Deakin University
Master of Data Science (Professional)
- Delivery: Online
- Study Level: Postgraduate
- Duration: 24 months
- Course Type: Master's
Enroll in an online program to become a data specialist who can leverage data to generate insights, support decision-making and create a competitive advantage in the business world.
Course overview
The sheer volume and complexity of data available to businesses today present challenges that tomorrow's graduates must be ready to solve. Modern organisations are placing increasing emphasis on using data to inform both day-to-day operations and long-term strategic decisions. You will explore the various origins of data and the methods to manage, organise and manipulate data within regulatory, ethical and security constraints.
The Master of Data Science (Professional) equips you with specialised skills in data science. He offers you the chance to engage in industry-based learning or a research project supervised by our internationally recognised staff. With a growing demand for data specialists in every sector, you will be equipped with the skills to optimise performance and add a competitive advantage.
Develop specialised expertise in categorising and transferring raw data into meaningful information for prediction and robust decision-making. You will gain the technical skills to harness the power of data through artificial intelligence and machine learning, developing innovative solutions to the challenges faced by industry and governments.
This course focuses on developing skills in data science, data modelling and design, machine learning, programming and software development. You will acquire expert knowledge of these technical aspects of data science and in-depth skills in your chosen area of specialisation.
Key facts
July, 2026
What you will study
To complete the Master of Data Science (Professional), students must pass 16 credit points.
The course is structured in four parts:
- Part A: Fundamental data science studies (four credit points)
- Part B: Mastery data science studies (four credit points)
- Part C: Specialisation (four credit points) or course elective units (four credit points)
- Part D: Professional studies (four credit points)
- Academic Integrity and Respect at Deakin Module (zero-credit-point compulsory unit)
- Academic Integrity and Respect at Deakin (Zero credit points)
- Real World Analytics
- Data Wrangling
- Mathematics for Artificial Intelligence
- Machine Learning
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 must meet the minimum academic and English language proficiency requirements or higher to be considered for selection, but this does not guarantee admission.
A combination of qualifications and experience may be deemed equivalent to the minimum academic requirements.
Academic requirements
To be considered for admission to this degree, 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).
* Related to the broad field of Information Technology.
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's 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.
Recognition of Prior Learning
Deakin 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 (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
Deakin's graduate learning outcomes describe the knowledge and capabilities graduates can demonstrate after their course. These outcomes mean that regardless of the Deakin course you undertake, you can rest assured that your degree will teach you the skills and professional attributes employers value. They'll set you up to learn and work effectively in the future.
- 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 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 modern organisations across multiple industry sectors.
- Assess the role data analytics plays in the context of modern organisations and society to add value. Have a broad appreciation of advanced topics within the IT domain through engagement with research or specialist studies.
- Communicate in professional and other contexts to inform, explain and drive sustainable innovation through data science. Motivate and effect change by drawing upon advances in technology, future trends, and industry standards and utilising a range of verbal, graphical, and written methods. Recognise the needs of diverse audiences, including specialist and non-specialist clients, industry personnel and other stakeholders.
- Identify, evaluate, select and use advanced digital technologies, platforms, frameworks and tools from the field of data science to generate, manage, process, and share digital resources and justify the selection of digital tools 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 data science.
- Demonstrate an advanced and integrated understanding of data science and 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 professionally to complex situations and ongoing learning in 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 specialist knowledge and skills from data science to advance the team's 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 data science, with appreciation for the global context and openly and respectfully collaborate with diverse communities and cultures.
Fees and FEE-HELP
Estimated first-year tuition fee in 2026: $34,400 (domestic full-fee paying place).
All costs are calculated using current rates and are based on a full-time study load of eight credit points (normally eight 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.
FEE-HELP loans are available to assist eligible full-fee paying domestic students with the cost of a university course.