Auckland University of Technology

What you'll study

The programme blends core technical training with applied problem solving. Typical topics include statistical modelling and inference, machine learning, predictive analytics, optimisation, databases and data engineering, data visualisation and communication, and ethical, legal and governance issues for data. You will learn programming and tools commonly used in industry, such as Python and R, and gain experience with relational and NoSQL databases, cloud-based workflows and big‑data technologies.

Study is organised around a mix of taught courses and a substantial applied project or research thesis. Core modules focus on building quantitative foundations (probability and statistical methods), algorithmic approaches (supervised and unsupervised learning, time-series forecasting) and systems topics (data architecture, scalable computing). Elective options typically allow specialisation in areas such as business analytics, health analytics, spatial analytics, or natural language processing. The capstone component requires you to apply methods to a real dataset — often in collaboration with industry partners — producing a portfolio-ready project deliverable and report.

Entry requirements

Applicants are normally expected to hold a recognised bachelor’s degree with a substantial quantitative, mathematical, statistical or computing component, or an equivalent professional qualification. Applicants from other backgrounds who can demonstrate strong quantitative aptitude and relevant work experience may be considered, but may be required to complete bridging coursework to build core skills in programming and statistics before progressing to advanced modules. International applicants must meet the university's English language requirements.

Admission decisions consider academic transcripts, referees and a statement of purpose outlining your analytics experience and career objectives. For research-track entry, an outline proposal and evidence of research capability may be required.

Career prospects

Graduates enter a wide range of data-focused roles across sectors. Common career outcomes include data analyst, data scientist, business intelligence analyst, analytics consultant, data engineer and machine learning practitioner. Employers are found in finance, telecommunications, healthcare, public sector agencies, retail, consulting firms and technology companies. The programme emphasises practical skills and an industry-aligned capstone, which helps graduates move into roles that require both technical expertise and the ability to communicate insights to decision-makers.

Why study at Auckland University of Technology

AUT combines applied teaching with strong links to industry across Auckland and New Zealand. The university emphasises hands-on learning, work-integrated projects and access to up-to-date computing resources and research laboratories. Faculty teaching in analytics have practical experience and research interests spanning machine learning, business analytics and applied statistics, providing supervision for both applied projects and more theoretical research. Located in New Zealand’s largest city, AUT offers networking opportunities with a diverse set of employers and a supportive environment for international students.