EPM5029 Introduction to health data analytics
Faculty of Medicine, Nursing and Health Sciences
EPM5029 Introduction to health data analytics is a level 5, 6-credit-point, postgraduate unit from the Faculty of Medicine, Nursing and Health Sciences, offered in 2027 in Semester 1 at Caulfield. It has no prerequisites and unlocks 1 unit.
- Credit points
- 6
- Offered in 2027
- Semester 1
- Caulfield
- Assessment
- No exam
- 4 tasks
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Requisites
Before EPM5029
No prerequisites or corequisites besides the enrolment rules below.
After EPM5029
1 unit list EPM5029 as a prerequisite or corequisite.
Enrolment rules
Must be enrolled in M6036 Master of Health Data Analytics, M6024 Master of Public Health, M6030 Master of Biotechnology, M6008 Master of Health Management, or M6028 Master of Clinical Research.
Overview
In this unit you will gain foundational knowledge for the study of Health Data Analytics by introducing key health, statistical and machine learning concepts. You will be introduced to the key ethical and privacy issues when using health data. You will obtain a basic understanding of common statistics used in health data analytics and the use machine learning techniques and their applications using the R software package.
You will be presented with the classification of health data analytics tasks into description, prediction and explanation.
You will be introduced to the different sources of health data, including linked data, together with the appropriate methods for analysis and strengths and weaknesses surrounding their use. You will discuss the fast-changing nature of data analytics and current topics of major interest including precision and evidence-based medicine, and the surge in data-driven evaluation and policy.
Offerings in 2027
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Caulfield | On campus |
Assessment
- Critical reflections x 3Written15%
- Ethics and privacy (900 words)Exercise15%
- Health data sources (2,100 words)Exercise35%
- Considering health literature (2,100 words)Exercise35%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Explain the appropriate use of health data analytics in health research.
- 2
Appraise and summarise ethics and privacy with respect to health data analytics.
- 3
Evaluate different health data sources for health data analytics.
- 4
Differentiate between descriptive, predictive and explanatory research problems, and statistical compared with machine learning models.
- 5
Evaluate the different uses of linked health data and explain their strengths and weaknesses.
- 6
Implement the steps in health data analytics.
- 7
Critically assess and effectively use artificial intelligence (AI) tools responsibly, with transparency and specific to health data analytics
Workload and teaching
- Tutorials2 hours
Twelve hours per week, consisting of (on average)
- 4 hours per week for reading core material
- 1-hour online lecture materials (multiple videos)
- 2-hour face-to-face tutorial
- 2 hours per week for online communication via online moderated discussions, and
- 3 hours per week for assignment and quiz preparation
No residential component is required.
Contacts
- Chief Examiners
- Dr Joanna Dipnall
- Unit Coordinators
- Dr Joanna Dipnall
Common questions
What are the prerequisites for EPM5029?
EPM5029 has no prerequisites, but enrolment rules apply.
What can I take after EPM5029?
EPM5029 is a prerequisite or corequisite for 1 unit, including EPM5032.
When is EPM5029 offered?
In 2027, EPM5029 runs in Semester 1 at Caulfield.
Does EPM5029 have an exam?
No. EPM5029 has 4 assessment tasks and no exam.