Yes, there is meaningful hands-on work, but it's mostly guided rather than open-ended. You'll build electronic data capture instruments in REDCap, test study forms, critique a poorly designed survey, and create a de-identified survey of your own. That practice helps you apply each idea as you learn it, instead of only reading about clinical data workflows.

The course covers the main parts of clinical research data management, from planning variables and collection methods to organizing forms and study procedures. It also looks at standards, regulatory compliance, data quality, de-identification, data sharing, and survey-based data collection. Overall, you'll see how good data management supports a study from setup through close-out.

After finishing, you should be able to plan a basic data collection strategy for a clinical study and make better decisions about how data should be captured and organized. You'll also be able to build or review simple REDCap forms, spot common data quality issues, and understand core compliance and privacy considerations. For example, you could outline baseline and visit forms for a prospective study or design a cleaner survey for participant data collection.

It's more concept-first than project-heavy, with guided practice throughout. The course spends a lot of time explaining why good data planning, compliance, and quality matter, then reinforces that through walkthroughs, quizzes, and assignments.