Key facts
Duration
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
Campuses
Key dates
Direct applications to Deakin for Trimester 2 2022 close 26 June 2022
Direct applications to Deakin for Trimester 3 2022 close 30 October 2022
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’ll 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
2022 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 STP050 Academic Integrity (0-credit point compulsory unit)
Depending upon prior qualifications and/or experience, you may receive credit for Foundation and Fundamental Information Technology Studies.
Note that if you are eligible for credit for prior studies you may elect not to receive the credit.
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
Plus one level 7 SIT or MIS elective
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.
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.
Entry requirements
Entry information
1 year full time (2 years part time) – 8 credit points
Admission is based on:
- Bachelor’s degree in a related discipline and two years relevant work experience OR
- Bachelor's degree in a related discipline and Graduate Certificate of Data Analytics or equivalent OR
- Bachelor Honours Degree in a related discipline OR
- Graduate Certificate of Information Technology and Graduate Certificate of Data Analytics OR
- 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 OR
- Bachelor’s degree in any discipline and two years relevant work experience OR
- Graduate Certificate of Information Technology OR
- 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 in any discipline OR
- Evidence of academic capability judged to be equivalent
Deakin University offers admission to postgraduate courses through a number of Admission categories. To be eligible for admission to this program, applicants must meet the course requirements.
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
Recognition of prior learning
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 System which outlines the credit that may be granted towards a Deakin University degree.
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 $47,014.
Please note: fees shown by the calculator are indicative only and based on 2022 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 2022 fees.
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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 can be made directly to the University through the Deakin 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.
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
Pathways for students to enter the Master of Data Science are as follows:
- Graduate Certificate of Data Analytics (S576) (followed by a 12 credit point Master of Data Science)
Pathway options will depend on your professional experience and previous qualifications.
Alternative exits
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
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