Overview
This capstone provides an integrative project experience in which students independently or collaboratively address a substantial real-world problem using statistical methods. Projects may draw on business, social science, environmental, health, scientific, or public-sector data and require consultation with stakeholders or clients, background research, problem formulation, data acquisition and governance, exploratory analysis, study-design evaluation, method selection, statistical modelling, validation, uncertainty assessment, sensitivity analysis, reproducible computation, and final communication.
Students develop and defend a complete statistical analysis plan, selecting and justifying appropriate methods from regression, experimental design, categorical data, time series, multivariate analysis, resampling, Bayesian methods, machine learning, and related areas. Emphasis is placed on integrating statistical theory with computation, identifying bias and limitations, checking assumptions, protecting confidentiality, distinguishing evidence from speculation, and communicating actionable conclusions to technical and non-technical audiences.
Learning Outcomes
- Formulate a substantial statistical problem in consultation with stakeholders and translate it into explicit analytical objectives.
- Evaluate relevant literature, data sources, governance requirements, and study designs to establish an appropriate evidential basis for analysis.
- Construct a complete statistical analysis plan that justifies method selection, assumptions, validation procedures, uncertainty assessment, and sensitivity analyses.
- Implement reproducible statistical workflows using documented code, version-controlled computational practices, and appropriate data-management procedures.
- Diagnose model inadequacies, bias, confounding, assumption violations, and sources of uncertainty using suitable statistical and graphical techniques.
- Synthesize analytical results from multiple methods and distinguish defensible evidence from speculation and unsupported causal claims.
- Communicate technical findings through clear visualizations, a structured technical report, an oral presentation, and actionable recommendations.
- Reflect critically on the analytical process, including project constraints, ethical responsibilities, limitations, stakeholder needs, and opportunities for improvement.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Seminar | 2 hours | Weekly | First term |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Weekly | All semester |
| Workshop | 2 hours | Fortnightly | All semester |
| Seminar | 1 hour | Weekly | Second term |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Project proposal and stakeholder brief | 10.00% |
| Assignment | Literature and data review | 10.00% |
| Deliverable | Documented code and reproducible analysis repository | 15.00% |
| Deliverable | Interim progress report and consultation | 10.00% |
| Assignment | Technical report | 25.00% |
| Capstone | Final presentation | 15.00% |
| Assignment | Reflective evaluation | 5.00% |
| Attendance | Professional participation and consultations (10 × 1%) | 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.