Changing the Nature of Quantitative Biology Education: Data Science as a Driver
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Changing the Nature of Quantitative Biology Education: Data Science as a Driver Raina S. Robeva1 · John R. Jungck2 · Louis J. Gross3,4 Received: 24 April 2020 / Accepted: 28 July 2020 © Society for Mathematical Biology 2020
Abstract We live in a data-rich world with rapidly growing databases with zettabytes of data. Innovation, computation, and technological advances have now tremendously accelerated the pace of discovery, providing driverless cars, robotic devices, expert healthcare systems, precision medicine, and automated discovery to mention a few. Even though the definition of the term data science continues to evolve, the sweeping impact it has already produced on society is undeniable. We are at a point when new discoveries through data science have enormous potential to advance progress but also to be used maliciously, with harmful ethical and social consequences. Perhaps nowhere is this more clearly exemplified than in the biological and medical sciences. The confluence of (1) machine learning, (2) mathematical modeling, (3) computation/simulation, and (4) big data have moved us from the sequencing of genomes to gene editing and individualized medicine; yet, unsettled policies regarding data privacy and ethical norms could potentially open doors for serious negative repercussions. The data science revolution has amplified the urgent need for a paradigm shift in undergraduate biology education. It has reaffirmed that data science education interacts and enhances mathematical education in advancing quantitative conceptual and skill development for the new generation of biologists. These connections encourage us to strive to cultivate a broadly skilled workforce of technologically savvy problem-solvers, skilled at handling the unique challenges pertaining to biological data, and capable of collaborating across various disciplines in the sciences, the humanities, and the social sciences. To accomplish this, we suggest development of open curricula that extend beyond the job certification rhetoric and combine data acumen with modeling, experimental, and computational methods through engaging projects, while also providing awareness and deep exploration of their societal implications. This process would benefit from embracing the pedagogy of experiential learning and involve students in open-ended explorations derived from authentic inquiries and ongoing research. On this founda-
The first author was partially supported by the Karl Peace Fellowship in Mathematics, Randolph-Macon College, VA. The third author was supported by NSF Award #DBI-1300426 to the University of Tennessee. Extended author information available on the last page of the article 0123456789().: V,-vol
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tion, we encourage development of flexible data science initiatives for the education of life science undergraduates within and across existing models. Keywords Mathematical biology education · Data science education · Education reform · Big data Mathematics Subject Classification 97m60 · 92B05 · 6
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