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
Key dates
Direct applications to Deakin for Trimester 1 2024 close 18 February 2024
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 already at the fingertips of businesses and research organisations gives rise to challenges that must be solved by tomorrow’s graduates. With modern organisations 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 to the important challenges being faced by industry and governments. With a growing demand for data specialists in every sector, you will be able to help organisations manage risk, optimise performance and add 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, combined with methods to manage, organise and manipulate data within regulatory, ethical and security constraints. You will 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 competitive advantage 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 and enable you to build professional networks before graduating.
Read MoreCourse information
- Award granted
- Master of Data Science
- Year
2024 course information
- Deakin code
- S777
- CRICOS code?
- 099225J Burwood (Melbourne)
- Level
- Higher Degree Coursework (Masters and Doctorates)
- Approval status
This course is approved by the University under the Higher Education Standards Framework.
- 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 attain 8, 12 or 16 credit points, depending on your prior experience. Most students choose to study 4 units per trimester, and usually undertake two trimesters each year.
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
- Completion of 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 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.
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Download course guideEntry requirements
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 admission credit and complete your course in 1 year full time (or part-time equivalent).
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 at least one of 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’re 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 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
Learn more about fees and your options for paying.
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 based on a typical enrolment of students completing the first year of 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.
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 (16 credit points), based on the 2024 fees.
- is the annual FEE-HELP payment, based on your current salary
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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
Applications for Trimester 1, 2024 are open. Create an account in the Deakin Application Portal, start your application, enter personal details, 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
Further study options:
Upon completion of the Master of Data Science, you could use the credit points you’ve earned to enter into further study, including:
S770 Master of Data Science (Professional)
Alternative exits
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 Mon–Fri, 9am–5pm
- Live Chat: Mon–Thurs, 8am–7pm, Fri 8am–5pm
- Submit an online enquiry
- Help hub find common and trending questions and answers
Careers
Career outcomes
Graduates of this course may find careers as data analysts, data scientists, analytics programmers, analytics managers, analytics consultants, business analysts, management advisors, management analysts, business advisors and strategists, marketing managers, market research analysts and marketing specialists.
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 effectively in order to design, evaluate and respond to advances in data analytics approaches, technology, future trends and industry standards and utilise a range of verbal, graphical and written forms, customised for diverse audiences including specialist and non- specialist clients, colleagues and industry personnel. |
| Digital literacy | Utilise a range of digital technologies and information sources to discover, select, analyse, synthesise, evaluate, critique and disseminate both technical and professional information. |
| Critical thinking | Appraise complex information using critical and analytical thinking and judgement to identify problems, analyse user requirements and propose appropriate and innovative solutions. |
| Problem solving | Generate data solutions through the application of specialised theoretical constructs, expert skills and critical analysis to real-world, ill-defined problems to develop appropriate and innovative IT solutions. |
| Self-management | Take personal, professional and social responsibility within changing national and international professional IT contexts to develop autonomy as researchers and evaluate own performance for continuing professional development. Work autonomously and responsibly to create solutions to new situations and actively apply knowledge of theoretical constructs and methodologies to make informed decisions. |
| Teamwork | Work independently and collaboratively towards achieving the outcomes of a group project, thereby demonstrating interpersonal skills including the ability to brainstorm, negotiate, resolve conflicts, manage difficult and awkward conversations, provide constructive feedback, and demonstrate the ability to function effectively in diverse professional, social and cultural contexts. |
| Global citizenship | Engage in professional and ethical behaviour in the design, development and management of IT systems, in the global context, in collaboration with diverse communities and cultures. |
Approved by Faculty Board 27 June 2019
We invite industry speakers to our classrooms to show our students what they can do with the knowledge of data analysis and optimisation in real-life.
Vicky Mak
Senior lecturer, School of Information Technology