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Learnings from developing an applied data science curricula for undergraduate and graduate students

Published online by Cambridge University Press:  24 February 2020

Roger H. French and
Laura S. Bruckman
Roger H. French*
Affiliation:
SDLE Research Center, Case Western Reserve University, Cleveland OH, 44106Dept. of Materials Science & Engineering, Case Western Reserve University, Cleveland OH, 44106Dept. of Macromolecular Science & Engineering Case Western Reserve University, Cleveland OH, 44106Dept. of Computer & Data Sciences, Case Western Reserve University, Cleveland OH 44106
Laura S. Bruckman
Affiliation:
SDLE Research Center, Case Western Reserve University, Cleveland OH, 44106Dept. of Materials Science & Engineering, Case Western Reserve University, Cleveland OH, 44106
*
*(Email: roger@vuvllc.com)
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Abstract

Data science has advanced significantly in recent years and allows scientists to harness large-scale data analysis techniques using open source coding frameworks. Data science is a tool that should be taught to science and engineering students in addition to their chosen domain knowledge. An applied data science minor allows students to understand data and data handling as well as statistics and model development. This move will improve reproducibility and openness of research as well as allow for greater interdisciplinarity and more analyses focusing on critical scientific challenges.

Keywords

educationmodelingsimulation

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Type
Articles
Information
MRS Advances , Volume 5 , Issue 7: BROADER IMPACT , 2020, pp. 347 - 353
Copyright
Copyright © Materials Research Society 2020

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References

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