Overview
This capstone course develops the skills required to complete an applied data investigation from initial problem formulation through communication of findings. Students define an answerable question, establish objectives and success criteria, review relevant literature and data sources, and acquire or collect appropriate data. The course addresses data governance, privacy, ethics, data quality assessment, cleaning, exploratory analysis, statistical reasoning, visualization, model selection, validation, uncertainty, and interpretation.
Students integrate programming, database, statistical, and visualization tools in a supervised independent project. Emphasis is placed on reproducibility, transparent documentation, responsible data use, methodological justification, and critical evaluation of limitations. Structured milestones, feedback cycles, interim demonstrations, and a final written and presented investigation support the development of professional analytical practice for technical and nontechnical audiences.
Learning Outcomes
- Formulate an answerable applied data question with clearly defined objectives, scope, success criteria, and ethical considerations.
- Evaluate literature, data sources, governance requirements, data quality, privacy risks, and limitations relevant to an applied investigation.
- Acquire, clean, organize, and document data using appropriate programming, database, and data-management techniques.
- Select and apply statistical, computational, visualization, and modelling methods appropriate to the research question and data characteristics.
- Validate analytical procedures and results, quantify or communicate uncertainty, and evaluate sources of bias, error, and model limitation.
- Synthesize findings into a reproducible analytical workflow with clearly documented assumptions, decisions, code, and supporting evidence.
- Communicate analytical findings effectively to technical and nontechnical audiences through a written report and oral or visual presentation.
- Defend methodological choices and conclusions using evidence, critical reflection, and responsible data-practice principles.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 2 hours | Weekly | First term |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Weekly | All semester |
| Workshop | 1 hour | Fortnightly | Second term |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Project proposal and success criteria | 10.00% |
| Assignment | Annotated literature and data-source review | 10.00% |
| Deliverable | Interim progress demonstration | 15.00% |
| Deliverable | Reproducible analysis workflow and code repository | 20.00% |
| Capstone | Technical report | 25.00% |
| Deliverable | Oral or visual presentation | 10.00% |
| Assignment | Reflective evaluation | 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.
