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In this podcast episode, Rebecca Nugent, the Associate Head and Co-Director of Undergraduate Studies for the Carnegie Mellon Statistics & Data Science Department, discusses the importance of making Data Science education accessible. She speaks about her work at CMU and how she is studying how to teach Data Science and build entry points into the field for people of all backgrounds and ages in a bid to make Data Science more inclusive. She also encourages open-mindedness in teaching Data Science, highlighting that the education does not need to start with programming and people can benefit from learning about the conceptual ideas first as well. Furthermore, she emphasizes the importance of data literacy as well as the risk of inequity in society that limited access to data poses. Finally, she discusses how scaling poses a challenge in the ever-growing field of Data Science and how educators need more investment and resources for pedagogical innovations.

Rebecca Nugent is the Stephen E. and Joyce Fienberg Professor of Statistics & Data Science, the Department Head and Co-Director of Undergraduate Studies for the Carnegie Mellon Statistics & Data Science Department, and an affiliated faculty member of the Block Center for Technology and Society. She received her PhD in Statistics from the University of Washington in 2006.  Prior to that, she received her B.A. in Mathematics, Statistics, and Spanish from Rice University and her M.S. in Statistics from Stanford University.  She was won several national and university teaching awards including the American Statistical Association Waller Award for Innovation in Statistics Education and serves as one of the co-editors of the Springer Texts in Statistics.  She recently served on the National Academy of Sciences study on Envisioning the Data Science Discipline:  The Undergraduate Perspective and is the co-chair of the current NAS study Improving Defense Acquisition Workforce Capability in Data Use.  She is the Founding Director of the Statistics & Data Science Corporate Capstone program, an experiential learning initiative that matches groups of faculty and students with data science problems in industry, non-profits, and government organizations.  She has worked extensively in clustering and classification methodology with an emphasis on high-dimensional, big data problems and record linkage applications.  Her current research focus is the development and deployment of low-barrier data analysis platforms that allow for adaptive instruction and the study of data science as a science.

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