University of Technology Sydney

What you'll study

This extended master's builds core analytics capabilities and adds applied experience through advanced electives and a substantial practicum or capstone. Core topics cover statistical modelling, machine learning for business, optimisation and predictive analytics, while technical subjects address databases, programming for analytics (often Python or R), and big data technologies. Students will also study business-focused units such as data visualisation, analytics strategy, and the ethical, legal and governance issues surrounding data use.

• Core analytics and statistics: regression and multivariate analysis, time series forecasting, experimental design, and inferential methods for business.
• Machine learning and optimisation: supervised and unsupervised learning, classification, clustering, recommender systems, and optimisation techniques for decision support.
• Data engineering and tools: databases and SQL, data wrangling, cloud and big data platforms, and programming in Python or R.
• Business and communication: data visualisation, storytelling with data, performance measurement, and translating analytics into strategy.
• Applied experience: a major project, industry practicum or extended capstone that applies analytics methods to a real organisational problem and emphasises teamwork and stakeholder engagement.
• Electives and extension topics: subject choices that may include advanced machine learning, optimisation in operations, marketing analytics, health analytics, or specialised industry streams.

Entry requirements

Applicants are typically expected to hold an undergraduate degree from a recognised university. Degrees in business, commerce, economics, mathematics, statistics, engineering, computer science or other quantitative disciplines are commonly accepted. Applicants without a directly related undergraduate degree may be considered if they have relevant professional experience or prior study in quantitative methods.

• Academic background: a completed bachelor’s degree or equivalent from a recognised institution. A strong academic record in quantitative subjects is favourable.
• Work experience: relevant professional experience can strengthen an application, particularly for applicants from non-quantitative backgrounds.
• English language proficiency: evidence of English proficiency is required from applicants whose prior study was not in English; accepted tests typically include IELTS, TOEFL or equivalent with scores meeting the university’s graduate requirements.
• Additional requirements: some applicants may be asked to provide a CV, statement of purpose, or evidence of prior coursework in mathematics, statistics or programming. Specific prerequisite subjects may need to be completed before or during the programme.

Career prospects

Graduates are prepared for professional analytics roles across private and public sectors. The programme emphasises practical skills employers seek, enabling graduates to move into positions that turn data into actionable insight.

• Data analyst and senior data analyst
• Business analyst and analytics consultant
• Data scientist and machine learning engineer (entry to mid-level)
• Business intelligence developer and reporting specialist
• Industry-specific analytics roles in finance, marketing, supply chain, health, government and utilities

Why study at University of Technology Sydney

UTS is known for its practical, industry-aligned approach to professional education and strong engagement with businesses in Sydney and beyond. The Business School and related faculties emphasise applied learning, using real-world projects, industry practicum and collaborations to develop job-ready skills. Students benefit from access to specialised labs, analytics software and networks of employers who recruit graduates into analytics roles. The university’s central Sydney location also supports opportunities for internships, networking and industry events.

Teaching is delivered by academics with research and industry experience, complemented by guest practitioners. UTS provides career support services, industry mentoring and workshops to help transition graduates into the workforce and to develop portfolios of applied analytics work for prospective employers.