Adelaide University
Master of Data Science
- Delivery: Face to Face
- Study Level: Postgraduate
- Duration: 24 months
- Course Type: Master's
Understand the intricacies of the world’s data, bringing order to chaos and finding solutions for global businesses.
Course overview
Adelaide University's Master of Data Science will equip you with high-level skills in the data science pipeline – from collection right through to analysis and evaluation. You’ll also learn the skills needed for strategic leadership roles in data science.
Choose to specialise in artificial intelligence and develop in-depth knowledge of statistical, computational and machine learning techniques. A hands-on industry project in your final year allows you to apply your skills in a work environment. Collaborating with industry professionals, you’ll develop deep knowledge in real-world data science practices.
You’ll emerge as a sought-after data expert, ready to improve business models in a variety of sectors. Graduate ready to explore data trends and join the groundbreaking realm of big data.
Key features
- Develop advanced knowledge of the data science pipeline, from data collection through to evaluation.
- Discover how artificial intelligence influences data processes.
- Learn to interpret complex data and spot trends.
- Be guided by academics who are experts in data science.
- Prepare to be a leader in the data science field.
- Undertake an industry project to build professional skills and networks.
CSP Subsidised Fees Available
This program has a limited quota of Commonwealth Supported Places (CSP). The indicative CSP price is calculated based on first year fees for EFT. The actual fee may vary if there are choices in electives or majors.
Key facts
Mawson Lakes Campus
July, 2026
What you will study
Students must complete 96 units comprising:
- 66 units for all core courses.
- 18 units for discipline courses.
- 12 units for all work-integrated learning courses.
Each course is worth six units.
Complete 66 units for all of the following:
- Problem Solving and Programming Foundations
- Data in Information Technology Systems
- Data Taming and Prediction
- Data Visualisation
- Machine Learning Algorithms
- Introduction to Data Science, Ethics and Privacy
- Data-Driven Decision-Making
- Leading People, Leading Teams and Cyber Security
- Mathematics for Data Analytics A
- Mathematics for Data Analytics B
- Statistical Foundations for Data Science and Artificial Intelligence
Entry requirements
Admission criteria
To be eligible, an applicant must have achieved at least one of the following minimum entry requirements and demonstrate they fulfil any essential criteria for admission. In cases where there are more eligible applicants than available places, admission will be competitive with ranks based on the entry criteria.
- A completed bachelor's (AQF level 7) or bachelor's honours (AQF level 8) degree or equivalent from a recognised higher education institution; OR
- A completed nested or related graduate certificate (AQF level 8) or higher, or equivalent from a recognised higher education institution.
Recognition of Prior Learning
Adelaide University is committed to recognising the contribution of students’ prior learning towards their program requirements.
Credit may be granted for formal, informal and non-formal learning, with guidance on eligibility provided to prospective students in a clear, transparent and publicly available credit framework.
Credit determinations will be:
- Evidence-based, equitable and transparent.
- Academically sound.
- Applied consistently, fairly and subject to review.
Credit will be granted for prior learning that is relevant and equivalent to the learning required for the courses for which credit is sought.
Credit will only be granted if it:
- Maintains the credibility and integrity of the program for which credit is sought.
- Does not contravene the conditions of the program’s professional accreditation.
- Does not disadvantage the student’s ability to achieve the expected course or program learning outcomes.
For more information, contact the university or visit its website.
Outcomes
Career outcomes
You’ll graduate ready to lead research and development initiatives in data science across both public and private sectors. Picture yourself a data expert in the world of sports analytics, predicting match dynamics and player performances to catapult teams to success. Or perhaps you’ll predict upcoming global trends using machine learning.
Other careers to consider include:
- Data Engineer
- Data Scientist
- Research Scientist
- Machine Learning Engineer
- Statistician
- Data Modeller
Fees and CSP
Estimated student contribution amount per 1.0 EFTSL (48 units) in 2026: From $6,538 up to $8,337.25 (Commonwealth Supported Place)
Commonwealth-supported students are charged a portion of the cost of their higher education through a student contribution. Where the duration of the program is less than one year, the total cost of the program is displayed.
Commonwealth Supported Places (CSP)
A Commonwealth Supported Place is a higher education place where the Australian Government subsidises your fees so that you only pay a portion through a student contribution amount.
The amount of student contribution you’ll pay depends on:
- Number of courses you are enrolled in.
- Unit value of courses.
- Funding cluster your courses fall under.
A HECS-HELP loan allows students to borrow from the Australian Government to cover some or all of their student contribution. To be eligible for HECS-HELP, you must be studying in a Commonwealth Supported Place.