Key facts
Duration
Locations
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 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. You will explore the various origins of data and the 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. You will gain the technical skills to harness the power of data through artificial intelligence and machine learning, developing innovative solutions to the challenges being faced by industry and governments. With a growing demand for data specialists in every sector, you will be equipped with the skills to optimise performance and add a competitive advantage.
Want to take your career to the next level with specialised study?
The Master of Data Science (Professional) builds on the specialised skills from the Master of Data Science, offering you the chance to engage in industry-based learning or a research project supervised by our internationally recognised staff.
You will also have the opportunity to hone your skills in a specialisation of your choosing, with options such as cyber security, blockchain and software development, networking and cloud technologies, AI and more.
You will develop expert knowledge of the technical aspects of data science as well as in-depth skills in your chosen area of specialisation.
Read MoreCourse information
- Award granted
- Master of Data Science (Professional)
- Year
2025 course information
- Deakin code
- S770
- CRICOS code?
- 107030E 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 (Professional), students must pass 16 credit points.
The course is structured in three parts:
- Part A. Core data science studies (8 credit points),
- Part B. Specialisation (4 credit points) or course electives (4 credit points), and
- Part C. Professional studies (4 credit points)
The three parts comprise the following:
- DAI001 Academic Integrity Module Module (0-credit-point compulsory unit)
- 8 credit points of core units,
- a 4 credit point specialisation, or 4 credit points of course electives (level 7 SIT or MIS coded units) (excluding SIT771, SIT772, SIT773 and SIT774)
- 4 credit points of professional units
8
Core units
4
Specialistion units
4
Professional units
16
Total
Core
Mandatory unit for all entry levels
Part A: Core Data Science Studies
Part B: Specialisation or course electives
Four (4) core units from a chosen specialisation (four credit points),
OR
Four (4) course electives (level 7 SIT or MIS coded units)#
Part C: Professional Studies
One (1) level 7 SIT elective (1 credit point)
OR
One (1) level 7 SIT elective (1 credit point)
OR
OR
One (1) level 7 SIT elective (1 credit point)
*Students undertaking this unit must have successfully completed STP710 Career Tools for Employability (0-credit point unit)
# excluding SIT771, SIT772, SIT773, SIT774
^ Entry to SIT746 is subject to specific unit entry requirements.
Specialisations
Refer to the details of each specialisation for availability.
- AI and Computer Vision
- Analytics in Internet of Things
- Blockchain and Software Development
- Business analytics
- Cyber Security
- Information Systems
- Networking and Cloud Technologies
Students commencing in Trimester 3 may be required to complete units in subsequent Trimester 3 teaching periods depending on their choice of specialisation.
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
Additional course information
Course duration
Course duration may be affected by delays in completing course requirements, such as accessing or completing work placements.
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.
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.
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Download course guideEntry 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 academic and English language proficiency requirements to be considered for selection, but this does not guarantee admission.
Academic requirements
To be considered for admission to this degree 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)
- completion of a Graduate Certificate of Information Technology or equivalent
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 which exceeds the normal entrance requirements for the course and is within the constraints of the course regulations. Students are required to complete a minimum of one-third of the course at Deakin University, or four credit points, whichever is the greater. In the case of certificates, including graduate certificates, a minimum of two credit points within the course must be completed at Deakin.
You can also 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.
FEE-HELP calculator
What is FEE-HELP?
FEE-HELP loans cover up to 100% of tuition fees for eligible students. By taking out a FEE-HELP loan, the government pays your tuition fees directly to Deakin, and the balance is repaid from your employment income - but only once you're earning over $51,550.
Please note: fees shown by the calculator are indicative only and based on 2024 rates. Actual fees may vary. We advise confirming fees with Prospective Student Enquiries prior to enrolment.
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- $* is the estimated full cost for a Master of Data Science (Professional) (16 credit points), based on the 2024 fees.
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*Disclaimer
Deakin University (Deakin):
- gives no warranty and accepts no responsibility for the currency, accuracy or the completeness of the information provided;
- advises users that no reliance should be placed upon on the information provided, and;
- instructs users that they should confirm the actual course fee with Prospective Student Enquiries prior to enrolment.
This tool provides indicative information about the fees that will be payable in respect of courses and subjects offered to prospective students domiciled in Australia during the periods indicated.
Please note that the fees shown by the calculator are indicative only and actual fees may vary. Users are advised to confirm the actual course fee with Prospective Student Enquiries prior to enrolment.
The estimated course fee is based on the tuition fee costs applicable to a domestic full time student commencing the course in Trimester 1 and studying full time for the duration of the course but:
- does not include non-tuition costs that may apply, such as Student Services and Amenities Fees (SSAF);
- does not take into account any scholarships or bursaries awarded to the student (including the 10% Deakin alumni discount);
- assumes the maximum number of units that need to be successfully completed actual number completed may be reduced if recognition of prior learning is granted;
- assumes that no exceptional, or non-typical, circumstances apply to the proposed course of study;
- assumes that the options that the user selects are appropriate for the course of study that they intend to undertake;
- where fees are estimated for future years those fee will be subject to annual increases in accordance with increases in the cost of course delivery.
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
Some of our courses have limited places available - to browse those still open for application, visit courses by trimester.
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.
Entry pathways
Pathways for students to enter the Master of Data Science (Professional) are as follows:
- Graduate Certificate of Information Technology (S578), followed by a 16 credit point Master of Data Science (Professional)
- Graduate Certificate of Data Analytics (S576), followed by a 12 credit point Master of Data Science (Professional)
- Graduate Diploma of Data Science (S667), followed by an 8 credit point Master of Data Science (Professional)
Pathway options will depend on your professional experience and previous qualifications.
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
In fiercely competitive markets where businesses are constantly striving to increase profit, reduce costs and provide exceptional customer value, the requirement for skilled data professionals is growing at a rapid pace. Graduates of this course may find a career as a data analyst, data scientist, analytics programmer, analytics manager, analytics consultant, business analyst, management advisor, management analyst, business advisor and strategist, marketing manager, market research analyst or a marketing specialist.
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. Have a broad appreciation of advanced topics within the IT domain through engagement with research or specialist studies. |
| 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 advanced 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 | Demonstrate an advanced and integrated understanding of data science and 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 specialist 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. |
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