Graduate Diploma of Data Science
2025 Deakin University Handbook
| Year | 2025 course information |
|---|---|
| Award granted | Graduate Diploma of Data Science |
| Deakin course code | S677 |
| Faculty | Faculty of Science, Engineering and Built Environment |
| Campus | Offered at Burwood (Melbourne) |
| Online | Yes |
| Duration | 1 year full-time or part-time equivalent |
| Course Map - enrolment planning tool | The course map for new students commencing from Trimester 1 2025. Course maps for commencement in previous years are available on the Course Maps webpage or please contact a Student Adviser in Student Central. |
| 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
- Indicative student workload
- Career opportunities
- Participation requirements
- Mandatory student checks
- Pathways
- Alternative exits
- Course Learning Outcomes
- Course rules
- Course structure
- Fees and charges
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.
Indicative student 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.
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 advisor, management analyst, business advisor and strategist, marketing manager, market research analyst and marketing specialist.
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.
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
| Graduate Certificate of Data Analytics (S576) |
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 and meet the following course rules to be eligible to graduate:
- 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)
Students are required to meet the University's academic progress and conduct requirements. See the enrolment codes and terminology to help make sense of the University’s vocabulary.
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
Course duration
Course duration may be affected by delays in completing course requirements, such as failing of units or accessing or completing placements.
Further information
It is important to ensure your course plan meets the course rules detailed above. Students should contact Student Central for assistance with course planning, choosing the right units and understanding course rules.
Fees and charges
Fees and charges vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study, and their study discipline or your study load.
Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as mandatory checks, travel and stationery.
For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current students website.