Key facts
Duration
2 years full-time or part-time equivalent. Depending on your professional experience and previous qualifications, you may be eligible for credit which could reduce your course duration.
Locations
Course overview
The volume and complexity of data available to organisations continues to grow, creating demand for professionals who can analyse, interpret, and apply data effectively. Deakin’s Master of Data Science prepares you with a strong foundation in computing, analytics, and statistical modelling.
You will develop advanced skills in artificial intelligence, machine learning, and data modelling. During your studies, you will prepare, analyse, and interpret complex datasets, applying your insights to challenges across industry and government. You will also develop an understanding of ethical, regulatory, and security frameworks that guide professional data practice.
Through practical, applied projects and collaborative teamwork using industry-standard tools, you will build the ability to use data to inform decisions and create meaningful impact across sectors. You can also choose an optional industry placement or internship to gain professional experience and expand your network.
Current Deakin students
To access your official course details for the year you started your degree, please visit the handbook
- Award granted
- Master of Data Science
- Year
2027 course information
- Deakin code
- S777
- CRICOS code?Commonwealth Register of Institutions and Courses for Overseas Students
- 099225J Burwood (Melbourne)
- Level
- Higher Degree Coursework (Masters and Doctorates)
- Australian Qualifications Framework (AQF) recognition
The award conferred upon completion is recognised in the Australian Qualifications Framework at Level 9
Start studying in November
Start your studies in Deakin's Trimester 3 (November) intake and begin building your future sooner. Join Melbourne's #1 university for international student graduate employment and benefit from industry-led learning, advanced facilities and strong student support. You can also apply now for our 2027 Trimester 1 (March) and Trimester 2 (July) intakes.
Course structure
The Master of Data Science is structured in four parts:
- Part A: Foundation Information Technology Studies (4 credit points)
- Part B: Fundamental Data Analytics Studies (4 credit points)
- Part C: Mastery Data Science Studies (4 credit points)
- Part D: Data Science Capstone Studies (4 credit points).
To complete the Master of Data Science you must pass 16 credit points. This includes:
- DAI001 Academic Integrity and Respect at Deakin (0-credit-point compulsory unit) in your first study period
- 15 credit points of core units
- 1 credit point of course elective units (level 7 SIT or MIS-coded units).
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.
Students are required to meet the University's academic progress and conduct requirements.
You are required to complete four core units within Part A (these are essential units for this degree).
In your first trimester you must also complete a 0-credit point unit which are compulsory for all Deakin University degrees.
You are required to complete three core units and one course elective unit within Part D.
Plus 1 level 7 SIT or MIS-coded elective unit (1 credit point)
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.
- Start date: March
- Available at:
- Burwood (Melbourne)
- Online
- Start date: July
- Available at:
- Burwood (Melbourne)
- Online
- Start date: November
- Available at:
- Burwood (Melbourne)
- Online
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. Bring your own device (BYOD) guidelines via the School of Information Technology website in addition to the individual unit outlines in the Handbook.
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.
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 classes, 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.
Students commencing the course in Trimester 3 will be required to complete units in Trimester 3.
Work experience
You may have an opportunity to undertake a placement as part of your course. For more information, please visit deakin.edu.au/sebe/wil.
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 course entry requirements to be considered for selection, but this does not guarantee admission.
Depending on your professional experience and previous qualifications, you may commence this course with Recognition for Prior Learning credit and complete your course sooner.
Master of Data Science - 8 credit points
To be considered for admission to this degree (with 8 credit points of Recognition of Prior Learning applied~) you will need to meet at least one of the following criteria:
- completion of a graduate certificate or graduate diploma in a related discipline^
- completion of a bachelor honours degree in a related discipline^
- completion of a bachelor degree in a related discipline*, and at least two years' of relevant work experience^ (or part-time equivalent).
Master of Data Science - 12 credit points
To be considered for admission to this degree (with 4 credit points of Recognition of Prior Learning applied~) 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).
Master of Data Science - 16 credit points
To be considered for admission to this degree you will need to meet the following criteria:
- completion of a bachelor degree or higher in any discipline.
*Examples of related disciplines and relevant work experience include but not limited to the broad field of Information Technology.
^Examples of related disciplines and relevant work experience include, but not limited to: the field of Data Science which may be considered to comprise artificial intelligence, business analytics, data science and data analytics.
~ Admission credit will be considered on a case-by-case basis and may be granted to applicants based on prior studies and/or equivalent industry experience.
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)
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.
Students intending to gain professional membership of the Australian Computer Society (ACS) on the basis of their study of this program must complete the following units at Deakin University, or equivalent units undertaken within another ACS-accredited program: SIT731, SIT743, SIT744, SIT753, SIT764, SIT782, SIT723, SIT792, SIT791, SIT709.
