Progressive, real-world learning. Online.
Key facts
Depending on your professional experience and previous qualifications, your course will be:
- 1 year full time (2 years part time) - 8 credit points
- 1.5 years full time (3 years part time) - 12 credit points
- 2 years full time (4 years part time) - 16 credit points
Key dates
Direct applications to Deakin for Trimester 2 2020 close 10 July 2020
Direct applications to Deakin for Trimester 3 2020 close 1 November 2020
Current Deakin Students
To access your official course details for the year you started your degree, please visit the handbook
Course information
Deakin’s Master of Data Science prepares students for professional employment across all sectors. 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. Become a data analytics specialist capable of using data to learn insights and support decision making.
Modern organisations are placing increasing emphasis on the use of data to inform day-to-day operations and long-term strategic decisions.
Throughout your studies you’ll learn 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’ll 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.
Units in the course may include assessment hurdle requirements.
Read MoreCourse structure
To complete the Master of Data Science, you will complete 8, 12 or 16 credit points, depending on your prior experience.
The course is structured in three parts:
- Part A. Fundamental Data Analytics Studies (4 credit points),
- Part B. Introductory Data Science Studies (4 credit points), and
- Part C. Mastery Data Science Studies (8 credit points).
Depending upon prior qualifications and/or experience, you may receive credit for Parts A and B.
Note that if you are eligible for credit for prior studies you may elect not to receive the credit.
Students are required to meet the University's academic progress and conduct requirements. Click here for more information.
Core
Mandatory unit for all entry levels
Part A: Fundamental Data Analytics Studies
Part B: Introductory Data Science Studies
Part C: Mastery Data Science Studies
~ Note: Students are expected 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.
Electives should be selected from one of the following combinations:
Four (4) credit points of project/thesis/placement units from the list below:
2 additional level 7 SIT/MIS-coded elective units or
*Students undertaking this unit must have successfully completed STP710 Career Tools for Employability (0-credit point unit)
Key information
2020 course information
Campuses by intake
Campus availability varies per trimester. This means that a course offered in Trimester 1 may not be offered in the same location for Trimester 2 or 3. Read more to learn where this course will be offered throughout the year.
Trimester 1 - March
- Start date: March
- Available at:
- Burwood (Melbourne)
- Cloud Campus
Trimester 2 - July
- Start date: July
- Available at:
- Burwood (Melbourne)
- Cloud Campus
Trimester 3 - November
- Start date: November
- Available at:
- Burwood (Melbourne)
- Cloud Campus
Additional course information
Course duration - additional information
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. Click here for more information.
Students commencing in Trimester 3 will be required to complete units in Trimester 3.
Work experience
You will have an opportunity to undertake a placement as part of your course.
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Entry requirements
Entry information
Deakin University offers admission to postgraduate courses through a number of Admission categories.
All applicants must meet the minimum English language requirements.
Please note that meeting the minimum admission requirements does not guarantee selection, which is based on merit, likelihood of success and availability of places in the course.
For more information on the Admission Criteria and Selection (Higher Education Courses) Policy visit the Deakin Policy Library
Admission to study postgraduate coursework at Deakin is based on your professional experience and previous academic qualifications. Depending on your prior experience, your course will be:
1 year full time (2 years part time) – 8 credit points
Admission is based on:
- Bachelor’s Honours (AQF8) degree in a related discipline
- Bachelor’s degree in a related discipline, plus two years relevant work experience
- Graduate Certificate of Data Science (or equivalent)
- Evidence of academic capability judged to be equivalent
1.5 years full time (3 years part time) – 12 credit points
Admissions is based on:
- Bachelor’s degree in a related discipline
- Bachelor’s degree in any discipline, plus two years relevant work experience
- Graduate Certificate of Data Analytics or equivalent
- Evidence of academic capability judged to be equivalent
2 years full time (4 years part time) – 16 credit points
Admissions is based on:
- Bachelor’s degree or other qualification at a higher AQF level in any discipline
- Evidence of academic capability judged to be equivalent
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 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.
Fees and charges vary depending on your course, your fee category and the year you started. To find out about the fees and charges that apply to you, visit www.deakin.edu.au/fees.
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 $45,881.
Please note: fees shown by the calculator are indicative only and based on 2020 rates. Actual fees may vary. We advise confirming fees with Prospective Student Enquiries prior to enrolment.
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- $* is the 2020 estimated tuition fee for a Master of Data Science (16 credit points) at Deakin
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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 15% Deakin Alumni Postgraduate Course Fee Bursary);
- 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 15% reduction per unit on your enrolment fees. Your Immediate Family Members may also be eligible to apply for this bursary.
Apply now
How to apply
Applications can be made directly to the University through the Application Portal. For information on the application process and closing dates, see the how to apply web page. Please note that closing dates may vary for individual courses.
To learn more about what the Master of Data Science at Deakin has to offer, download our course flyer.
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.
Please complete the Register your interest form to receive further information about our direct application opportunities.
Entry pathways
Pathways for students to enter the Master of Data Science are as follows:
- Option 1: Graduate Certificate of Data Analytics (S576) (followed by a 12 credit point Master of Data Science)
- Option 2: Graduate Certificate of Data Science (S577) (followed by an 8 credit point Master of Data Science)
Pathway options will depend on your professional experience and previous qualifications.
Alternative exits
- Graduate Certificate of Data Analytics (S576)
- Graduate Diploma of Data Science (S677)
Contact information
Faculty of Science, Engineering and Built Environment
School of Information Technology
deakin.edu.au/information-technology
Prospective student enquiries
Are you looking to apply for this course or would like further information?
Call 1800 693 888 or email us at myfuture@deakin.edu.au
Enquire online
Current student course and enrolment enquiries
Call 03 9244 6699 or email us at sebe-enquire@deakin.edu.au
Submit an online enquiry
Why choose Deakin
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.
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 7 June 2018
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
* 2019 Student Experience Survey, based on undergraduate students
# ARWU Rankings 2019
~ According to the Voice Project IT Service Quality Support Benchmark Survey
^ Australian Graduate Recruitment Industry Awards, 2017, 2018, 2019 winner
^^ Australian Graduate Survey 2010–2015, Graduate Outcomes Survey 2016–2019 (GOS), Quality Indicators for Learning and Teaching (QILT)