Resumen de Data Science for Biochemists: Integrating and Evaluating the Use of Interactive Digital Python Notebooks in a Large-Enrollment Undergraduate Biochemistry Course
Rebecca Brunk, Kriti Shukla, Bryant L. Hutson, Yue Wang, Matthew Verber, Christina Ford, William Dennis, Aarav Mehta, Brian P. Hogan, Tyson Swetnam, Elizabeth Brunk
Genomic sequencing and other big biological data are unquestionably of paramount value; however, the success in recruiting highly skilled individuals with diverse backgrounds has been limited. A main reason for this deficiency could be due to the lack of educational resources and early exposure to the field. With the steady increase in big biological data over the past decade, we need not only to increase the number of skilled researchers in the field but also to empower the next generation of students with skills that can apply data analysis skills to a variety of career trajectories. Here, we share a successful example of integrating Python-based interactive digital notebooks in a large-enrollment undergraduate chemistry course with more than 400 participants across various degree programs. The goal of this Article is to detail the teaching pedagogy, supply the teaching materials, and evaluate the outcomes of integrating coding in a large-enrollment undergraduate chemistry course. The guiding research questions of this study are the following: How can we effectively integrate coding and big-data analysis in a large-enrollment class and does this integration change student attitudes towards coding and research? We expect that providing early exposure to data science will help undergraduates gain skills in computational analysis, which will be an asset to any student, regardless of career path or academic trajectory.
