Monash University
Master of Health Data Analytics
- Delivery: Face to Face
- Study Level: Postgraduate
- Duration: 24 months
- Course Type: Master's
Enhance your skills and prepare yourself to engage in every stage of a health data project. This includes identifying the health problem, exploring potential solutions, implementing advanced analytical techniques and effectively communicating the results to stakeholders.
Course overview
The Master of Health Data Analytics is designed to meet the high demand for data analysts to tackle real-world health questions, such as quantifying the effectiveness of new treatments, implementing sophisticated modelling of patient outcomes and pathways and developing algorithms for diagnostic imaging classification.
The course will enable you to develop these skills and equip you to contribute throughout the full length of a health data project, from conceptualising the health problem to determining an avenue for solution, implementing cutting-edge analytical solutions and communicating the results to stakeholders.
Graduates with such skills are in increasingly high demand in academia, government and industry worldwide. This interdisciplinary course enables you to focus your studies by selecting a stream in biostatistics, machine learning, or a combination of both.
Key facts
What you will study
The course consists of 96 credit points and structured into three parts: Part A. Advanced expertise, Part B. Applied health data analytics and Part C. Health data analytics stream.
You must complete the following units
- Principles of statistical inference
- Mathematical foundations for biostatistics
- Regression modelling for biostatistics 1
- Introduction to health data analytics
- Programming principles for health data analytics using Python
- Introduction to data analysis
- Data wrangling
- Algorithms and programming foundations in Python
- Introductory epidemiology
Entry requirements
You need to satisfy the following entrance requirements to be considered for entry to this course.
Minimum Entry Requirements (Domestic students) Qualifications
An Australian bachelor degree (or equivalent) with a Weighted Average Mark (WAM) of at least 60%.
English requirements
- IELTS (Academic): 6.5 Overall score, with minimum band scores: Listening 6.0, Reading 6.0, Writing 6.0 and Speaking 6.0.
- Pearson Test of English (Academic): 58 Overall score, with minimum scores: Listening 50, Reading 50, Speaking 50 and Writing 50.
- TOEFL Internet-based test: 79 Overall score, with minimum scores: Reading 13, Listening 12, Speaking 18 and Writing 21.
- Equivalent approved English test.
Recognition of Prior Learning
You may be able to get credit for your course based on prior formal, non-formal or informal learning. To apply, you will need to provide supporting documentation outlined by the university. Contact the university for more information.
Outcomes
Learning outcomes
These course outcomes are aligned with the Australian Qualifications Framework level 9 and Monash Graduate Attributes.
Upon successful completion of this course, it is expected that you will be able to:
- Be critical and creative scholars able to produce innovative and creative solutions to health data analysis problems with the application of the appropriate research skills
- Be able to effectively communicate the outcomes of their research to specialist and non-specialist audiences.
- Be responsible and effective global citizens who can engage in planetary health research, exhibit cross-cultural competence and demonstrate necessary ethical values related to data and health research.
- Be able to apply the major theories in the field of health data analytics to incorporate health-related knowledge, critical analysis, expert judgement, autonomy, adaptability and responsibility into practice to address health problems at local, national or global levels.
- Possess a sound understanding of epidemiological study design and the theory and application of key areas of biostatistics and machine learning relevant to professional practice.
- Have acquired skills in complex statistical and machine learning analyses to handle a variety of analytical problems using both traditional and modern statistical techniques and programming in a range of statistical software, ensuring reproducibility and quality control.
- demonstrate the ability to interpret and understand biostatistical and machine learning techniques to a level of depth and sophistication consistent with contemporary professional practice.
- Be able to recognise the necessary sampling, data collection and technical methodologies for real-world problems and translate them into practical solutions.
- possess the programming skills to wrangle and visualise data, fit models, make predictions and produce high-quality reports and presentations
- Be able to effectively and efficiently document and communicate ethical and legal issues and norms in privacy and security, regarding the practice of health data analytics.
Fees and FEE-HELP
Indicative 2026 first-year fee: $35,100 (domestic full-fee paying place).
The estimated per-unit fee is calculated using the annual average first-year fee. It is based on a full-time study load of 48 credit points (eight units).
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.