Master of Data Science at Deakin University
The Master of Data Science at Deakin University is a well-rounded foundation course containing programming, AI, analytics and team projects.
The Master of Data Science can be a compact or extended program depending on how much background you have in the field. Degree duration ranges from 8 subjects (one year) to as many as 16 subjects (two years) if you have limited foundations in IT and analytics.
Course Summary
Study mode: Online or Burwood campus (Melbourne)
Study periods: March, July and November intakes
Completion: 1 year full-time for the 8-subject version, 1.5 years for the 12-subject version, or 2 years for the 16-subject version
The data science masters is most useful for students who need formal foundations in IT and analytics before moving into data science. People already working with code, databases, statistics or analytics should investigate advanced standing.
Graduate Outcomes in Computing and Information Systems
Deakin University performs above the national average for postgraduate Computing and Information Systems graduates. The employment rate, median salary and satisfaction score are all higher than the national benchmarks.
| Measure | Deakin Result | National Average |
|---|---|---|
| Overall satisfaction | 77.3% | 73.8% |
| Full-time employment | 90.6% | 85.8% |
| Median salary | $115,000 | $104,400 |
Source: Course Experience Questionnaire and Graduate Outcomes Survey. Updated: 28 May 2026.
Employment outcome: 90.6% of Deakin postgraduate Computing and Information Systems graduates are in full-time employment. The national average is 85.8%, 4.8 percentage points lower.
Salary outcome: The median salary for Deakin postgraduate Computing and Information Systems graduates is $115,000. The national median is $104,400, making the uni $10,600 higher.
Student satisfaction: Overall satisfaction for Deakin postgraduate Computing and Information Systems graduates is 77.3%. The national average is 73.8%, 3.5 percentage points lower.
These figures apply to Deakin University’s postgraduate Computing and Information Systems field. The results are not specific to a particular degree.
Deakin Data Science Program Structure
Deakin University uses multiple admission pathways for the data science program. Students with related IT or analytics backgrounds may receive significant recognition of prior learning.
The data science program is divided into four main parts that move from IT foundations into advanced machine learning and project work. Students entering without an IT background complete foundation programming and systems subjects first.
| Course Component | Main Focus |
|---|---|
| Part A | Foundation IT, including programming, databases, systems and web technologies |
| Part B | Core analytics, including data wrangling, predictive analytics and mathematics for AI |
| Part C | Advanced data science, including machine learning, statistical analysis and deep learning |
| Part D | Capstone delivery, team projects, professional practice and elective study |
The final stage includes industry-style capstone projects and professional practice subjects. Students complete team-based project work along with an elective.
Related: Skills for Data Science and the Data Scientist
Machine learning and AI emphasis
Artificial intelligence and machine learning are central themes in the degree rather than minor electives. The advanced study sequence includes predictive analytics, deep learning, artificial intelligence, statistical analysis and modern data science methods.
Advanced subjects move beyond introductory analytics into specialised modelling approaches. Subjects such as Bayesian Learning and Graphical Models and Deep Learning indicate a technical orientation.
Related: How AI and Data Science Evolve Together
Group projects and capstone work
The Master of Data Science includes two capstone team project units covering project management, execution and delivery. Group-based work also appears in parts of the broader course structure.
The final stage shifts away from foundational technical study and toward collaborative project delivery and professional practice.
Online and Campus Study Options
The master’s degree in data science is offered fully online or at the Burwood campus in Melbourne. The qualification is delivered by the Faculty of Science, Engineering and Built Environment.
The main flexibility is duration rather than location. Depending on previous qualifications and professional experience, the degree may require 8, 12 or 16 subjects.
Career Directions
Australian data analyst jobs now expect more than dashboarding or Excel reporting. Employers increasingly look for people who can clean data, work with databases, write code, automate analysis and understand machine learning concepts even when the role itself is not called “data scientist”.
The Master of Data Science develops capability across many of those areas through programming, databases, analytics, machine learning, AI and statistical modelling subjects. Career options include data scientist, data analyst, analytics programmer, business analyst, analytics consultant, market research analyst and analytics manager.