Master of Data Science
2025 Deakin University Handbook
| Year | 2025 course information |
|---|---|
| Award granted | Master of Data Science |
| Deakin course code | S777 |
| Faculty | Faculty of Science, Engineering and Built Environment |
| Campus | Offered at Burwood (Melbourne) |
| Online | Yes |
| 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 new students commencing from Trimester 1 2025. Course maps for commencement in previous years are available on the Course Maps webpage or please contact a Student Adviser in Student Central. |
| CRICOS course 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 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.
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).
Career opportunities
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.
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) |
Course Learning Outcomes
| 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. |
Course rules
To complete the Master of Data Science students must pass 16 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
- 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.
Depending upon prior qualifications and/or experience, you may receive credit towards Parts A and B.
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: Foundation information technology studies
| 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
| SIT718 | Real World Analytics |
| SIT731 | Data Wrangling |
| SIT787 | Mathematics for Artificial Intelligence |
| SIT720 | Machine Learning |
Part C: Mastery data science studies
| SIT741 | Statistical Data Analysis |
| SIT742 | Modern Data Science |
| SIT743 | Bayesian Learning and Graphical Models |
| SIT744 | Deep Learning |
Part D: Data science capstone studies
| SIT753 | Professional Practice in Information Technology |
| SIT764 | Team Project (A) - Project Management and Practices |
| SIT782 | Team Project (B) - Execution and Delivery |
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
Course duration may be affected by delays in completing course requirements, such as failing of units or accessing or completing placements.
Further information
Student Central can help you with course planning, choosing the right units and explaining course rules and requirements.
- Contact Student Central
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
Fees and charges vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study, and their study discipline or your study load.
Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as mandatory checks, travel and stationery.
For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current students website.