Graduate Certificate of Data Analytics
2025 Deakin University Handbook
| Year | 2026 course information |
|---|---|
| Award granted | Graduate Certificate of Data Analytics |
| Deakin course code | S576 |
| Course Credit Points | 4 |
| Course version | 1 |
| Faculty | |
| Course Information | For students who commenced from 2019 onwards |
| Campus | Offered at Burwood (Melbourne), Online |
| Duration | 0.5 year full-time or part-time equivalent |
| Course Map - enrolment planning tool | This course map is for students commencing from Trimester 1 2026 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 |
| Supplementary Information | 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. |
Course sub-headings
- Course overview
- Indicative student workload
- Career opportunities
- Participation requirements
- Mandatory student checks
- Pathways
- Course Learning Outcomes
- Course rules
- Course structure
- Fees and charges
Course overview
The sheer volume and complexity of data available to businesses and research organisations presents challenges that tomorrow’s graduates must be ready to solve. With an increasing emphasis on the use of data to inform both daily operations and long-term strategic decisions, modern organisations are relying more on data analysts. This course will equip you with the essential skills and knowledge in data analytics to meet this demand.
With a focus on fundamental data analytics, this course covers foundation al skills, security and privacy issues, research and development, and real-world analytics. You will learn how to use data to support organisational decision-making, ensuring you graduate ready for employment across a range of industries or for further studies in IT and data science.
This course is ideal for students with a computing background, as well as those who would like to support their industry experience with a recognised academic qualification.
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
Deakin's Graduate Certificate of Data Analytics prepares students for professional employment across all sectors as data analytics specialists. Data analysts may find employment with organisations who make data-driven decisions, in areas including software development, pharmaceutical discovery, marketing, consulting, manufacturing, financial services, telecoms, e-commerce, retail, health care, public services, information security, and more.
Participation requirements
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 Certificate of Data Analytics, you could use the credit points you’ve earned to enter into further study, including:
- S677 Graduate Diploma of Data Science
- S777 Master of Data Science
- S770 Master of Data Science (Professional)
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.
Course Learning Outcomes
| Deakin Graduate Learning Outcomes | Course Learning Outcomes |
|---|---|
| Discipline-specific knowledge and capabilities | Develop data analytics solutions based on user requirements by applying foundational knowledge of real world analytics concepts and technologies. |
| 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 analytics. |
| 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 to new situations in research and professional practice 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 analytics, and openly and respectfully collaborate with diverse communities and cultures. |
Course rules
To complete the Graduate Certificate of Data Analytics students must pass 4 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
- 3 credit points of core units
- 1 credit point of course elective units
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
Core
| DAI001 | Academic Integrity and Respect at Deakin (0 credit points) |
| SIT718 | Real World Analytics |
| SIT731 | Data Wrangling |
| SIT787 | Mathematics for Artificial Intelligence |
Plus, 1 credit point from the following list of course electives:
| SIT720 | Machine Learning |
| SIT741 | Statistical Data Analysis |
| SIT742 | Modern Data Science |
| SIT743 | Bayesian Learning and Graphical Models |
Course duration
Course duration may be affected by delays in completing course requirements, such as failing of units or accessing or completing placements.
Fees and charges
Tuition fees will vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study, your study load and/or unit discipline.
Your tuition fees will increase annually at the start of each calendar year. All fees quoted are in Australian dollars ($AUD) and do not include additional costs such as textbooks, computer equipment or software, other equipment, mandatory checks, travel, consumables and other costs.
For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current students website.
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 explaining course rules and requirements.