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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 you to develop these skills and equips you 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 you to focus your studies by choosing a stream in biostatistics, machine learning or a combination of both.

Course structure

Part A. Foundation studies54 credit points

You must complete the following units including only one of EPM5003 or FIT5197

Part B. Applied studies24 credit points

You must complete the following units including only one of EPM5031 or EPM5032.

Note: Enrolment into EPM5031 is by invitation from the Course Director. The unit is available to a limited number students, and selection is based on academic achievement throughout the course.

Part C. Discipline studies18 credit points

You must complete one of the following disciplines

Biostatistics studies

18 credit points

You must complete EPM5028 and two units (12 credit points) from the following.

Note: Permission is required to enrol in ETC5523 Communicating with Data.

Machine learning studies

18 credit points

You must complete two of the following units (12 credit points), and one elective from the Machine learning elective studies list below

Machine learning elective studies

6 credit points

You must complete one of the following units.
Note: Permission is required to enrol in ETC5523 Communicating with data.

Health data analytics general studies

18 credit points

You must complete 18 credit points from the following units

The handbook's description of this structure

The course is structured into three parts: Part A. Foundation studies, Part B. Applied studies, and Part C. Discipline studies.

Part A. Foundation studies

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 studies

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

Part C. Discipline studies

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

Course progression map

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

Units are 6 points unless otherwise stated.

Part A. Foundation studies (54 points)

You must complete the following units including one only of EPM5003 or FIT5197:

EPM5026 Mathematical foundations for biostatistics
EPM5027 Regression modelling for biostatistics I
EPM5029 Introduction to health data analytics
EPM5033 Programming principles for health data analytics using Python
ETC5510 Introduction to data analysis
FIT5196 Data wrangling
FIT9136 Introduction to Python programming
MPH5040 Introductory epidemiology
EPM5003 Principles of statistical inference
FIT5197 Statistical data modelling

Part B. Applied studies (24 points)

You must complete the following units including one only of EPM5031 or EPM5032.
Note: Enrolment into EPM5031 is by invitation from the Course Director. The unit is available to a limited number students, and selection is based on academic achievement throughout the course.

EPM5030 Human health and disease processes
ETC5250 Introduction to machine learning
MPH5289 Professional practice development
EPM5031 Health data analytics project
EPM5032 Applied health data analytics group case study

Part C. Discipline studies (18 points)

You must complete one of the following disciplines.

Biostatistics studies (18 points)

You must complete EPM5028 and two units (12 points) from the following.

Note: Permission is required to enrol in ETC5523 Communicating with data

EPM5028 Regression modelling for biostatistics II
BMS5021 Introduction to Bioinformatics
EPM5001 Health indicators and health surveys
EPM5007 Design of randomised controlled trials
EPM5008 Longitudinal and correlated data analysis
EPM5013 Bayesian statistical methods
EPM5018 Causal inference
ETC5521 Diving deeply into data exploration
ETC5523 Communicating with data
ETC5555 Statistical machine learning
FIT5147 Data exploration and visualisation

Machine learning studies (18 points)

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

FIT5047 Fundamentals of artificial intelligence
FIT5201 Machine learning
FIT5212 Data analysis for semi-structured data

Machine learning elective studies

You must complete one of the following units.

Note: Permission is required to enrol in ETC5523 Communicating with data

EPM5013 Bayesian statistical methods
EPM5028 Regression modelling for biostatistics II
ETC5521 Diving deeply into data exploration
ETC5523 Communicating with data
ETC5555 Statistical machine learning
FIT5047 Fundamentals of artificial intelligence
FIT5147 Data exploration and visualisation
FIT5201 Machine learning
FIT5202 Data processing for big data
FIT5212 Data analysis for semi-structured data
FIT5215 Deep learning
FIT5217 Natural language processing
FIT9132 Introduction to databases

Health data analytics general studies (18 points)

You must complete 18 credit points from the following units.

BMS5021 Introduction to Bioinformatics
EPM5001 Health indicators and health surveys
EPM5007 Design of randomised controlled trials
EPM5008 Longitudinal and correlated data analysis
EPM5013 Bayesian statistical methods
EPM5018 Causal inference
EPM5028 Regression modelling for biostatistics II
ETC5450 Advanced R programming
ETC5513 Collaborative and reproducible practices
ETC5521 Diving deeply into data exploration
ETC5523 Communicating with data
ETC5550 Applied forecasting
ETC5555 Statistical machine learning
FIT5047 Fundamentals of artificial intelligence
FIT5147 Data exploration and visualisation
FIT5201 Machine learning
FIT5202 Data processing for big data
FIT5212 Data analysis for semi-structured data
FIT5215 Deep learning
FIT5217 Natural language processing
FIT9132 Introduction to databases

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
Produce innovative and creative solutions to health data analysis problems with the application of the appropriate research skills and reflective practice.
2
Communicate the outcomes of research to specialist and non-specialist audiences.
3
Engage in health research globally, exhibiting cross-cultural competence and ethical values related to data and health research as a responsible global citizen.
4
Apply the major theories in the field of health data analytics to incorporate health related knowledge, critical analysis and expert judgement into practice to address health problems at local, national or global levels.
5
Apply epidemiological study design theory and key areas of biostatistics and machine learning relevant to professional practice.
6
Undertake a range of complex statistical and machine learning analyses in a variety of health analytical problems using industry standard software, ensuring reproducibility and quality control.
7
Interpret sophisticated statistical and machine learning techniques consistent with contemporary professional practice.
8
Recognise the necessary sampling, data collection and technical methodologies for real-world problems and translate them into practical solutions.
9
Program to wrangle and visualise data, fit models, make predictions, and produce high quality reports and presentations.
10
Document and communicate ethical and legal issues, and norms in privacy and security, with regard to health data analytics.
11
Critically assess and effectively use artificial intelligence (AI) tools in a responsible and transparent way, specific to health data analytics.
12
Apply professional values, attributes and collaborative skills to health data analytics practice conventions.

Entry requirements

English language

Monash 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 for you 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. You will become a proficient health data analyst with not only proficient in a set of powerful computing tools but also a broad understanding of health systems, health data sources, human body systems and epidemiological principles. You gain skills in mathematics, programming, biostatistical theory and modelling, data visualisation and machine learning.

The Master of Health Data Analytics enables you to develop these skills and equips you 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 you to focus your studies by choosing a stream in biostatistics, machine learning or a combination of both.

Contacts

Academic Coordinator: Professor Andrew Forbes; Dr Joanna Dipnall

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: 2022 2023 2024 2025 20262027