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 (AQF level 7) or bachelor 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.
This degree requires a Grade Point Average (GPA) equivalent to an Adelaide University 4.5 on a 7-point scale for entry.
English language entry requirements
In addition, international students who speak English as an additional language must have obtained one of the following standards within the last two years prior to admission. Possession of one or more of these qualifications, in addition to the academic entry requirements, does not, in itself, guarantee a place at Adelaide University. Applications are considered on an individual basis and selection is competitive. Where previous study/work experience was conducted in English, the application must be accompanied by certified documentation from the educational institution/employer certifying that the language of instruction/employment was English.
- IELTS Overall 6.5
- IELTS Reading 6
- IELTS Listening 6
- IELTS Speaking 6
- IELTS Writing 6
Please access the following link for a comprehensive list of English language tests accepted by Adelaide University and other important information in relation to meeting the University’s language requirements:
Equivalent English qualificationsDegree Structure: Master of Data Science
Program code: MADSC
Complete 96 units comprising:
- 66 units for all Core courses, and
- 18 units for Discipline courses, and
- 12 units for all Work integrated learning
Study plan
A study plan sets out the courses you will need to complete and the recommended timing for each one. This will help to guide your enrolment in each study period, alongside any additional information provided below. If you have received a personalised plan or alternative advice from your Program Director, please refer to that guidance in the first instance, or seek help from your Program Director or Student Assist if you are unsure.
Year 1
Semester 1
Semester 2
Year 2
Semester 1
Semester 2
Enrolment information
Find details about the rules and notes that apply to your program, along with other essential information required for successful enrolment. Supplementary supporting resources may also be provided where applicable.
Some programs include additional enrolment advice. If available for your program, it will be displayed here.
Useful links
Courses
Listed here are all the courses that contribute to your program, including elective options that can be chosen. These courses come together to form your study plan. Courses fall into different categories, each of which have specific unit values that need to be met under the program rules.
What courses you'll study
Complete 96 units comprising:
- 66 units for all Core courses, and
- 18 units for Discipline courses, and
- 12 units for all Work integrated learning
Complete 66 units for ALL of the following:
| Course name | Course code | Units | |
|---|---|---|---|
|
Course name
Problem Solving and Programming Foundations
|
Course code
COMP5002
|
Units
6
|
|
|
Course name
Data in Information Technology Systems
|
Course code
COMP5003
|
Units
6
|
|
|
Course name
Data Taming and Prediction
|
Course code
MATHX105
|
Units
6
|
|
|
Course name
Data Visualisation
|
Course code
MATHX314
|
Units
6
|
|
|
Course name
Machine Learning Algorithms
|
Course code
ARTI6003
|
Units
6
|
|
|
Course name
Introduction to Data Science, Ethics, and Privacy
|
Course code
COMP6005
|
Units
6
|
|
|
Course name
Data-Driven Decision-Making
|
Course code
COMP6011
|
Units
6
|
|
|
Course name
Leading People, Leading Teams and Cyber Security
|
Course code
INFO6011
|
Units
6
|
|
|
Course name
Mathematics for Data Analytics A
|
Course code
MATH5109
|
Units
6
|
|
|
Course name
Mathematics for Data Analytics B
|
Course code
MATH5110
|
Units
6
|
|
|
Course name
Statistical Foundations for Data Science and Artificial Intelligence
|
Course code
STAT5020
|
Units
6
|
|
Complete 18 units comprising:
- 18 units from Discipline
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Multi-Modal Data Analysis
|
Course code
COMP1013
|
Units
6
|
|
|
Course name
Multi-Source Data Analytics
|
Course code
COMP5030
|
Units
6
|
|
|
Course name
Data-Driven Customer Insights and Segmentation
|
Course code
COMP6017
|
Units
6
|
|
|
Course name
Time Series Analysis and Forecasting
|
Course code
STAT6002
|
Units
6
|
|
|
Course name
Neural Networks and Deep Learning
|
Course code
ARTIX300
|
Units
6
|
|
|
Course name
Generative Artificial Intelligence
|
Course code
ARTI5001
|
Units
6
|
|
|
Course name
Large Language Models and Applications
|
Course code
COMP6012
|
Units
6
|
Work Integrated Learning description
WIL courses give you real‑world experience as part of your degree.
Complete 12 units for ALL of the following:
| Course name | Course code | Units | |
|---|---|---|---|
|
Course name
ICT Master Capstone Project 1
|
Course code
COMP6024
|
Units
6
|
|
|
Course name
ICT Master Capstone Project 2
|
Course code
COMP6000
|
Units
6
|
|