Top 150 worldwide for computer science
Accredited by Australian Computer Society (ACS)
#1 Victorian university for computing median salary
Why choose data science at Deakin
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.
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Professional accreditation and global recognition
Graduate with a degree accredited by the Australian Computer Society (ACS) and recognised worldwide through the Seoul Accord.
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Skills employers want
Gain skills in data analysis, machine learning and computing that organisations rely on to turn complex data into insights.
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Practical industry experience
Tackle real business challenges with clients such as ANZ and Barwon Water through industry projects and internship opportunities.
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Degrees shaped by employers
Learn from a degree designed with industry input to reflect current AI, analytics, and data science practices.
Connected to industry
Deakin’s strong industry connections help shape your learning. Build practical experience in real workplace contexts through work-integrated learning, mentoring and networking opportunities. Industry advisory boards also help shape our degrees, which means what you study is current, relevant and aligned to what your future employers need.
Deakin puts employability first
Career outcomes
Develop advanced expertise in data science, analytics and machine learning to solve complex problems across industry and government. Graduate with practical experience turning data into meaningful insights that support informed decision-making.
Data scientist
Analyse large and complex datasets to uncover insights, build predictive models and solve business problems. Apply statistical analysis, machine learning and programming skills to support data-driven decision-making.
- $168k
Typical salary in Australia
- 11%
Projected job growth over five years
Data analyst
Collect, prepare and interpret data to identify trends and provide actionable insights. Work with stakeholders to help organisations make informed decisions using evidence-based analysis and reporting.
- $105k
Typical salary in Australia
- 23%
Projected job growth over five years
Other jobs
- Analytics programmer
- Analytics consultant
- Analytics manager
- Business analyst
- Market research analyst
- Marketing specialist
“I have never felt understood and supported as much as I have at Deakin. All the help and support that you can think of is available, with the diverse community that Deakin have, it is almost impossible to feel like you don't belong.”
ReginaMaster of Data Science
Key dates
Direct applications
Trimester 3 2026 applications close 18 October 2026
Trimester 1 2027 applications close 7 February 2027
Download a course guide
Study at Deakin and be rewarded with new skills that will give you an edge in the job market
FAQs
Deakin's Master of Data Science develops practical skills in the technologies used by data professionals to collect, manage, analyse and interpret data. You may work with programming languages such as Python for data analysis, machine learning and automation, and SQL for querying and managing databases. Throughout the course, you'll also explore statistical modelling, data wrangling, machine learning, artificial intelligence and modern data science techniques to transform complex datasets into meaningful insights. These tools and methods are applied to real data challenges, helping you develop the technical and analytical capabilities sought by employers across a range of industries.
Yes, the Master of Data Science can be worth it if you want technical skills that connect data to decisions. Deakin’s course develops programming, machine learning, data modelling, statistical analysis and capstone project capability, with the option to undertake industry placement or internship learning where available.
Deakin's Master of Data Science takes one to two years full time, depending on prior study and credit. Your exact timeframe can change with part-time study, trimester choices or credit for prior learning, so check the course structure before planning your enrolment.
Deakin's Master of Data Science can support roles such as data scientist, machine learning engineer, data analyst, data engineer, analytics consultant or business intelligence specialist. Deakin’s course develops programming, statistical analysis, machine learning, data modelling and capstone project capability for sectors that rely on evidence-based decisions.
Study data science if you want to turn complex information into decisions that help organisations improve services, manage risk and plan strategy. Deakin’s Master of Data Science develops programming, machine learning, statistical analysis, data modelling and capstone project skills that are valued across government, technology, health, finance and business sectors.
*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.
What will I study in the Master of Data Science?
The Master of Data Science at Deakin takes you from foundational technical skills to advanced data modelling and industry application. You will gain the expertise to interpret, analyse and apply data, turning information into insights that drive decisions and solve practical challenges.
