Overview
This course develops computational foundations for modern statistical practice, with emphasis on reliable programming, numerical methods, algorithmic reasoning, and reproducible analysis. Students use a primary statistical programming language and associated packages to implement data-analysis workflows involving vectors, matrices, functions, control structures, object-oriented or functional programming, data import and transformation, validation, visualization, modelling, reporting, and workflow automation.
Topics include numerical linear algebra, optimization, random-number generation, Monte Carlo simulation, numerical integration, resampling, iterative estimation, computational complexity, debugging, testing, version control, documentation, and reproducible research pipelines. Advanced applications address vectorized and parallel computation, database connections, APIs, and scalable analysis. Students evaluate computational efficiency and numerical stability, apply secure and ethical data practices, and communicate computational decisions to technical and nontechnical audiences.
Learning Outcomes
- Implement statistical procedures as reliable, readable, and maintainable programs.
- Design and evaluate algorithms for data transformation, numerical computation, simulation, and statistical estimation.
- Apply numerical linear algebra, optimization, random-number generation, integration, and resampling methods to statistical problems.
- Diagnose numerical instability, computational inefficiency, software defects, and data-validation failures.
- Construct clear visualizations and computational reports that communicate statistical results and methodological decisions.
- Evaluate computational complexity, scalability, security, ethical implications, and reproducibility in statistical workflows.
- Develop tested, documented, version-controlled, and reproducible analysis pipelines using statistical software and relevant packages.
- Synthesize computational results and methodological evidence for technical and nontechnical audiences.
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 |
|---|---|---|
| Quiz | Programming quizzes (5 × 2%) | 10.00% |
| Assignment | Computational assignments (2 × 10%) | 20.00% |
| Test | Practical coding test | 15.00% |
| Capstone | Reproducible statistical computing project | 25.00% |
| Exam | Final examination | 30.00% |
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.