Graduate Diploma of Data Science

2025 Deakin University Handbook

Year	2026 course information
Award granted	Graduate Diploma of Data Science
Deakin course code	S677
Course Credit Points	8
Course version	4
Faculty	Faculty of Science, Engineering and Built Environment
Course Information	For students who commenced from 2022 onwards
Campus	Offered at Burwood (Melbourne), Online
Duration	1 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 programs.

Course sub-headings

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.

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:

S770 Master of Data Science (Professional)

Alternative exits

Graduate Certificate of Data Analytics (S576)

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 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

The Graduate Diploma of Data Science is structured in two parts:

Part A: Fundamental Data Analytics Studies (4 credit points)
Part B: Core Data Science Studies (4 credit points)

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
6 credit points of core units
2 credit points of course elective units (level 7 SIT or MIS-coded units 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

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.

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.

Estimate your fees

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.