Interdisciplinary Computing Education: An Introductory Programming and Data Science Course for Postdoctoral Researchers in the Biosciences

Abstract

This Innovative-Practice Full Paper presents the curriculum development of an introductory course in programming and data science for postdoctoral researchers (PDRs) in the biosciences. The use of computing software has become ubiquitous and a working knowledge of data science has become increasingly essential for researchers in all domains. However, curriculum development focusing on imparting foundational programming skills and fundamentals of data science for researchers in domains other than computing has been scarce. Thus, there is an unmet need for curriculum development involving computational thinking, programming, and the fundamentals of data science for this audience. Recognizing these growing needs and demands of researchers to learn programming and data science that can then be applied to their area of research or practice, we developed an introductory course in programming and data science for PDRs in biology and medicine. The primary goal of the course was to develop computational thinking skills in PDRs who hail from backgrounds that have traditionally not focused on inculcating computational thinking. This course covered the fundamental concepts of programming using either Python or R - languages that researchers outside the computing community use in numerous ways including the statistical analysis of large datasets that are becoming increasingly common in biomedical research. Further, PDRs enrolled in the course were introduced to some of the broad categories of problems in data science - exploratory data analysis, classification, regression, and clustering - along with relevant algorithms and how they can be applied to real-world datasets in their respective domains using packages or libraries in Python or R. We also report the feedback from the enrolled PDRs, lessons learned, and recommendations for instructors interested in designing similar curricula. Our course focusing on computing and data science education for postdoctoral scholars from a non-computing background demonstrates a promising model for incorporating computing education in other areas of study that do not traditionally have a focus on computing education as well as in continuing education.