Units / EPM5033
EPM5033 · Programming principles for health data analytics using Python
2026 Handbook6 credit pointsLevel 5Department of Epidemiology and Preventive Medicine
Last checked: 23 Aug 2026 UTCOverview
You will gain the skills in the Python programming language regularly used in health data analytics. You will be equipped to implement several commonly used Python libraries specifically designed for data manipulation, exploration, visualisation, and machine learning. You will develop the knowledge and skills of programming in Python, including foundational skills such as programming syntax, functions, data and file management and an introduction to linear and binary regression. The common Python library packages such as Numpy, Matplotlib and Pandas will be introduced to enable you to describe, visualise, analyse and interpret health data. Using these industry standard packages, you will investigate contemporary case studies and engage in weekly programming exercises to build your applied skills throughout the unit.
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Caulfield | First semester | Teaching activities are on-campus (ON-CAMPUS) |
Assessment
The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | 4 x quizzes | Quiz / Test | 20% | — |
| 2 | Report: Data frames and visualisations | Project | 35% | — |
| 3 | Report: Regression and classification (2,100 words) | Project | 35% | — |
| 4 | Reflection (600 words) | Written | 10% | — |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
prohibitions
- FIT9136 — Algorithms and programming foundations in Python
Learning outcomes
- Apply foundational programming principles using the Python language
- Describe and implement basic elements and various data types in Python
- Investigate and apply relevant Python packages for health data analytics
- Illustrate appropriate data wrangling, visualisation and basic regression techniques in Python for health data analytics
- Solve problems in a variety of health-related contexts using the Python programming language
- Reflect on your learning, and how it might relate to future learning experiences.
- Critically assess and effectively use artificial intelligence (AI) tools responsibly, with transparency and specific to health data analytics
Workload
Twelve hours per week, consisting of (on average): • 4 hours per week for reading and working through core learning 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 self-directed study (assignment and quiz preparation) No residential component is required.
| Activity | Duration |
|---|---|
| Tutorials | — |
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