RMIT University
Master of Data Science
- Delivery: Face to Face
- Study Level: Postgraduate
- Duration: 24 months
- Course Type: Master's
Gain the skills to navigate huge volumes of data generated via social media, financial transactions, transportation and scientific discovery as a data scientist.
Course overview
This interdisciplinary field of data science combines computer science with mathematical statistics and domain expertise to manage and analyse data. As a data scientist, you'll develop the capability to derive insight and opportunity from the vast repositories of information that organisations collect. Data science also puts an emphasis on the specialised computational skills required to manage and analyse big data from sources such as massive sensors, mobile and transaction data.
Through data science, businesses gain a competitive edge, governments deliver more targeted services and research teams make new discoveries.
Key facts
July, 2026
What you will study
To earn the Master of Data Science, students must complete 192 credit points. Unless otherwise indicated, each course is worth 12 credit points.Complete 84 credit points from the following:
- Practical Data Science with Python
- Programming Fundamentals
- Database Concepts
- Applied Analytics
- Data Wrangling
- The Data Science Professional
- Advanced Programming for Data Science
AND
Complete 12 credit points from the following:
- Big Data Processing 12
- Data Visualisation and Communication
- Case Studies in Data Science
Entry requirements
Academic requirements
You must have one of the following:
- An Australian bachelor's degree in computing, science, engineering, health, or statistics with a GPA of at least 2.0 out of 4.0 or equivalent.
- You may also be considered if you have an Australian bachelor degree with a GPA of at least 2.0 out of 4.0, or equivalent and; relevant completed courses in programming and statistics in an undergraduate or postgraduate degree or a minimum three years’ of current, relevant work experience or professional practice as a programmer, statistician or equivalent.
If you wish to have industry or employment experience assessed as part of meeting the entry requirements, you will need to provide a detailed CV/resume listing previous positions, dates of employment and position responsibilities; a statement from your employer confirming these details (or contact details of the employer so RMIT can seek confirmation); and evidence of any relevant professional development undertaken.
International qualifications are assessed for comparability to Australian qualifications according to the Australian Qualifications Framework (AQF).
English language requirements
You must meet the University's minimum English language requirements to be eligible for a place in this program.
Contact the university for more information.
Recognition of Prior Learning
Credit for prior experience, which is also called recognition of prior learning (RPL), may be awarded for skills or knowledge gained during employment, professional development, short courses, on-the-job training, or life experience. Contact the university for more details.
Outcomes
Upon completion of the Master of Data Science, you will have achieved the following Program Learning Outcomes:
- Demonstrate mastery of a body of knowledge that includes recent developments in computer science and information technology.
- Understand and use appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.
- Recognise and use research principles and methods applicable to data science.
- Analyse and model complex requirements and constraints for the purpose of designing and implementing software artefacts and IT systems.
- Evaluate and compare designs of software artefacts and IT systems on the basis of organisational and user requirements.
- Bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of statistical problems.
- Design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification.
- Apply an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.
- Interpret abstract theoretical propositions, choose methodologies, justify conclusions and defend professional decisions to both IT and non-IT personnel via technical reports of professional standard and technical presentations.
- Work effectively in different roles, to form, manage and successfully produce outcomes from collaborative teams, whose members may have diverse cultural backgrounds and life circumstances and differing levels of technical expertise.
- Effectively apply relevant standards, ethical considerations and an understanding of legal and privacy issues to designing software applications and IT systems.
- Contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions.
- Reflect on experience and improve your own future practice.
- Locate and use data and information and evaluate its quality with respect to its authority and relevance.
- Demonstrate mastery of theoretical knowledge and reflect critically on theory and professional practice or scholarship.
- Plan and execute a substantial research-based project, capstone experience and/or piece of scholarship.
Fees and FEE-HELP
Indicative annual fee in 2026: $35,520 (domestic full-fee paying place)
Additional expenses:
- Student services and amenities fee (SSAF): $365 maximum fee for 2026.
- Other items related to your program include field trips, textbooks and equipment.
The amounts quoted are indicative fees per annum and are based on a standard year of full-time study (96 credit points). A proportionate fee applies for more or less than the full-time study load. Fees are adjusted on an annual basis. These fees should only be used as a guide.
A student’s fee may vary depending on:
- The number of courses studied per term.
- The choice of major or specialisation.
- Choice of courses.
- Credit from previous study or work experience.
- Eligibility for government-funded loans.
FEE-HELP loans are available to assist eligible full-fee paying domestic students.