Graduate Diploma of Data Science
Course summary for international students
| Year | 2026 course information |
|---|---|
| Award granted | Graduate Diploma of Data Science |
| Campus | This course is only offered Online |
| Length | 1 year full-time |
| Fee paying annual fee - commencing 2026 | $44,200 for 1 yr full-time AUD |
| Level | Postgraduate (Graduate Certificate and Graduate Diploma) |
| Deakin course code | S677 |
| Australian Qualifications Framework (AQF) recognition | The award conferred upon completion is recognised in the Australian Qualifications Framework at Level 8 |
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 sub-headings
- Course overview
- Fees and charges
- Career opportunities
- Course learning outcomes
- Course rules
- Course structure
- Admission criteria
- Academic requirements
- English language proficiency requirements
- Admissions information
- Pathways
- Credit for prior learning - general
- Alternative exits
- Workload
- Participation requirements
- Mandatory student checks
Course overview
Modern organisations are increasingly emphasising the use of data to inform both daily 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 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.
Fees and charges
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 fees and available payment options.
Career opportunities
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 adviser, management analyst, business adviser and strategist, marketing manager, market research analyst and marketing specialist.
Course learning outcomes
| 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. |
Course rules
To complete the Graduate Diploma of Data Science students must pass 8 credit points. The number of credit points required may vary, depending on your entry point or how much credit you receive as recognition of prior learning (RPL) based on your professional experience and previous qualifications.
An 8-credit point Graduate Diploma of Data Science includes:
- DAI001 Academic Integrity and Respect at Deakin (0-credit-point compulsory unit) in their first study period
- Part A: Fundamental Data Analytics studies
- 4 credit points of core units
- Part B: Core Data Science studies
- 2 credit points of core units
- 2 credit points of course elective units, level 7 SIT or MIS coded (excluding SIT771, SIT772, SIT773 and SIT774)
Most units are equal to one credit point. As a full-time student you will study four credit points per trimester and usually undertake two trimesters per year.
All students are required to meet the University's academic progress and conduct requirements.
Course structure
Part A: Fundamental data analytics studies
| DAI001 | Academic Integrity and Respect at Deakin (0 credit points) |
| SIT718 | Real World Analytics |
| SIT731 | Data Wrangling |
| SIT787 | Mathematics for Artificial Intelligence |
| SIT720 | Machine Learning |
Part B: Core data science studies
| SIT741 | Statistical Data Analysis |
| SIT742 | Modern Data Science |
Plus 2 level 7 SIT or MIS-coded elective units (2 credit points) #
# Excluding SIT771, SIT772, SIT773 and SIT774
Equipment requirements
The learning experiences and assessment activities within this course may require students to have access to a range of technologies beyond a laptop or desktop computer. For information regarding hardware and software requirements, please refer to the Bring your own device (BYOD) guidelines via the School of Information Technology website in addition to the individual unit outlines in the Handbook.
Admission criteria
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).
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.
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:
Credit for prior learning - general
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.
Alternative exits
| Graduate Certificate of Data Analytics (S576) |
Course duration
Course duration
You may be able to study available units in the optional third trimester to fast-track your degree, however your course duration may be extended if there are delays in meeting course requirements, such as completing a placement.
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.
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.