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Requisite map

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. A proficient health data analyst requires more than a set of powerful computing tools – they must have a broad understanding of health systems, health data sources, human body systems and epidemiological principles married with core data analytics skills in mathematics, programming, biostatistical theory and modelling, data visualisation and machine learning. The Master of Health Data Analytics enables students to develop these skills and equips them to contribute along the full length of a health data project, from conceptualisation of the health problem, to determining an avenue for solution, implementation of cutting-edge analytical solutions, and communication of the results to stakeholders. Graduates with such skills are in increasingly high demand in academia, government and industry worldwide. This interdisciplinary course allows students to choose a stream in biostatistics, machine learning or a combination of both.

Course structure

Part A. Advanced expertise48 credit points

You must complete the following units

Part B. Applied health data analytics24 credit points

You must complete the following units, an additional unit and a capstone/project unit below.

Additional unit

6 credit points

You must complete one of the following units

Capstone or project

6 credit points

You must complete one of the following units. Note: The practical project is available to a limited number of students based on WAM.

Part C. Health data analytics stream24 credit points

You must complete one of the following streams

Biostatistics stream

24 credit points

You must complete the following units and one elective from the list below

Biostatistics electives

6 credit points

You must complete one of the following units

Machine learning stream

24 credit points

You must complete two of the following units (12 credit points), one additional unit and one elective from the list below.

Additional unit

6 credit points

You must complete one of the following units

Machine learning electives

6 credit points

You must complete one of the following elective units

General stream

24 credit points

You must complete two of the following units (12 credit points) and two elective units from the list below.

General stream elective units

12 credit points

You must complete 12 credit points from the following units

The handbook's description of this structure

The course is structured into three parts: Part A. Advanced expertise, Part B. Applied Health Data Analytics, and Part C. Health Data Analytics stream.

Part A. Advanced Expertise

In these studies you will gain knowledge and skills in the foundation units across health data analytics, programming, biostatistics, epidemiology and data analytics.

Part B. Applied Health Data Analytics

In these studies you will gain expertise and competence in key aspects of health data analytics.

Part C. Health Data Analytics Stream

You will be able to choose biostatistics, machine learning, or complete a general stream in health data analytics.

Course progression map

The course progression map provides guidance on unit enrolment for each semester of study.

The course comprises 96 points structured into three parts: Part A. Advanced expertise (48 points), Part B. Applied health data analytics (24 points) and Part C. Health data analytics stream (24 points).

Units are 6 points unless otherwise stated.

Part A. Advanced Expertise (48 points)

You must complete the following units:

EPM5029 Introduction to health data analytics
FIT9136 Algorithms and programming foundations in python
EPM5026 Mathematical foundations for biostatistics
ETC5510 Introduction to data analysis
MPH5040 Introductory epidemiology
EPM5003 Principles of statistical inference
EPM5027 Regression modelling for biostatistics I
FIT5196 Data wrangling

Part B. Applied Health Data Analytics (24 points)

You must complete:

EPM5xxx - Human health and disease
FIT5149 Applied data analysis or ETC5250 Introduction to machine learning
MPH5289 Professional practice development
EPM5xxx Master of health data analytics practical project (limited number of students based on WAM) or EPM5xxx Capstone unit - Big data in health

Part C. Health Data Analytics Stream (24 points)

You must complete one of the following streams:

Biostatistics stream

You must complete the following 3 units (18 points):

EPM5018 Causal inference or BMS5021 Introduction to bioinformatics
EPM5028 Regression modelling II
EPM5008 Longitudinal data analysis

and one Biostatistics elective unit (6 points) from:

FIT5147 Data exploration/visualisation
BMS5022 Advanced Bioinformatics
EPM5013 Bayesian statistical methods
ETC5555 Statistical machine learning
ETC5521 Exploratory data analysis
ETC5523 Communicating with Data (with permission)

Machine learning stream

You must complete two units (12 points) from:

FIT5212 Semi-structured data
FIT5201 Machine learning
FIT5047 Fundamentals of AI

and one unit (6 points) from:

FIT5215 Deep learning or FIT5217 Natural language processing

and one Machine learning elective unit (6 points) from:

