With the exponential growth of data around the world, how can we best prepare undergraduate students to use data science skills to tackle critical issues in the life sciences?

 

This is one of the main questions asked by MSU Academic Specialist Nathan Emery and collaborators in a recent publication in BioScience. In their paper "Data Science in Undergraduate Life Science Education: A Need for Instructor Skills Training," they surveyed college/university educators from around the globe on teaching practices related to data science and how scientists use data science in their own research. Their work offers a window into how data science is currently taught and how to best empower instructors to incorporate data science into future biology and environmental science courses.

“The survey asked biology instructors to consider six categories of data science skills that they might teach, use in their own research or view as important for biology or environmental science undergraduates” said Emery, who is in the Department of Plant Biology in the College of Natural Science. “Across institution types, undergraduate Biology instructors viewed data management, analysis and visualization as the most important data science skills to use in research and teach in courses.”

In contrast, the instructors placed less time and value on coding, modeling or reproducibility; however, emphasis on these skills differed by career stage of the instructor. For some data science skills, the findings potentially represent an ‘aspirational gap’ where instructors place a high value or importance on certain skills, but are not teaching those skills to their students. For example, some skills had high importance and were frequently taught, such as data analysis. But other skills such as data visualization, management and reproducibility were valued by instructors, but a relatively low percentage of respondents teach those skills.

For the full story, visit the College of Natural Science website.