Overview
This project-based course applies data science, public health, and reproductive health principles to the investigation of questions affecting individuals and communities. Students examine surveys, clinical records, vital statistics, registries, demographic datasets, and public health indicators relating to gestational health, pregnancy, childbirth, abortion, contraception, sexually transmitted infections, infertility, maternal and infant outcomes, and gender-responsive care, including male gestational and perinatal health.
Students develop a focused research question, conduct literature and data-source reviews, design a reproducible analysis plan, prepare and document a dataset, and apply descriptive and introductory inferential methods using a selected software environment. Emphasis is placed on data quality, sampling bias, confounding, measurement limitations, causal reasoning, uncertainty, privacy, informed consent, confidentiality, data governance, and culturally responsive interpretation of sensitive health information.
The final project requires a documented analytical workflow, appropriate statistical outputs and visualizations, an interpretation of public health significance, and a critical reflection on equity, ethics, limitations, and potential impact. Findings are communicated through a project report and a presentation, dashboard, or policy brief for technical and nontechnical audiences.
Learning Outcomes
- Formulate answerable research questions concerning reproductive, gestational, perinatal, and population health.
- Evaluate datasets and data sources for relevance, quality, representativeness, completeness, and measurement limitations.
- Apply ethical, privacy, confidentiality, consent, governance, and culturally responsive principles to sensitive health data.
- Organize, clean, document, and preserve datasets through a reproducible analytical workflow.
- Select and justify suitable descriptive statistics, visualizations, association measures, risk measures, and introductory inferential methods.
- Interpret statistical results, uncertainty, and subgroup differences without overstating causation or public health significance.
- Assess disparities across demographic, geographic, socioeconomic, gender, and other relevant groups while recognizing structural and sampling-related sources of bias.
- Synthesize literature, data evidence, analytical results, and limitations into evidence-informed recommendations.
- Communicate findings clearly through a technical report and an appropriate presentation, dashboard, or policy brief.
- Reflect critically on the equity, ethical implications, data limitations, and potential impacts of reproductive health analysis.
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 | All semester |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Project proposal and data-source review | 10.00% |
| Deliverable | Data management portfolio | 15.00% |
| Assignment | Reproducible analysis plan | 15.00% |
| Test | Practical data analysis test | 20.00% |
| Capstone | Final project report and presentation | 30.00% |
| Attendance | Tutorial participation | 10.00% |
Prerequisites
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.