Deakin University
Master of Data Science
- Delivery: Online
- Study Level: Postgraduate
- Duration: 24 months
- Course Type: Master's
Study online and gain a competitive advantage through the increasing volumes of data collection.
Course overview
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.
CSP Subsidised Fees Available
This program has a limited quota of Commonwealth Supported Places (CSP). The indicative CSP price is calculated based on first year fees for EFT. The actual fee may vary if there are choices in electives or majors.
Key facts
What you will study
To complete the Master of Data Science, students must pass eight, 12 or 16 credit points, depending on your prior experience.
The course is structured in four parts:
- Part A: Foundation information technology studies (four credit points)
- Part B: Fundamental data analytics studies (four credit points),
- Part C: Mastery data science studies (four credit points)
- Part D: Data science capstone studies (four credit points)
- Academic Integrity and Respect at Deakin (zero-credit point compulsory unit).
Depending upon prior qualifications and/or experience, you may receive credit towards Parts A and B.
- Academic Integrity and Respect at Deakin (zero credit points)
- Object-Oriented Development
- Database Fundamentals
- Software Requirements Analysis and Modelling
- Web Technologies and Development
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 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.
Master of Data Science - eight 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's honours degree in a related^ discipline
- Completion of a bachelor's 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 four credit points of Recognition of Prior Learning applied~) you will need to meet at least one of the following criteria:
- Completion of a bachelor's degree or higher in a related* discipline
- Completion of a bachelor's 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's degree or higher in any discipline.
* Related to the broad field of Information Technology.
^ Related to the field of Data Science which may be considered to include 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.
Recognition of Prior Learning
Recognition of Prior Learning
Deakin 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.\
Recognition of prior learning may be granted for relevant postgraduate studies, in accordance with standard University procedures.
Visit their website or contact the university for more information.
Outcomes
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Fees and CSP
Estimated tuition fee in 2025: $8,962 (Commonwealth Supported Place)
All costs are calculated using current rates and are based on a full-time study load of four credit points (normally four units) per year.
A student’s annual fee may vary in accordance with:
- The number of units studied per term.
- The choice of major or specialisation.
- Choice of units.
- Credit from previous study or work experience.
- Eligibility for government-funded loans.
Student fees shown are subject to change. Contact the university directly to confirm.
Commonwealth Supported Places (CSP)
The Australian Government allocates certain numbers of CSP to universities each year, which are then distributed to students based on merit.
If you're a Commonwealth Supported Student (CSS), you'll only need to pay a portion of your tuition fees. This is known as the student contribution amount – the balance once the government subsidy is applied. This means your costs are much lower.
Limited CSP spaces are offered to students enrolled in selected postgraduate courses.
Your student contribution amount is:
- Calculated per unit you're enrolled in.
- Dependent on the study areas they relate to.
- Reviewed and adjusted each year.
HECS-HELP loans are available to CSP students to pay the student contribution amount.