Overview
This course develops advanced applied data analysis skills for public health practice. Students examine major public health data sources, including surveillance systems, vital statistics, electronic health records, registries, surveys, administrative datasets, and other routinely collected health information.
The course addresses data dictionaries, data quality assessment, privacy, ethical stewardship, cleaning, coding, reshaping, linkage, and documentation. Statistical methods include rates and proportions, standardization, confidence intervals, group comparisons, correlation, linear and logistic regression, trend analysis, stratification, confounding, and interpretation of risk ratios, odds ratios, and rate ratios.
Through applied work with authentic datasets, students use statistical programming environments or equivalent analytical tools to construct reproducible workflows, visualizations, summary tables, dashboards, and concise technical reports. Emphasis is placed on evaluating data fitness for purpose, recognizing bias and uncertainty, communicating findings clearly, and translating analysis into evidence-informed public health recommendations.
Learning Outcomes
- Evaluate the fitness, completeness, validity, and provenance of public health datasets for specified analytical purposes.
- Apply appropriate procedures for data cleaning, coding, reshaping, linkage, quality assessment, and reproducible documentation.
- Select and justify statistical methods for rates, proportions, standardization, confidence intervals, group comparisons, correlation, regression, and trend analysis.
- Interpret risk ratios, odds ratios, rate ratios, regression estimates, confidence intervals, and measures of statistical uncertainty in public health contexts.
- Analyze the effects of stratification, confounding, selection bias, information bias, and data limitations on public health findings.
- Construct reproducible analytical workflows using a statistical programming environment or equivalent analytical tools.
- Create clear visualizations, summary tables, dashboards, and technical reports for professional public health audiences.
- Synthesize analytical findings into evidence-informed recommendations that address public health priorities and equity considerations.
- Critique the privacy, ethical, governance, and disclosure risks associated with the use and linkage of health data.
- Defend analytical decisions and communicate results accurately to technical and non-technical stakeholders.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 2 hours | Weekly | All semester |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Fortnightly | All semester |
| Workshop | 2 hours | Fortnightly | Second term |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Data management and documentation assignment | 15.00% |
| Assignment | Statistical analysis assignments (2 × 15%) | 30.00% |
| Test | Practical data analysis test | 20.00% |
| Deliverable | Technical report and public health recommendation | 20.00% |
| Exam | Final examination | 15.00% |
Prerequisites
- STAT301 Machine Learning Methods
- Requirement Prior study in epidemiology, biostatistics, or an equivalent quantitative health sciences subject
Teaching Staff & Programs
This course is delivered jointly by faculty from the participating programs listed below. In line with the Douchewater Way, the University of Sexology tailors core instruction directly to each cohort's specific discipline — adapting curriculum to program needs rather than forcing students into a one-size-fits-all model. Learn more about our approach at The Douchewater Way.