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Undergraduate Data Science Pathways: What is Needed for Entry and Success?


With Nicholas Horton, Roxy Peck, & Rebecca Hartzler (Amherst College)


Abstract

The need for Data Scientists at all degree levels is growing rapidly, with a rapid expansion of programs at two- and four-year colleges and universities. The interdisciplinary nature of Data Science and need for a non-traditional complex and integrated set of mathematical, statistical, and computational skills has made for great variation in curricular designs across institutions and a lack of coherence between gateway courses and courses needed for degree programs.

Earlier this year the Charles A. Dana Center launched an initiative to develop a set of recommendations for entry-level courses in mathematics and statistics that provide a foundation to help students develop data acumen skills that are integral to undergraduate Data Science. This interactive breakout session will begin with a scan of successful programs and the findings from several national initiatives, including the May 2018 Two Year College Data Science Summit hosted by the ASA. The majority of the session will engage the participants through a series of polls interspersed with discussion with the goal of identifying key learning outcomes in mathematics and statistics needed for data science.


Recording