Master of Data Science
2027 Deakin University Handbook
| Year | 2027 course information |
|---|---|
| Award granted | Master of Data Science |
| Course credit points | 16 |
| Deakin course code | S777 |
| Course version | 3 |
| Faculty | Faculty of Science, Engineering and Built Environment |
| Course information | For students who commenced from 2022 onwards |
| Campus | Offered at Burwood (Melbourne), Online |
| 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. |
| Course map - enrolment planning tool | The course map for students commencing from Trimester 1 2027 will be available soon Course maps for commencement in previous years are available on the Course Maps webpage or please contact a Student Adviser in Student Central. |
| CRICOS code | 099225J Burwood (Melbourne) |
| Australian Qualifications Framework (AQF) recognition | The award conferred upon completion is recognised in the Australian Qualifications Framework at Level 9 |
Course sub-headings
- Course overview
- Indicative student workload
- Professional recognition
- Career opportunities
- Participation requirements
- Mandatory student checks
- Pathways
- Alternative exits
- Course learning outcomes
- Course rules
- Course structure
- Work experience
- Other learning experiences
- Fees and charges
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.
Indicative student 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.
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.
Career opportunities
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.
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.
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
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
| Graduate Certificate of Data Analytics (S576) | |
| Graduate Certificate of Information Technology (S578) | |
| Graduate Diploma of Data Science (S677) |
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 learning outcomes
| 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.
Course rules
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.
Course structure
Part A: Foundation information technology studies
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.
| DAI001 | Academic Integrity and Respect at Deakin (0 credit points) |
| SIT771 | Object-Oriented Development |
| SIT772 | Database Fundamentals |
| SIT773 | Software Requirements Analysis and Modelling |
| SIT774 | Web Technologies and Development |
Part B: Fundamental data analytics studies
You are required to complete four core units within Part B (these are essential units for this degree).
| SIT718 | Real World Analytics |
| SIT731 | Data Wrangling |
| SIT787 | Mathematics for Artificial Intelligence |
| SIT720 | Machine Learning |
Part C: Mastery data science studies
You are required to complete four core units within Part C (these are essential units for this degree).
| SIT741 | Statistical Data Analysis |
| SIT742 | Modern Data Science |
| SIT743 | Bayesian Learning and Graphical Models |
| SIT744 | Deep Learning |
Part D: Data science capstone studies
You are required to complete three core units and one course elective unit within Part D.
| SIT753 | Professional Practice in Information Technology |
| SIT764 | Team Project (A) - Project Management and Practices (capstone) |
| SIT782 | Team Project (B) - Execution and Delivery (capstone) |
Plus 1 level 7 SIT or MIS-coded elective unit (1 credit point)
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.
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.
Other learning experiences
You may choose to use one of your elective units to undertake an internship or participate in an overseas study tour to enhance your global awareness and experience.
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
Contact Student Central for assistance in course planning, choosing the right units and explaining course rules and requirements. Student Central can also provide information for a wide range of services at Deakin. To help you understand the University vocabulary, please refer to our Enrolment codes and terminology page.