployment considerations.
• Data engineering and databases: relational and NoSQL databases, data warehousing, ETL and working with big data platforms.
• Data visualisation and communication: techniques for exploratory analysis, storytelling with data and dashboarding.
• Ethics and governance: privacy, fairness, reproducibility and regulatory considerations for data-driven systems.
• Capstone or industry project: a substantial applied project with industry or research partners to develop an end-to-end data solution.

The degree is delivered through a mix of lectures, hands‑on labs and project work. Students can typically tailor their studies with electives in areas such as natural language processing, computer vision, time‑series analysis, bioinformatics or business analytics.

Entry requirements

Applicants are normally expected to hold a recognised bachelor degree with a substantial quantitative or computing component (for example, mathematics, statistics, engineering, computer science, information technology or a closely related discipline). Candidates with a degree in another discipline may be considered if they can demonstrate relevant quantitative coursework or professional experience.

• Academic preparation: prior exposure to calculus, linear algebra, probability/statistics and introductory programming is required or should be addressed through bridging coursework.
• Work experience: relevant industry experience can strengthen an application, particularly for applicants from non‑quantitative backgrounds.
• English language: international applicants must meet the University of Adelaide's English language proficiency requirements (evidence such as IELTS, TOEFL or equivalent is accepted in line with university policy).
• Additional requirements: some applicants may be asked to submit a CV, academic transcripts and a statement of purpose; referees or an interview may be requested for shortlisted candidates.

Career prospects

Graduates develop skills that are in demand across public and private sectors. Typical roles taken by alumni include data scientist, machine learning engineer, data engineer, data analyst, business intelligence analyst and analytics consultant. The programme’s applied capstone and industry connections support graduate entry into areas such as healthcare analytics, finance, agriculture and resources, defence and government policy, and technology start‑ups.

Career pathways also include progression into specialist technical roles (for example, machine learning research or engineering) and positions that combine domain knowledge with data expertise, as well as opportunities to continue into research higher degrees.

Why study at University of Adelaide

The University of Adelaide is a research‑intensive institution with strong expertise in computing and data science, and close links to regional and national industry. Students benefit from access to research groups and centres active in machine learning, artificial intelligence and applied data analytics, and from collaborative projects with government, health, defence and agricultural partners.

Teaching emphasises hands‑on learning through labs, real datasets and an industry or research capstone that develops practical, deployable skills. The university’s facilities, computing infrastructure and professional networks support students seeking to transition into data science roles or to deepen technical specialisation.