Key facts
Duration
1 year full-time
Locations
Current Deakin Students
To access your official course details for the year you started your degree, please visit the handbook
Course overview
Modern organisations are increasingly emphasising the use of data to inform day-to-day operations and long-term strategic decisions, resulting in 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 covers modern data science concepts, statistical data analysis, descriptive analytics, and machine learning, equipping you with the theory, methodologies, techniques, and tools of modern data science. Through this course, you will develop the ability to confidently work with any type of data, identify trends, make predictions, draw conclusions, drive innovations, make decisions and share information that influences people. This course gives you essential skills in data analytics, enabling you to discover insights and support decision-making across a range of industries.
Read MoreCourse information
- Award granted
- Graduate Diploma of Data Science
- Year
2025 course information
- Deakin code
- S677
- Level
- Postgraduate (Graduate Certificate and Graduate Diploma)
- Australian Qualifications Framework (AQF) recognition
The award conferred upon completion is recognised in the Australian Qualifications Framework at Level 8
Course structure
To complete the Graduate Diploma of Data Science students must pass 8 credit points.
- DAI001 Academic Integrity and Respect at Deakin (0-credit point compulsory unit).
The course is structured in 2 parts:
- Part A: Fundamental data analytics studies (4 credit points)
- Part B: Core data science studies (4 credit points).
Depending upon prior qualifications and/or experience, you may receive credit for Part A.
Students are required to meet the University's academic progress and conduct requirements.
4
Fundamental data analytics units
4
Core data science units
8
Total
Part A: Fundamental data analytics studies
Part B: Core data science studies
Plus 2 level 7 SIT or MIS-coded elective units (2 credit points) #
# Excluding SIT771, SIT772, SIT773 and SIT774
Intakes by location
The availability of a course varies across locations and intakes. This means that a course offered in Trimester 1 may not be offered in the same location for Trimester 2 or 3. Check each intake for up-to-date information on when and where you can commence your studies.
Trimester 1* - March
- Start date: March
- Available at:
- Online
*Full time or part-time available
Trimester 2* - July
- Start date: July
- Available at:
- Online
*Full time or part-time available
Trimester 3* - November
- Start date: November
- Available at:
- Online
*Only part-time available
INTERNATIONAL STUDENTS – Please note that due to Australian Government regulations, student visas to enter Australia cannot be issued to students who enrol in Deakin Online programs.
Course duration
Course duration may be affected by delays in completing course requirements, such as failing of units or accessing or completing placements.
Mandatory student checks
Any unit which contains work integrated learning, a community placement or interaction with the community may require a police check, Working with Children Check or other check.
Workload
You can expect to participate in a range of teaching activities each week. This could include lectures, seminars, practicals and online interaction. You can refer to the individual unit details in the course structure for more information. You will also need to study and complete assessment tasks in your own time.
Participation requirements
Elective units may be selected that include compulsory placements, work-based training, community-based learning or collaborative research training arrangements.
Reasonable adjustments to participation and other course requirements will be made for students with a disability. More information available at Disability support services.
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 will need to 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 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:
- completion of a bachelor degree or higher in a related* discipline
- completion of a bachelor 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 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)
Admissions information
Learn more about Deakin courses and how we compare to other universities when it comes to the quality of our teaching and learning.
Not sure if you can get into Deakin postgraduate study? Postgraduate study doesn’t have to be a balancing act; we provide flexible course entry and exit options based on your desired career outcomes and the time you are able to commit to your study.
Recognition of prior learning
The 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.
Fees and scholarships
Fee information
Learn more about fees and your options for paying.
The tuition fees you pay are determined by the course you are enrolled in. The 'Estimated tuition fee' is provided as a guide only and represents the typical tuition fees for students completing this course within the same year they started. The cost will vary depending on the units you choose, your study load, the length of your course and any approved Recognition of prior learning. One year full-time study load is typically represented by eight credit points of study. Each unit you enrol in has a credit point value.
The 'Estimated tuition fee' is calculated by adding together eight credit points of a typical combination of units for your course.
You can find the credit point value of each unit under the Unit Description by searching for the unit in the handbook.
Learn more about tuition fees.
Scholarship options
A Deakin scholarship might change your life. If you've got something special to offer Deakin – or you just need the financial help to get you here – we may have a scholarship opportunity for you.
Postgraduate bursary
If you’re a Deakin alumnus commencing a postgraduate award course, you may be eligible to receive a 10% reduction per unit on your enrolment fees.
Apply now
Applications can be made directly to the University through StudyLink Connect - Deakin University's International Student Application Service.
We recommend engaging with a Deakin Authorised Agent who can assist you with the process and submit the application.
For information on the application process, including required documents and important dates, see the How to apply webpage.
If you need assistance, please contact us.
Pathways
Upon completion of the Graduate Diploma of Data Science, you could use the credit points you’ve earned to enter into further study, including:
Alternative exits
Careers
Career outcomes
Graduates of this course are prepared for professional employment across all sectors as data science specialists. Professionals with solid knowledge in data science and strong skills for analysing and interpreting data are in high demand in today's data-rich economy. 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 and marketing specialist.
Course learning outcomes
Deakin's graduate learning outcomes describe the knowledge and capabilities graduates can demonstrate at the completion of their course. These outcomes mean that regardless of the Deakin course you undertake, you can rest assured your degree will teach you the skills and professional attributes that employers value. They'll set you up to learn and work effectively in the future.
| Deakin Graduate Learning Outcomes | Course Learning Outcomes |
|---|---|
| Discipline-specific knowledge and capabilities | Develop specialised knowledge of data analytics concepts and technologies to solutions based on specifications and user requirements. |
| Communication | 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. |
| Digital literacy | Identify, select and use digital technologies, platforms, frameworks, and tools from the field of data science to generate, manage, process and share digital resources. |
| Critical thinking | 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. |
| Problem solving | 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. |
| Self-management | 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. |
| Global citizenship | Apply professional and ethical standards and accountability in the field of data science, and openly and respectfully collaborate with diverse communities and cultures. |