Interdisciplinary Approach to Prepare Undergraduates for Data Science Using Real-World Data from High Frequency Monitoring Systems

With support from the National Science Foundation (NSF) Improving Undergraduate STEM Education Program, this project aims to help the incorporation of data science concepts and skill development in undergraduate courses in biology, computer science, engineering, and environmental science. Through a collaboration between Virginia Tech, Vanderbilt University, and North Carolina Agricultural and Technical State University, we are developing interdisciplinary learning modules based on high frequency, real-time data from water and traffic monitoring systems. The learning module topics will include Interdisciplinary Learning, Data Analytics, and Industry Partnerships. These topics will facilitate incorporation of real-world data sets to enhance the student learning experience and they are broad enough that they can incorporate other data sets in the future. Such expertise will better prepare students to enter the STEM workforce, especially those STEM professions that focus on smart and connected computing. The project will investigate how and in what ways the modules support student learning of data science. The project is also investigating how implementation of the modules varies across the collaborating institutions. It is expected that the project will define key considerations for integrating data science concepts into STEM courses and will host workshops to introduce faculty to these considerations and strategies so they can incorporate the learning modules into the STEM courses that they teach. Collaborators : V. Lohani, R. Dymond, & K. Xia (Virginia Tech); G. Biswas, Erin Hotchkiss, C. Vanags (Vanderbilt); M.K. Jha, N. Aryal, & E.H. Park (North Carolina Agricultural & Technical State University).

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