Master of Data Science (Artificial Intelligence and Machine Learning)

What you will study

The program consists of 16 units comprising of:

• Eight Units of Core ICT Courses
• Four Units of Specialisation
• Four Units of Elective Courses

Entry requirements

Find the entry requirements most relevant to you.

• Three-year bachelor's degree from an Australian university, or equivalent, in any area or
• A minimum of five years of professional work experience equivalent to an AQF Level 7 qualification (bachelor).

To complete this degree successfully, students must meet inherent requirements. Inherent requirements are fundamental skills, capabilities and knowledge that students must demonstrate to achieve the degree's essential learning outcomes while maintaining the degree's academic integrity. Please read and understand the inherent requirements specific to the Master of Data Science before applying.

Suppose you think you may experience any problems meeting inherent requirements. In that case, you can talk to a Student Equity Officer about reasonable adjustments that may be put in place to assist you. Any reasonable adjustments must not fundamentally change the nature of the inherent requirement.

English language requirements

To meet the English language requirements for this degree, we accept the combined level and duration of study specified below or the minimum English language proficiency scores from one of the following tests. Your test must be completed within two years of applying to UniSQ.

• Tertiary study (Diploma level or higher): One-plus years of full-time tertiary study in a country from the Accepted English-speaking countries list.
• IELTS (Academic) Minimum overall score of 6.0 with a minimum of 5.5 in each subscore (speaking, listening, writing, reading).
• Pearson Test of English: Minimum overall score of 50 with a minimum of 50 in each subscore.
• TOEFL iBT: Minimum overall score of 80 with a minimum of 19 in each subscore.

If you do not meet the English language requirements, you can apply to study with our English language partner, the Union Institute of Language (UIL).

Recognition of Prior Learning

If you have previously studied or have relevant work experience, you may be eligible for recognition of prior learning. This will help reduce the number of courses you need to take to finish your program.

Outcomes

Career outcomes

• This degree provides a diverse range of career outcomes and the opportunity to advance your profession into any big data, artificial intelligence, or machine-learning field. As a graduate, you will become an efficient data scientist, utilising predictive analytics, big data management, big data analysis and modelling, artificial intelligence and machine learning to produce insights that make important, real-life decisions.
• Data scientists are highly sought after, and the demand is set to increase as organisations within technical, engineering, science, business, education and research sectors require skilled employees to understand and interpret data and navigate artificial intelligence and machine learning for the betterment of business.
• Careers focusing on artificial intelligence may include computer engineering, games development, intelligence analysis, programming and web development. Data science careers can consist of data scientist, analyst, data engineer or data manager.

Learning outcomes

Upon completion of this program, graduates will be able to:

• Autonomously apply key ICT and data science professional knowledge, technologies and programming skills to critically investigate and analyse contemporary core issues in a global market and to develop big data analysis and evidence-based decision-making skills.
• Select, adapt and apply specialised quantitative and technical skills to work independently and collaboratively to process and interpret major theories and concepts associated with big data to solve and analyse complex and real-life problems.
• Work under broad direction within a team environment, manage conflict and take a leadership role for a project task.
• Apply and communicate ethical, legal and professional standards related to big data privacy and building a security culture, and assess and evaluate risks to comply with customer organisational requirements.
• Investigate, critically analyse, evaluate and communicate research findings and problem solutions associated with applied data theories and methodologies to specialist and non-specialist audiences.

Fees and CSP

Indicative annual fee in 2025: $28,960 (domestic full-fee paying place)

Indicative annual fee in 2025: $9,312 (Commonwealth Supported Place)

A student’s annual fee may vary in accordance with:

• The number of units studied.
• The choice of major or specialisation.
• Choice of courses.
• 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 only need to pay some of your tuition fees. The student contribution amount is 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 course you're enrolled in.
• Depending on the study areas they relate to.
• Reviewed and adjusted each year.

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.