The Friedman School pursues cutting-edge research and education from cell to society, including in molecular nutrition, human metabolism, population studies, clinical trials, nutrition interventions and behavior change, communication, food systems and sustainability, global food insecurity, humanitarian crises, and food economics and policy.
This project-based course capitalizes on student interests to formulate research questions with understanding of data limitations, conduct multi staged data analysis, and select proper data visualization and graphical representation tools. Students will learn advanced modern analytical tools and techniques essential for analysis in a variety of disciplines such as Climate, Environment, Nutrition and Health applications (knowledge of only one of these disciplines. This course also covers research design, the scientific method, data quality and validity, data management, and research ethics in data analysis. Students should attempt to identify data sets relevant to their specific interests prior to the course. Instructor will approve data set suitability. If students cannot identify appropriate datasets, the instructor will provide a dataset.
Students should have basic working knowledge of statistical methods in environmental and/or nutrition research and epidemiology. Recommended courses that cover those topics include: Statistical Methods for Nutrition Research I and II (NUTR 0209/0309) or Statistical Methods in Nutrition Research and Regression Analysis for Nutrition Policy (NUTR 0207/NUTR 0307) or equivalent. Ability to analyze data by use of R is preferable, but students may utilize other statistical programs as long as those programs are sufficient for the analysis that is proposed.