Foundations in computing and analytics
Programming and systems fundamentals
Learn how software, databases and web systems are designed, built and maintained, giving you the technical foundation to work with data-driven applications.
Data preparation and analysis
Explore how raw data is collected, cleaned and transformed, and apply statistical and mathematical thinking to uncover patterns and insights.
Introduction to analytics and modelling
Gain experience with optimisation and machine learning approaches, preparing you to analyse datasets and solve practical problems.
Advanced modelling and applied data science
Advance statistical and machine learning methods
Work with probabilistic, predictive and deep learning techniques to generate insights from large and complex datasets.
Contemporary data platforms and tools
Analyse and manage data using modern technologies and frameworks, handling diverse and large-scale data sources with confidence.
Capstone project and industry experience
Collaborate on an industry-relevant, team-based project, developing solutions, communicating findings and applying your learning in a professional setting.
Course structure
To complete the Master of Data Science, you must pass 16 credit points.
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.
Are you a current student? For course information that applies to the year you started your degree, visit the handbook.
All students are required to meet the University's academic progress and conduct requirements
Units
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)
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.
What to expect while studying
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.
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.
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.
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.
Elective units may be selected that include compulsory placements, work-based training, community-based learning or collaborative research training arrangements.
Students commencing the course in Trimester 3 will be required to complete units in Trimester 3.
You may have an opportunity to undertake a placement as part of your course. For more information, please visit deakin.edu.au/sebe/wil.
At Deakin, we're committed to ensuring an inclusive environment that supports all students to belong, thrive and succeed. We work closely with students, where needed, to identify potential barriers to participation and where possible, provide tailored support so students can engage with learning and assessment activities.
If you are neurodivergent, have a disability, health condition or mental health condition that may impact your study or participation in university life, and want to know how Deakin could support you, contact our Domestic students (opens in a new window) or International students (opens in a new window) or Disability Resource Centre (opens email client) teams for a collaborative and confidential discussion. This discussion is to support you to make plans for your studies and future career and will not have any bearing on your application to the course.
When can I start?
When you study depends on your course, location and available intakes. Check the table to see when you can start and how study is offered throughout the year.
Your course learning outcomes describe the knowledge, skills and capabilities you’ll develop and be able to demonstrate by the time you graduate. Built into your course through your units and assessments, they provide a clear picture of what you’ll be equipped to know, understand and do – preparing you to apply your learning with confidence beyond university.
| 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. |
- Award granted
- Master of Data Science
- Year
2027 course information
- Deakin code
- S777
- 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
Fees
Estimated tuition fee
$9,035
for 1 yr full-time - Commonwealth Supported Place (CSP)
The estimated tuition (student contribution) fee above is based on a typical first-year full-time enrolment of 8 credit points and is provided as a guide only. Your student contribution is determined by the units you enrol in and will vary depending on the units you choose, your study load, the course duration, and any approved recognition of prior learning. You may also be eligible for a HECS-HELP loan to defer payment of all or part of your student contribution.
Check your eligibility or learn more about CSP or HECS-HELP.
Scholarships
We want to help you excel at Deakin. Our scholarships recognise your dedication and achievements, providing financial support that can ease the cost of living and studying. With less pressure, you'll have more freedom to focus on what matters most – your education and future success.
Applications for 2027 are open
Ready to take the next step? Apply now to start your journey at Deakin and get ready for what’s next.
Key dates
Direct applications
Trimester 3 2026 applications close 18 October 2026
Trimester 1 2027 applications close 7 February 2027
Apply now
Apply direct
Simply create an account in the Deakin Application Portal, enter your personal and educational details, upload your supporting documents and submit your application.
Recognition of prior learning
We value your previous study and experience. Recognition of prior learning enables you to get credit for what you've already achieved. When you apply, we'll consider your previous study and experience for recognition of prior learning.
Recognition of prior learning may be granted for relevant postgraduate studies, in accordance with standard University procedures.
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.
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.