Deakin University
Graduate Diploma of Data Science
- Delivery: Online
- Study Level: Postgraduate
- Duration: 12 months
- Course Type: Graduate Diploma
Enhance your skills by studying online to confidently analyze any type of data, identify trends, make predictions, draw conclusions, drive innovations, make informed decisions and share information that influences others.
Course overview
Modern organisations are increasingly emphasising the use of data to inform both daily operations and long-term strategic decisions, resulting in a high demand for data scientists. This course equips you with the essential skills and knowledge to meet this demand and excel in a high-job-growth area.
The Graduate Diploma of Data Science introduces you to modern data science concepts, statistical analysis, descriptive analytics and machine learning. You will gain the theory, methodologies, techniques and tools needed to confidently work with all types of data – identifying trends, making predictions, driving innovation and influencing decisions. With these in-demand skills, you will be ready to deliver valuable insights and support evidence-based decision-making across a wide range of industries.
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
July, 2026
November, 2026
What you will you study
To complete the Graduate Diploma of Data Science students must pass eight credit points.
Academic Integrity and Respect at Deakin (zero-credit point compulsory unit).
The course is structured in two parts:
- Part A: Fundamental data analytics studies (four credit points)
- Part B: Core data science studies (four credit points).
Depending upon prior qualifications and/or experience, you may receive credit for Part A.
- Academic Integrity and Respect at Deakin (zero credit points)
- Real World Analytics
- Data Wrangling
- Mathematics for Artificial Intelligence
- Machine Learning
Entry requirements
Selection is based on a holistic consideration of your academic merit, work experience, likelihood of success, availability of places, participation requirements, regulatory requirements and individual circumstances. You must meet the minimum academic and English language proficiency requirements or higher to be considered for selection, but this does not guarantee admission.
A combination of qualifications and experience may be deemed equivalent to the minimum academic requirements.
Academic requirements
To be considered for admission to this degree, you will need to meet at least one of the following criteria:
- Completing 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' relevant work experience (or part-time equivalent).
* Related to the broad field of Information Technology.
English language proficiency requirements
To meet the English language proficiency requirements of this course, you will need to demonstrate at least one of the following:
- Bachelor's degree from a recognised English-speaking country.
- IELTS overall score of 6.5 (with no band score less than 6.0) or equivalent.
- Other evidence of English language proficiency (learn more about other ways to satisfy the requirements).
Recognition of Prior Learning
Deakin University aims to provide students with as much credit as possible for approved prior study or informal learning which exceeds the normal entrance requirements for the course and is within the constraints of the course regulations. Students are required to complete a minimum of one-third of the course at Deakin University, or four credit points, whichever is the greater. In the case of certificates, including graduate certificates, a minimum of two credit points within the course must be completed at Deakin.
You can also refer to the recognition of prior learning (RPL) system which outlines the credit that may be granted towards a Deakin University degree and how to apply for credit.
Visit their website or contact the university for more information.
Outcomes
Learning outcomes
- Develop specialised knowledge of data analytics concepts and technologies to solutions based on specifications and user requirements.
- Communicate in a professional context to inform, explain and drive sustainable innovation through data science and to motivate and effect change, utilising a range of verbal, graphical and written methods, recognising the needs of diverse audiences.
- Identify, select and use digital technologies, platforms, frameworks and tools from the field of data science to generate, manage, process and share digital resources.
- Evaluate and critically analyse information provided and their sources to inform decision making and evaluation of plans and solutions associated with the field of data science.
- Apply advanced cognitive, technical and creative skills from data science to understand requirements and design, implement, operate and evaluate solutions to real-world and ill-defined computing problems.
- Work independently to apply knowledge and skills in a professional manner to new situations and/or further learning in the field of data science with adaptability, autonomy, responsibility and personal accountability for actions as a practitioner and a learner.
- Apply professional and ethical standards and accountability in the field of data science and openly and respectfully collaborate with diverse communities and cultures.
Career outcomes
You may find a career as a:
- Data Analyst
- Data Scientist
- Analytics Programmer
- Analytics Manager
- Analytics Consultant
- Business Analyst
- Management Advisor
- Management Analyst
- Business Advisor and Strategist
- Marketing Manager
- Market research Analyst
- Marketing Specialist
Fees and CSP
Estimated tuition fee in 2026: $8,619 (Commonwealth Supported Place).
Estimated first-year tuition fee in 2026: $34,400 (domestic full-fee paying place).
All costs are calculated using current rates and are based on a full-time study load of eight credit points (normally eight units) per year.
A student’s annual fee may vary in accordance with:
- The number of units studied per term.
- The choice of major or specialisation.
- 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 (CSP)
The Australian Government allocates certain numbers of CSP to 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 unit you're enrolled in.
- Dependent 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.