R Workflows in Azure Machine Learning for Athletic Data Analysis
Regular talk, 10:10-10:25
The fast-paced needs of the University of Oregon Athletics Department contrast with the slow work often required for rigorous data science. Through this project, we have developed a framework with the goal of making the ‘slow work’ a bit faster. Through using R integrated into Microsoft Azure’s Machine Learning Studio, we are able to integrate data, create and collaborate on code and build machine learning pipelines. Azure allows us to create and access cloud computers to run code quicker, use Jupyter notebooks to create and run code in different languages, create modular pipelines and to collaborate as a team. The integration of R into Azure has opened new doors and increased efficiency for our team. This talk will cover the challenges of conducting data science within a fast-paced athletics environment, and how far we have come by using R and Azure for our data analysis.
 | Pronouns: she/herEugene, OREmily is a Research Data Science Assistant at the University of Oregon for a project regarding injury modeling and prevention. The project is shared by the Data Science and Athletics departments, as part of the Wu Tsai Human Performance Alliance. In addition to her research work, Emily is a Workshop Coordinator for UO Libraries Data Services, where she organizes and instructs programming workshops and data-related events. She received a BS with honors in Economics and International Studies from the University of Oregon, and has plans to attend Syracuse University for a MA in International Relations in Fall 2024. |