The final project is the capstone of the course. Its purpose is to bring together the major skills developed across the previous chapters: protocol interpretation, CRF design, REDCap database development, validation rules, data entry workflow, data quality management, R-based cleaning, descriptive analysis, visualization, reporting, documentation, and governance. The project should demonstrate not only that the learner can use tools, but that they can use them responsibly within a clinical research data management workflow.
The final project should be small enough to complete but realistic enough to show professional judgment. A suitable project may be a mock clinical cohort, a registry prototype, a small trial database, a laboratory-linked observational study, or a monitoring workflow for a sample REDCap project. The project should include a clear study scenario, a defined dataset, a database or data dictionary, quality checks, R scripts, at least one report, and documentation.
The project is not expected to be a full production system. Instead, it is a demonstration of competence. The learner should show that they can translate requirements into data structures, protect raw data, write reproducible scripts, produce meaningful outputs, and explain decisions.