FIT5147 Data exploration and visualisation
FIT5201 Machine learning
EPM5028 Regression modelling II
EPM5013 Bayesian statistical methods
ETC5555 Statistical machine learning
ETC5521 Exploratory data analysis
ETC5523 Communicating with Data (with permission)

General stream

You must complete two units (12 points) from

EPM5008 Longitudinal data analysis
EPM5018 Causal inference or BMS5021 Introduction to bioinformatics
EPM5028 Regression modelling II
FIT5047 Fundamentals of AI
FIT5201 Machine learning
FIT5212 Semi structured data
FIT5215 Deep learning
FIT5217 Natural language processing

and two General stream elective units units (12 points) from:

EPM5008 Longitudinal data analysis
EPM5018 Causal inference
BMS5021 Introduction to bioinformatics
EPM5028 Regression modelling II
FIT5047 Fundamentals of AI
FIT5201 Machine learning
FIT5212 Semi structured data
FIT5215 Deep learning
FIT5217 Natural language processing
BMS5022 Advanced bioinformatics
EPM5013 Bayesian statistical methods
EPM5028 Regression modelling II
FIT5147 Data exploration and visualisation
FIT5201 Machine learning
ETC5550 Applied Forecasting
ETC5521 Exploratory Data Analysis
ETC5555 Statistical machine learning
ETC5513 Collaborative and Reproducible Practices
ETC5523 Communicating with Data

Learning outcomes

These course outcomes are aligned with the Australian Qualifications Framework and Monash Graduate Attributes.

Upon successful completion of this course it is expected that you will be able to:

1
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
2
be able to effectively communicate the outcomes of their research to specialist and non-specialist audiences
3
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
4
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.
5
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
6
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 program in a range of statistical software, ensuring reproducibility and quality control
7
demonstrate the ability to interpret and understand biostatistical and machine learning techniques to a level of depth and sophistication consistent with contemporary professional practice
8
be able to recognise the necessary sampling, data collection and technical methodologies for real-world problems and translate them into practical solutions
9
possess the programming skills to wrangle and visualise data, fit models, make predictions, and produce high quality reports and presentations
10
be able to effectively and efficiently document and communicate ethical and legal issues, and norms in privacy and security, with regards to the practice of health data analytics

Entry requirements

English language

Monash minimum: Level A, that is: IELTS (Academic): 6.5 overall (no band lower than 6.0); or Pearson Test of English (Academic): score of 58 overall with no band lower than 50; or TOEFL Internet-based test: score of 79 overall with minimum scores: Writing: 21, Listening: 12, Reading: 13 and Speaking: 18; or Equivalent approved English test

More information

Other information

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. A proficient health data analyst requires more than a set of powerful computing tools – they must have a broad understanding of health systems, health data sources, human body systems and epidemiological principles married with core data analytics skills in mathematics, programming, biostatistical theory and modelling, data visualisation and machine learning. The Master of Health Data Analytics enables students to develop these skills and equips them to contribute along the full length of a health data project, from conceptualisation of the health problem, to determining an avenue for solution, implementation of cutting-edge analytical solutions, and communication of the results to stakeholders. Graduates with such skills are in increasingly high demand in academia, government and industry worldwide. This interdisciplinary course allows students to choose a stream in biostatistics, machine learning or a combination of both.

Contacts

Academic Coordinator: Dr Joanna Dipnall; Professor Andrew Forbes

Common questions

How long is Master of Health Data Analytics?

2 years full time, 96 credit points. At 24 credit points a semester, that is 4 semesters of full-time study.

Where can I study Master of Health Data Analytics?

At Alfred Hospital.

How do I plan my Master of Health Data Analytics units?

Open the course in the MonMap planner. It lays out your semesters, checks prerequisites as you drag units in, and tracks the credit points each requirement still needs.

Course details

Qualification: Master's Degree (Coursework)
AQF level: Level 9
Credit points: 96
Full time: 2 Years
Part time: 4 Years
Maximum time: 6 years

Faculty: Faculty of Medicine, Nursing and Health Sciences
CRICOS code: 106844H
Abbreviation: MHDA
Award title: Master of Health Data Analytics
Handbook years: 20222023 2024 2025 2026 2027