Dataset Information

An integrated, modular approach to data science education in microbiology.

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.

SUBMITTER: Dill-McFarland KA

PROVIDER: S-EPMC7906378 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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An integrated, modular approach to data science education in microbiology.

Dill-McFarland Kimberly A KA König Stephan G SG Mazel Florent F Oliver David C DC McEwen Lisa M LM Hong Kris Y KY Hallam Steven J SJ

PLoS computational biology 20210225 2

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 scien ...[more]

PMID: 33630850

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Dataset Information

An integrated, modular approach to data science education in microbiology.

Publications

An integrated, modular approach to data science education in microbiology.

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