This course covers essential aspects of data handling, cleaning and documentation, including error checking, handling missing data, outliers, and inconsistencies. It emphasizes best practices for readable code, logging, and data standardization, along with exploratory data analysis and visualization. Additionally, it addresses data formatting for pharmacometrics modeling, considerations for large datasets, research data management skills, legal aspects like GDPR, and software tools for reproducible research using R and RStudio.
- Teacher: Bindels Laure
- Teacher: Lorent Joseph
- Teacher: Niyigena Emmanuel
- Teacher: Symul Laura