Master of Data Science

Course summary for international students

Year	2026 course information
Award granted	Master of Data Science
Campus	Offered at Burwood (Melbourne), Online
Length	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.
Fee paying annual fee - commencing 2026	$44,200 for 1 yr full-time AUD
Level	Higher Degree Coursework (Masters and Doctorates)
CRICOS code	099225J Burwood (Melbourne)
Deakin course code	S777
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 that 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. 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. Learn how to apply your insights to develop innovative solutions to 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 smarter use of data.

Want to become a data science specialist capable of using data to learn insights and support decision making?

The Master of Data Science teaches you to identify and evaluate data from a wide range of sources, preparing you to use it effectively 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.

This course focuses on developing skills in data science, data modelling and design, machine learning, programming and software development.

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.

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.

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

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.

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, 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 includes:

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

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.

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 (capstone)

SIT782

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

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

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.

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 course entry requirements to be considered for selection, but this does not guarantee admission.

Depending on your professional experience and previous qualifications, you may commence this course with Recognition for Prior Learning credit and complete your course sooner.

Academic requirements

Master of Data Science - 8 credit points

To be considered for admission to this degree (with 8 credit points of Recognition of Prior Learning applied~) you will need to meet at least one of the following criteria:

completion of a graduate certificate or graduate diploma in a related discipline^
completion of a bachelor honours degree in a related discipline^
completion of a bachelor degree in a related discipline*, and at least two years' of relevant work experience^ (or part-time equivalent).

Master of Data Science - 12 credit points

To be considered for admission to this degree (with 4 credit points of Recognition of Prior Learning applied~) 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).

Master of Data Science - 16 credit points

To be considered for admission to this degree you will need to meet the following criteria:

completion of a bachelor degree or higher in any discipline.

*Examples of related disciplines and relevant work experience include but not limited to the broad field of Information Technology.

^Examples of related disciplines and relevant work experience include, but not limited to: the field of Data Science which may be considered to comprise artificial intelligence, business analytics, data science and data analytics.

~ Admission credit will be considered on a case-by-case basis and may be granted to applicants based on prior studies and/or equivalent industry experience.

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.

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.

Credit for prior learning - general

The University aims to provide students with as much credit as possible for approved prior study or informal learning.

You can 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.

Alternative exits

Graduate Certificate of Data Analytics (S576)
Graduate Certificate of Information Technology (S578)
Graduate Diploma of Data Science (S677)

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.

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.