Master of Data Science

What you will study

To earn the Master of Data Science, you must successfully complete units totalling 8, 12 or 16 credit points, depending on your entry point and approved recognition of prior learning, as detailed below. Unless otherwise noted, each course is worth 1 credit point.

The complete 16-credit-point course has the following structure.

Core units

Foundation information technology studies

• Object-Oriented Development
• Database Fundamentals
• Software Requirements Analysis and Modelling
• Web Technologies and Development

Fundamental data analytics studies

• Real World Analytics
• Data Wrangling
• Mathematics for Artificial Intelligence
• Machine Learning

Mastery data science studies

• Statistical Data Analysis
• Modern Data Science
• Bayesian Learning and Graphical Models
• Deep Learning

Data science capstone studies

• Professional Practice in Information Technology
• Team Project (A) – Project Management and Practices (capstone)
• Team Project (B) – Execution and Delivery (capstone)

Students must also complete one approved level 7 information technology or information systems Elective.

Entry requirements

Academic requirements

8-credit-point course

To be considered for admission with 8 credit points of recognition of prior learning, applicants must meet at least one of the following requirements:

• Completion of a graduate certificate or graduate diploma in a related discipline.
• Completion of a bachelor’s honours degree in a related discipline.
• Completion of a bachelor’s degree in a related discipline and at least two years of relevant work experience or part-time equivalent.

For this entry point, related study or experience should be in data science, including areas such as artificial intelligence, business analytics, data science or data analytics.

12-credit-point course

To be considered for admission with 4 credit points of recognition of prior learning, applicants must meet at least one of the following requirements:

• Completion of a bachelor’s degree or higher in a related discipline.
• Completion of a bachelor’s degree or higher in any discipline and at least two years of relevant work experience or part-time equivalent.

Related disciplines and relevant professional experience may include the broad field of information technology.

16-credit-point course

To be considered for admission without recognition of prior learning, applicants must meet the following requirement:

• Completion of a bachelor’s degree or higher in any discipline.

Selection also considers academic merit, work experience, likelihood of success, availability of places, participation and regulatory requirements and individual circumstances. Meeting the minimum requirements does not guarantee admission.

Mandatory student checks

Students selecting an elective involving work-integrated learning, community placement or interaction with the community may be required to complete a police check, Working with Children Check or other mandatory checks. Requirements will be confirmed in the relevant unit information.

English language requirements

Applicants must provide evidence of at least one of the following:

• A bachelor’s degree completed in a recognised English-speaking country.
• An IELTS overall score of 6.5, with no individual band score below 6.0, or an accepted equivalent.
• Other accepted evidence of English language proficiency.

Contact the university for further information.

Recognition of Prior Learning

You may be eligible for recognition of prior learning if you have previously studied or have relevant work experience. This will help reduce the number of units you need to study to finish your course. Contact the university for more information.

Outcomes

Learning outcomes

• Develop a broad, coherent knowledge of the analytics discipline, including: the origin and characteristics of data; the methods and approaches to dealing with data appropriately and securely; and how the use of analytics outcomes can be used to improve business, organisations or society.
• Apply advanced knowledge and skills to decompose complex processes (from real world situations) to develop data analytics solutions for use in modern organisations across multiple industry sectors.
• Assess the role data analytics plays in the context of modern organisations and society in order to add value.
• Communicate in professional and other context to inform, explain and drive sustainable innovation through data science and to motivate and effect change by drawing upon advances in technology, future trends and industry standards, and by utilising a range of verbal, graphical and written methods, recognising the needs of diverse audiences including specialist and non-specialist clients, industry personnel and other stakeholders.
• Identify, evaluate, select and use digital technologies, platforms, frameworks, and tools from the field of data science to generate, manage, process and share digital resources and justify digital tools selection to influence others.
• Questions assumptions and seeks to uncover inconsistencies and ambiguities in information and judgements, critically evaluates their sources and rationales, to inform and justify decision making in the field of data science.
• Apply expert, specialised cognitive, technical, and creative skills from data science to understand requirements and design, implement, operate, and evaluate solutions to complex real-world and ill-defined computing problems.
• Apply reflective practice and work independently to apply knowledge and skills in a professional manner to complex situations and ongoing learning in the field of data science with adaptability, autonomy, responsibility, and personal and professional accountability for actions as a practitioner and a learner.
• Work independently and collaboratively within multidisciplinary environments to achieve team goals, contributing advanced knowledge and skills from data science to advance the teams objectives, employing effective teamwork practices and principles to cultivate creative thinking, interpersonal adeptness, leadership skills, and handle challenging discussions, while excelling in diverse professional, social, and cultural scenarios.
• Engage in professional and ethical behaviour in the field of data science, with appreciation for the global context, and openly and respectfully collaborate with diverse communities and cultures.

Career outcomes

Graduates may pursue roles such as:

• Data analyst
• Data scientist
• Analytics programmer
• Analytics manager
• Analytics consultant
• Business analyst
• Management adviser
• Management analyst
• Business adviser and strategist
• Marketing manager
• Market research analyst
• Marketing specialist

Fees and CSP

Indicative annual fee in 2026: $8,937 (Commonwealth Supported Place)

Indicative annual fee in 2026: $34,400 (Full-fee paying place)

Indicative annual fees are based on your first year of study.

A student’s annual fee may vary by:

• The number of units studied.
• Choice of units.
• Credit from previous study or work experience.
• Eligibility for government-funded loans.

Student fees shown are subject to change. Contact the university directly to confirm.

Commonwealth Supported Places

The Australian Government allocates a certain number of CSPs to the universities each year, which are then distributed to students based on merit.

If you're a Commonwealth Supported Student (CSS), you'll only need to pay a portion of your tuition fees. This is known as the student contribution amount – the balance once the government subsidy is applied. This means your costs are much lower.

Limited CSP spaces are offered to students enrolled in selected postgraduate courses.

Your student contribution amount is:

• Calculated per the course you're enrolled in.
• Dependent on the study areas they relate to.
• Reviewed and adjusted each year.

Student fees shown are subject to change. Contact the university directly to confirm.

HECS-HELP loans are available to CSP students to pay the student contribution amount.

FEE-HELP loans are available to assist eligible full-fee-paying domestic students with the cost of a university course.