Overview
Advanced Numerical Data Handling develops systematic methods for acquiring, structuring, validating, analyzing, and reporting numerical data. Students work with large, incomplete, inconsistent, and multi-source datasets using spreadsheets, SQL, statistical software, or programming languages.
Topics include data profiling, schema design, data dictionaries, file formats, validation rules, missing-value treatment, duplicate detection, normalization, unit harmonization, precision management, outlier investigation, version control, descriptive and inferential statistics, distribution analysis, correlation, regression, time-series summaries, sampling, uncertainty, and sensitivity analysis.
Students design reproducible and auditable data pipelines, assess analytical assumptions, construct clear visualizations and dashboards, and communicate technical findings to non-specialist audiences. The capstone project requires ingestion and remediation of a messy dataset, implementation of quality checks, documented analysis, interpretation of limitations, and evidence-based recommendations, with attention to privacy, security, traceability, and ethical data use.
Learning Outcomes
- Design an end-to-end numerical data pipeline that documents acquisition, transformation, validation, analysis, and reporting procedures.
- Evaluate dataset structure, provenance, completeness, consistency, and fitness for intended analytical purposes.
- Implement validation rules and quality checks for missing values, duplicates, units, precision, ranges, and schema conformity.
- Select and justify transformations, statistical methods, sampling procedures, and uncertainty measures appropriate to defined research questions.
- Analyze distributions, associations, regression relationships, and time-series summaries while testing relevant assumptions.
- Investigate outliers, anomalous records, sensitivity to analytical choices, and patterns that may produce misleading conclusions.
- Construct clear visualizations and dashboards that accurately represent numerical evidence and disclose material limitations.
- Automate repeatable analytical workflows using suitable computational, database, or statistical tools.
- Produce an auditable analysis with version-controlled documentation, reproducible computations, and traceable outputs.
- Communicate technical findings, uncertainty, limitations, and actionable recommendations to specialist and non-specialist audiences while applying privacy, security, and ethical data principles.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 2 hours | Weekly | All semester |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Weekly | All semester |
| Workshop | 2 hours | Fortnightly | All semester |
| Practicum | 3 hours | Fortnightly | Second term |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Data profiling and schema design report. | 15.00% |
| Assignment | Validation, transformation, and quality-control workflow. | 15.00% |
| Test | In-semester test on data handling and statistical analysis. | 15.00% |
| Deliverable | Reproducible analysis notebook and technical documentation. | 10.00% |
| Quiz | Short applied quizzes (5 × 1%). | 5.00% |
| Capstone | Auditable numerical data pipeline, analysis, and recommendations. | 35.00% |
| Exam | Final examination on interpretation, uncertainty, and reporting. | 5.00% |
Prerequisites
- STAT301 Machine Learning Methods
- Requirement Prior study in introductory statistics and programming or database systems.
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.