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
Key dates
Direct applications to Deakin for Trimester 2 2025 close 22 June 2025
Direct applications to Deakin for Trimester 3 2025 close 26 October 2025
Current Deakin Students
To access your official course details for the year you started your degree, please visit the handbook
Course overview
The sheer volume and complexity of data available to businesses today presents challenges that tomorrow’s graduates must be ready to solve. Modern organisations are placing increasing emphasis on the use of data to inform day-to-day operations and long-term strategic decisions, Deakin’s Master of Data Science equips you for a career in this fast-growing sector.
Throughout your studies you will gain the technical skills to harness the power of data through artificial intelligence and machine learning. Use your insights to develop innovative solutions for the important challenges faced by industry and governments. With a growing demand for data specialists in every sector, you will help organisations manage risk, optimise performance and gain a competitive advantage through the increasing volumes of data collection.
Want to become a data science specialist capable of using data to learn insights and support decision making?
The Master of Data Science prepares you to understand the various origins of data to be used for analysis. You will learn methods to manage, organise and manipulate data within regulatory, ethical and security constraints. Develop specialised skills in categorising and transferring raw data into meaningful information for the benefit of prediction and robust decision-making.
As a graduate, your knowledge, skills and competencies in modern data science and statistical analysis will be highly valued by employers seeking greater efficiencies and a competitive edge through data insights.
Through the Master of Data Science you can choose to undertake an industry placement or internship as part of your degree. Industry placements provide you with an opportunity to develop the practical and job-ready skills employers are looking for, while enabling you to build professional networks before graduating.
Read MoreCourse information
- Award granted
- Master of Data Science
- Year
2025 course information
- Deakin code
- S777
- CRICOS code?
- 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
Course structure
To complete the Master of Data Science, you must pass 8, 12 or 16 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.
A 16-credit point Master of Data Science includes:
- DAI001 Academic Integrity and Respect at Deakin (0-credit-point compulsory unit) in their first study period
- Part A: Foundation Information Technology studies which consist of:
- 4 credit points of core units
- Part B: Fundamental Data Analytics studies:
- 4 credit points of core units
- Part C: Mastery Data Science studies:
- 4 credit points of core units
- Part D: Data Science capstone studies:
- 3 credit points of core units
- 1 credit point of course elective unit, level 7 SIT or MIS-coded
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.
4
Foundation Information Technology units
4
Fundamental Data Analytics units
8
Capstone Data Science & Mastery Data Science units
16
Total
Part A: Foundation information technology studies
Part B: Fundamental data analytics studies
Part C: Mastery data science studies
Part D: Data science capstone studies
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.
Trimester 1 - March
- Start date: March
- Available at:
- Burwood (Melbourne)
- Online
Trimester 2 - July
- Start date: July
- Available at:
- Burwood (Melbourne)
- Online
Trimester 3 - November
- Start date: November
- Available at:
- Burwood (Melbourne)
- Online
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.
Academic requirements
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 include but not limited to the broad field of Information Technology.
Examples of relevant work experience include, but not limited to: the field of Data Science which may be considered to include 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.
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.
Recognition of prior learning
The University aims to provide students with as much credit as possible for approved prior study or informal learning.
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.
Recognition of prior learning may be granted for relevant postgraduate studies, in accordance with standard University procedures.
Fees and scholarships
Fee information
The available fee places for this course are detailed above. Not all courses at Deakin have Commonwealth supported places available. The 'Estimated tuition fee' is provided as a guide only and represents the typical first-year tuition fees for students enrolled in this course. 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 you have.
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.
Scholarship options
A Deakin scholarship might change your life. If you've got something special to offer Deakin – or you just need the financial help to get you here – we may have a scholarship opportunity for you.
Postgraduate bursary
If you’re a Deakin alumnus commencing a postgraduate award course, you may be eligible to receive a 10% reduction per unit on your enrolment fees.
Apply now
To apply, create an account in the Deakin Application Portal, enter your personal details and education experience, upload supporting documents and submit. Need help? Play this video, or contact one of our friendly future student advisers on 1800 693 888 or submit an online enquiry.
For more information on the application process and closing dates, see the How to apply webpage. If you're still having problems, please contact us for assistance.
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.
Alternative exits
Contact information
Our friendly advisers are available to speak to you one-on-one about your study options, support services and how we can help you further your career.
- Call us: 1800 693 888 Monday – Friday, 9am–5pm
- Live Chat: Monday - Friday
- Submit an online enquiry
- Help hub find common and trending questions and answers
Careers
Career outcomes
Graduates of this course may find a career as data analyst, data scientist, analytics programmer, analytics manager, analytics consultant, business analyst, management advisor, management analyst, business advisors 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).
Course learning outcomes
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.
| Deakin Graduate Learning Outcomes | Course Learning Outcomes |
|---|---|
| Discipline-specific knowledge and capabilities | 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. |
| Communication | 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. |
| Digital literacy | 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. |
| Critical thinking | 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. |
| Problem solving | 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. |
| Self-management | 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. |
| Teamwork | 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. |
| Global citizenship | 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. |
Events Explore more events
We invite industry speakers to our classrooms to show our students what they can do with the knowledge of data analysis and optimisation in real-life.
Vicky Mak
Senior lecturer, School of Information Technology