An integrated, modular approach to data science education in microbiology.
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
02 2021
02 2021
Historique:
entrez:
25
2
2021
pubmed:
26
2
2021
medline:
7
7
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 33630850
doi: 10.1371/journal.pcbi.1008661
pii: PCOMPBIOL-D-20-01422
pmc: PMC7906378
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1008661Déclaration de conflit d'intérêts
SJH is a co-founder of Koonkie Inc., a bioinformatics consulting company that designs and provides scalable algorithmic and data analytics solutions in the cloud.
Références
PLoS Biol. 2015 Jul 07;13(7):e1002195
pubmed: 26151137
Sci Data. 2017 Oct 31;4:170160
pubmed: 29087368
Brief Bioinform. 2019 Mar 22;20(2):398-404
pubmed: 28968751
Ecol Evol. 2018 Jul 30;8(16):7744-7751
pubmed: 30250659
Curr Opin Microbiol. 2016 Jun;31:209-216
pubmed: 27183115
Nature. 1999 Sep 2;401(6748):10
pubmed: 10485694
Sci Data. 2017 Oct 31;4:170159
pubmed: 29087371
Proc Natl Acad Sci U S A. 2017 Sep 12;114(37):9854-9858
pubmed: 28847929
Proc Natl Acad Sci U S A. 2014 Jun 10;111(23):8410-5
pubmed: 24821756
Ann N Y Acad Sci. 2017 Jan;1387(1):54-60
pubmed: 27603332