Interdisciplinary data science training faculty workshop

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This project aims to serve the national interest by increasing the number of faculty and undergraduate students trained in data science. Business, industry, and government agencies have a tremendous demand for science, technology, engineering, and mathematics (STEM) majors, especially those who have a background in data science. This workshop will help faculty better prepare undergraduate students for industrial careers by learning more about data science, engaging with local industrial entities to assist them on data science problems, and completing their interdisciplinary data science workshop project (e.g., mentoring undergraduate students on an interdisciplinary research project in data science, co-teaching an interdisciplinary data science class, or working together in creating an interdisciplinary data science program). In this hands-on workshop at Brigham Young University, the 40 participants will be introduced to the fields of data science, statistical learning, and machine learning Participating faculty members will attend jointly with a colleague from the same institution with one faculty being in the mathematical sciences and the other being in a different STEM discipline. They will learn Python and its data science applications, and they will gain experience using the major classification algorithms such as K-means clustering, regression, and trees on actual data. The workshop will consist of both presentations by experts in data science and a group hands-on project analyzing an actual large data set by using the different classification algorithms. Participants will collaborate and network with cross-disciplinary colleagues from their institution and others from across the U.S. Finally, each team from the same institution will use what they learn to propose, plan, and implement a joint project at their institution. The program will promote and highlight the interdisciplinary nature of data science and its ability to foster collaboration spanning mathematics, statistics, technology, engineering, economics, and the natural, physical, and computational sciences. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.