RMIT University
Master of Analytics
- Delivery: Face to Face
- Study Level: Postgraduate
- Duration: 24 months
- Course Type: Master's
Become a specialist in analytics and build on skills in computer science, engineering and business.
Course overview
The Master of Analytics builds on the tradition of statistics and operations research, bringing together tools and expertise from computer science, engineering and business.
This blend of disciplines makes analytics unique in its capacity to solve critical real world problems. You will learn how to use data to make informed decisions that make significant contributions to the success of organisations, which provides a rewarding and valued career path.
This degree allows you to choose from a diverse range of electives combined with a strong core of statistics and operations research.
Key facts
July, 2026
What you will study
To graduate you must complete the following. All courses listed may not be available each semester. Each course is valued at 12 credit points.
Complete the following courses:
- Applied Analytics
- Database Concepts
- Data Wrangling
AND
Complete up to 24 credit points (two courses) from the following. Note: if you choose 24 credit points from this list, you may only choose 12 credit points (one course) from the second list.
- Essential Mathematics
- Data Visualisation and Communication
- Machine Learning
- Time Series Analysis
AND
Complete up to 24 credit points (two courses) from the following. Note: if you chose 24 credit points from the previous list, you may only choose 12 credit points (one course) from this list.
- Optimisation for Decision Making
- Applied Bayesian Statistics
- Analysis of Categorical Data
- Design and Analysis of Experiments
- Forecasting
- Multivariate Analysis Techniques
- Regression Analysis
- Statistical Inference
- Statistics of Quality Control and Performance Analysis
- Stochastic Processes and Applications
- Game Theory and its Applications
- Advanced Optimisation
- Questionnaire and Research Design
- System Dynamics
- Sports Analytics
- Introduction to Statistical Computing
- Statistical Data Science
AND
Complete 24 credit points (two courses) from the Science Options Courses list.
Entry requirements
Academic requirements
Applicants must have one of the following:
- A bachelor's degree.
- At least 10 years of relevant work experience.
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 employer so RMIT can seek confirmation); and evidence of any relevant professional development undertaken.
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 details.
Recognition of Prior Learning
If you have successfully completed a qualification majoring in analytics, statistics, operations research or a relevant discipline, you may be eligible for exemptions. Contact the university or visit their website for more details.
Outcomes
Learning outcomes
The following are the key learning outcomes developed in the program, which will make you, as a graduate, relevant to current industry and business requirements:
- The ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions.
- The ability to reflect on experience and improve your own future practice.
- The ability to apply the principles of lifelong learning to any new challenge.
- An understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.
- The ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems.
- An understanding of the balance between the complexity/accuracy of the mathematical/statistical models used and the timeliness of the delivery of the solution.
- The ability to contribute to professional work settings through effective participation in teams and organisation of project tasks.
- The ability to constructively engage with other team members and resolve conflicts.
- The ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral) and to tailor the style and means of communication to different audiences. Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.
- The ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.
- Develop the cognitive skills to review critically, analyse, consolidate and synthesise knowledge to identify and provide solutions to complex problems with intellectual independence.
- Use initiative and judgement in planning, problem solving and decision making in professional practice and/or scholarship.
- Take responsibility and accountability for one's own learning and professional practice and in collaborations with others within broad parameters.
Career outcomes
Graduates are employed by a variety of scientific, commercial and government enterprises, most commonly as data scientists, statisticians, business analysts, consultants, modellers and researchers.
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.