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bioRxiv · 10.1101/2021.06.17.448726

Teaching Python for Data Science: Collaborative development of a modular & interactive curriculum

Duda, M.·Sovacool, K. L.·Farzaneh, N.·Nguyen, V. K.·Haynes, S. E.·Falk, H.·Furman, K. L.·Walker, L. A.·Diao, R.·Oneka, M.·Drotos, A. C.·Woloshin, A.·Dotson, G. A.·Kriebel, A.·Meng, L.·Thiede, S. N.·Lapp, Z.·Wolford, B. N.
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Abstract

We are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our educational materials on GitHub.

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Duda, M., Sovacool, K. L., Farzaneh, N., Nguyen, V. K., Haynes, S. E., Falk, H., Furman, K. L., Walker, L. A., Diao, R., Oneka, M., Drotos, A. C., Woloshin, A., Dotson, G. A., Kriebel, A., Meng, L., Thiede, S. N., Lapp, Z., Wolford, B. N.. 2021-06-18. Teaching Python for Data Science: Collaborative development of a modular & interactive curriculum. https://doi.org/10.1101/2021.06.17.448726

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