Master of Data Science (Professional)

Course summary for international students

Year	2026 course information
Award granted	Master of Data Science (Professional)
Campus	Offered at Burwood (Melbourne), Online
Length	2 years full-time
Fee paying annual fee - commencing 2026	$44,200 for 1 yr full-time AUD
CRICOS code	107030E Burwood (Melbourne)
Level	Higher Degree Coursework (Masters and Doctorates)
Deakin course code	S770
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

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 both 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.

The Master of Data Science (Professional) equips you with specialised skills in data science and offers you the chance to engage in industry-based learning or a research project supervised by our internationally recognised staff. 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?

Develop specialised expertise 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 faced by industry and governments.

This course focuses on developing skills in data science, data modelling and design, machine learning, programming and software development. You will develop expert knowledge of these technical aspects of data science as well as in-depth skills in your chosen area of specialisation.

Professional recognition

The Master of Data Science (Professional) 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.

Fees and charges

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.

Career opportunities

In fiercely competitive markets where businesses are constantly striving to increase profit, reduce costs and provide exceptional customer value, the need 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 adviser, management analyst, business adviser and strategist, marketing manager, market research analyst or marketing specialist.

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. 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.

Course rules

To complete the Master of Data Science (Professional), 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 (Professional) includes:

DAI001 Academic Integrity and Respect at Deakin (0-credit-point compulsory unit) in their first study period
Part A: Fundamental Data Science studies:
- 4 credit points of core units
Part B: Mastery Data Science studies:
- 4 credit points of core units
Part C: Specialisation or course electives:
- 4 credit points which may comprise of:
  - 4 credit point specialisation or
  - 4 credit points of course elective units, level 7 SIT or MIS-coded
Part D: Professional studies:
- 4 credit points of professional studies units (excluding SIT771, SIT772, SIT773 and SIT774)

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.

Research information

Students interested in pursuing a Higher Degree by Research (HDR), including a Masters by Research or PhD are encouraged to undertake the Professional Studies – Research Project pathway. High achieving students with a particular interest in research should also consider undertaking either the Research Training in Information Technology specialisation or additional research units as electives (e.g. SIT724, SIT746 and/or SIT747). Students are encouraged to contact Student Central and speak to a course adviser if they are interested in pursuing this option.

Course structure

Part A: Fundamental Data Science studies

DAI001

Academic Integrity and Respect at Deakin (0 credit points)

SIT718

Real World Analytics

SIT731

Data Wrangling

SIT787

Mathematics for Artificial Intelligence

SIT720

Machine Learning

Part B: Mastery Data Science studies

SIT741

Statistical Data Analysis

SIT742

Modern Data Science

SIT743

Bayesian Learning and Graphical Models

SIT744

Deep Learning

Part C: Specialisation or Course elective units

A 4 credit point specialisation or 4 level 7 SIT or MIS-coded elective units (excluding SIT771, SIT772, SIT773 and SIT774).

Refer to the details of each specialisation for availability.

Part D: Professional studies

Team Project

SIT753

Professional Practice in Information Technology

SIT764

Team Project (A) - Project Management and Practices (capstone)

SIT782

Team Project (B) - Execution and Delivery (capstone)

1 level 7 SIT or MIS-coded elective (1 credit point)~

Professional Practice

STP710

Career Tools for Employability (0 credit points)

SIT753

Professional Practice in Information Technology

SIT764

Team Project (A) - Project Management and Practices (capstone)

SIT791

Professional Practice (2 credit points) * (capstone)

Research Project^

SIT753

Professional Practice in Information Technology

SIT764

Team Project (A) - Project Management and Practices (capstone)

Plus 1 unit (2 credit points) from the following:

SIT723

Research Techniques and Applications (2 credit points) + (research training capstone)

SIT792

Minor Thesis (2 credit points) + (research training capstone)

*Students undertaking this unit must have successfully completed STP710 Career Tools for Employability (0-credit point unit)

~ excluding SIT771, SIT772, SIT773 and SIT774

+ Entry is subject to specific unit entry requirements.

^Students interested in pursuing a Higher Degree by Research (HDR), including a Masters by Research or PhD are encouraged to undertake the Professional Studies – Research Project pathway. High achieving students with a particular interest in research should also consider undertaking either the Research Training in Information Technology specialisation or additional research units as electives (e.g. SIT724, SIT746 and/or SIT747). Students are encouraged to contact Student Central and speak to a course adviser if they are interested in pursuing this option.

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 via the School of Information Technology website in addition to the individual unit outlines in the Handbook.

Admission criteria

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 or higher to be considered for selection, but this does not guarantee admission.

A combination of qualifications and experience may be deemed equivalent to minimum academic requirements.

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)

Examples of related disciplines include, but not limited to the broad field of Information Technology.

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're able to commit to your study.

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 Information Technology (S578) and Graduate Certificate of Data Analytics (S576) followed by a 12-credit-point Master of Data Science (Professional)
Graduate Diploma of Data Science (S677) followed by an 8-credit-point Master of Data Science (Professional)

Pathway options will depend on your professional experience and previous qualifications.

Credit for prior learning - general

If you have completed previous studies which you believe may reduce the number of units you have to complete at Deakin, indicate in the appropriate section on your application that you wish to be considered for Recognition of prior learning. You will need to provide a certified copy of your previous course details so your credit can be determined. If you are eligible, your offer letter will then contain information about your Recognition of prior learning.
Your Recognition of prior learning is formally approved prior to your enrolment at Deakin during the Enrolment and Orientation Program. You must bring original documents relating to your previous study so that this approval can occur.

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.

Alternative exits

Graduate Certificate of Data Analytics (S576)
Graduate Diploma of Data Science (S677)
Master of Data Science (S777)

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.

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.

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.

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.

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.