You can 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
Estimated tuition fee - full-fee paying place
$45,800 for 1 yr full-time AUD
Learn more about fees and your options for paying.
The estimated tuition fee above is based on a typical first-year full-time enrolment of 8 credit points and is provided as a guide only. Your tuition fees are determined by your course and unit enrolment. Your tuition fees will vary depending on your study load, the course duration, and any approved recognition of prior learning.
Scholarship options
Deakin scholarships recognise your hard work and achievements. Our support can ease the financial pressure of studying in Australia so you stay focused on your success. Numbers are limited, so apply early for the best chance.
Deakin alumni discount
We love welcoming Deakin alumni back to continue their journey with us. If you're starting a postgraduate award course, you may be eligible for a 10% discount on your enrolment fees, applied per unit. It's our way of supporting your next step.
Apply now
Apply directly to Deakin
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.
Pathways
Pathways for students to enter the Master of Data Science are as follows:
- Graduate Certificate of Information Technology (S578) followed by a 12-credit-point Master of Data Science
- Graduate Certificate of Information Technology (S578) and Graduate Certificate of Data Analytics (S576) followed by an 8-credit-point Master of Data Science
Pathway options will depend on your professional experience and previous qualifications.
Alternate exits
- Graduate Certificate of Data Analytics (S576)
- Graduate Certificate of Information Technology (S578)
- Graduate Diploma of Data Science (S677)
Career outcomes
Graduates of this course may find a career as data analyst, data scientist, analytics programmer, analytics manager, analytics consultant, business analyst, management adviser, management analyst, business advisers and strategist, marketing manager, market research analyst or marketing specialist.
Professional recognition
The Master of Data Science is professionally accredited with the Australian Computer Society (ACS). This course is recognised internationally for entry to professional practice by other accrediting bodies through the Seoul Accord.
In order to meet ACS requirements for professional membership, students must satisfy the completion requirements for the whole degree. In addition, ACS guidelines require that the following specific units are completed at Deakin or equivalent units undertaken within another ACS-accredited program: SIT731, SIT743, SIT744, SIT753, SIT764, SIT782, SIT723, SIT792, SIT791, SIT709.
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.
| Course Learning Outcomes |
|---|
| Develop a broad, coherent knowledge of the analytics discipline, including: the origin and characteristics of data; the methods and approaches to dealing with data appropriately and securely; and how the use of analytics outcomes can be used to improve business, organisations or society. Apply advanced knowledge and skills to decompose complex processes (from real world situations) to develop data analytics solutions for use in modern organisations across multiple industry sectors. Assess the role data analytics plays in the context of modern organisations and society in order to add value. |
| Communicate in professional and other context to inform, explain and drive sustainable innovation through data science and to motivate and effect change by drawing upon advances in technology, future trends and industry standards, and by utilising a range of verbal, graphical and written methods, recognising the needs of diverse audiences including specialist and non-specialist clients, industry personnel and other stakeholders. |
| Identify, evaluate, select and use digital technologies, platforms, frameworks, and tools from the field of data science to generate, manage, process and share digital resources and justify digital tools selection to influence others. |
| Questions assumptions and seeks to uncover inconsistencies and ambiguities in information and judgements, critically evaluates their sources and rationales, to inform and justify decision making in the field of data science. |
| Apply expert, specialised cognitive, technical, and creative skills from data science to understand requirements and design, implement, operate, and evaluate solutions to complex real-world and ill-defined computing problems. |
| Apply reflective practice and work independently to apply knowledge and skills in a professional manner to complex situations and ongoing learning in the field of data science with adaptability, autonomy, responsibility, and personal and professional accountability for actions as a practitioner and a learner. |
| Work independently and collaboratively within multidisciplinary environments to achieve team goals, contributing advanced knowledge and skills from data science to advance the teams objectives, employing effective teamwork practices and principles to cultivate creative thinking, interpersonal adeptness, leadership skills, and handle challenging discussions, while excelling in diverse professional, social, and cultural scenarios. |
| Engage in professional and ethical behaviour in the field of data science, with appreciation for the global context, and openly and respectfully collaborate with diverse communities and cultures. |
Note – From 2026, Deakin commenced introducing Graduate Attributes (GAs), which define the distinctive characteristics of a Deakin graduate. You may notice some courses still refer to Graduate Learning Outcomes (GLOs) during this transition period.
*Deakin references data from a range of government, higher education and reputable media sources. Learn more about our achievements on our University rankings page, or explore our sources page for supporting references.
