Abstract
We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.
Highlights
We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms have generated a veritable tsunami of multi-omic information (e.g., DNA, RNA, protein, and metabolite) spanning multiple levels of biological organization [1,2]
The Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative at University of British Columbia (UBC) was launched in Fall 2017 in response to a clear and present need to expand data science education in the life sciences
Initial implementation of EDUCE learning modules and cocurricular activities is focused on ecology and microbiology but can be readily extended to other data sets and other discipline-specific software
Summary
We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms have generated a veritable tsunami of multi-omic information (e.g., DNA, RNA, protein, and metabolite) spanning multiple levels of biological organization [1,2]. The combined set of learning objectives are achieved through (1) a modular curriculum plugged into existing courses; (2) coordinated cocurricular activities; and (3) a cross-disciplinary community of practice that brings together teaching teams across multiple training levels.
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