Key facts
Duration
The time and cost could be reduced based on your previous qualifications and professional experience. This means you can fast track the masters degree from 2 years down to 1.5 years, or even 1 year duration. See entry requirements below for more information.
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 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.
Read MoreCourse information
- Award granted
- Master of Data Science
- Year
2025 course information
- Deakin code
- S777
- CRICOS code?
- 099225J 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, students must pass 8, 12 or 16 credit points, depending on your prior experience.
The course 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: Core data science studies (4 credit points), and
- Part D: Mastery data science studies (4 credit points), plus
- DAI001* Academic Integrity Module (0-credit point compulsory unit)
Depending upon prior qualifications and/or experience, you may receive credit for Foundation Information Technology and/or Fundamental Data Analytics Studies.
Note that if you are eligible for credit for prior studies you may elect not to receive the credit.
4
Foundation Information Technology units
4
Fundamental Data Analytics units
8
Core Data Science/Mastery Data Science units
16
Total
Core
Mandatory unit for all entry levels
Part A: Foundation Information Technology Studies
Plus one level 7 SIT or MIS elective
Part C: Core Data Science Studies
Part D: Mastery Data Science Studies
~ Note: Students are recommended to undertake SIT764 and SIT782 in consecutive trimesters. Students should seek advice from the unit chair if they are unable to complete SIT764 and SIT782 consecutively.
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
Trimester 3 - November
- Start date: November
- 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.
Students commencing the course in Trimester 3 will be required to complete units in Trimester 3.
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.
Entry 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.
Depending on your professional experience and previous qualifications, you may commence this course with admission credit and complete your course in 1 year full-time (or part-time equivalent).
The time and cost of your course could be reduced based on your previous qualifications and professional experience. This means the duration of the masters degree could be reduced from 2 years down to 1.5 years, or even 1 year duration. See academic requirements below for more information.
Academic requirements
1 year full-time (or part-time equivalent) - 8 credit points
To be considered for admission to this degree (with 8 credit points of admission credit applied) you will need to meet at least one of the following criteria:
- completion of a bachelor degree (honours) (AQF 8) or higher 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)
- completion of a bachelor degree in a related discipline and Graduate Certificate of Data Analytics or equivalent
- Graduate Certificate of Information Technology and Graduate Certificate of Data Analytics
1.5 years full-time (or part-time equivalent) - 12 credit points
To be considered for admission to this degree (with 4 credit points of admission credit 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' of relevant work experience (or part-time equivalent)
- Graduate Certificate of Information Technology (or equivalent)
2 years full-time (or part-time equivalent) - 16 credit points
To be considered for admission to this degree (without admission credit applied*) you will need to meet the following criteria:
- completion of a bachelor degree or higher in any discipline
^Recognition of Prior Learning into the Master of Data Science may be granted to students who have successfully completed appropriate postgraduate level studies.
Related disciplines which may be considered include: information technology, computing, computer science, software engineering.
*Credit for recognition of prior learning will still be considered on a case-by-case basis. Learn more below.
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.
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.
Fees and scholarships
Fee information
Learn more about fees and your options for paying.
The tuition fees you pay are determined by the course you are enrolled in. 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.
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 this course. Eight credit points is used as it represents a typical full-time enrolment load for a year.
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 international student fees.
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
Applications can be made directly to the University through StudyLink Connect - Deakin University's International Student Application Service.
We recommend engaging with a Deakin Authorised Agent who can assist you with the process and submit the application.
Fill out the application form and submit to a Deakin International office or take your application form to a Deakin representative for assistance
For information on the application process, including required documents and important dates, visit the How to apply webpage.
If you need assistance, please contact us.
Careers
Career outcomes
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.
Professional recognition
The Master of Data Science is professionally accredited with the Australian Computer Society (ACS).
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. |
| 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. |
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