EPI 223: Introduction to Data Management and Analysis in SAS & R

Course Description

Effective data management is a cornerstone of high-quality research ensuring that analyses are accurate and reproducible. As studies increasingly rely on complex, large-scale datasets, researchers must be able to organize, clean, and analyze data efficiently and transparently. Learning both SAS and R provides students with essential tools for this work. SAS is widely used in clinical research and regulatory environments for its reliability, standardized procedures, and ability to handle large datasets in a structured way (detailed and clear presentation of results is a bonus!). R offers powerful capabilities for statistical modeling, data visualization, and reproducible research, and is increasingly used in academic settings. Introducing both platforms in an introductory course prepares students to work across a variety of research environments and equips them with the practical skills needed to conduct rigorous and meaningful analyses. This course provides hands-on introduction to basic data management and analysis techniques using SAS (weeks 1-5) and R (weeks 6-10). Data management topics include: introduction to SAS/R and relevant syntax, importing data, creating and reading datasets, data cleaning and validation, creating new variables, and combining data sets. Analysis techniques include: basic descriptive statistics (e.g., means, frequency) and bivariate procedures for continuous and categorical variables (e.g., t-tests, chi-squares).