Overview
This culminating, project-based course requires students to integrate the full data science lifecycle in response to an authentic client, organizational, research, or community problem. Students undertake stakeholder discovery, requirements gathering, scope definition, success-metric design, feasibility analysis, and ethical risk review before developing a researchable problem and project plan.
Students acquire, document, clean, govern, and analyse data; engineer features; develop and evaluate suitable models; and translate findings into an interpretable decision-support product. Depending on project requirements, outputs may include reproducible code, data pipelines, trained models, interactive dashboards, analytical reports, APIs, or prototype deployments.
The course emphasizes version control, software testing, documentation, experiment tracking, reproducibility, privacy, security, fairness, and responsible communication of uncertainty. Students apply agile planning, peer review, client communication, presentation, and change-management practices while producing maintainable technical and analytical artifacts for technical and nontechnical audiences.
Learning Outcomes
- Manage an end-to-end data science project from stakeholder discovery and requirements analysis through delivery and evaluation.
- Formulate researchable questions, define success metrics, and justify project scope and feasibility using technical, organizational, and ethical criteria.
- Acquire, document, clean, govern, and assess data for quality, provenance, privacy, security, and fitness for purpose.
- Select, implement, and justify statistical, machine learning, and analytical methods appropriate to the project objectives and data characteristics.
- Evaluate models and decision-support products against technical performance measures, practical requirements, fairness considerations, and uncertainty.
- Construct maintainable and reproducible artifacts using version control, testing, documentation, experiment tracking, and appropriate deployment practices.
- Analyse project limitations, risks, assumptions, and changing requirements, and formulate defensible mitigation strategies.
- Communicate findings, recommendations, and uncertainty effectively to technical and nontechnical stakeholders through written, visual, and oral formats.
- Defend methodological choices and recommendations using evidence from analysis, validation, stakeholder requirements, and project documentation.
- Reflect critically on individual contribution, team processes, client engagement, and the professional responsibilities of data science practice.
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 |
| Practicum | 1 hour | Weekly | All semester |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Project Proposal | 10.00% |
| Deliverable | Milestone Reviews (3 × 5%) | 15.00% |
| Deliverable | Technical Portfolio | 25.00% |
| Assignment | Final Report | 20.00% |
| Capstone | Project Presentation | 15.00% |
| Assignment | Individual Reflection | 10.00% |
| Attendance | Client and Team Engagement | 5.00% |
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.
