Literature DB >> 35399224

Introducing R as a smart version of calculators enables beginners to explore it on their own.

Krishna Choudhary1,2, Alexander R Pico1.   

Abstract

Rapid technological advances in the past decades have enabled molecular biologists to generate large-scale and complex data with affordable resource investments, or obtain such data from public repositories. Yet, many graduate students, postdoctoral scholars, and senior researchers in the biosciences find themselves ill-equipped to analyze large-scale data. Global surveys have revealed that active researchers prefer short training workshops to fill their skill gaps. In this article, we focus on the challenge of delivering a short data analysis workshop to absolute beginners in computer programming. We propose that introducing R or other programming languages for data analysis as smart versions of calculators can help lower the communication barrier with absolute beginners. We describe this comparison with a few analogies and hope that other instructors will find them useful. We utilized these in our four-hour long training workshops involving participatory live coding, which we delivered in person and via videoconferencing. Anecdotal evidence suggests that our exposition made R programming seem easy and enabled beginners to explore it on their own. Copyright:
© 2022 Choudhary K and Pico AR.

Entities:  

Keywords:  R; data science training; learn programming; students

Mesh:

Year:  2021        PMID: 35399224      PMCID: PMC8976183.2          DOI: 10.12688/f1000research.54685.2

Source DB:  PubMed          Journal:  F1000Res        ISSN: 2046-1402


  21 in total

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Journal:  Trends Genet       Date:  2017-07-18       Impact factor: 11.639

Review 2.  High-throughput proteomics.

Authors:  Zhaorui Zhang; Si Wu; David L Stenoien; Ljiljana Paša-Tolić
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Journal:  Nat Cell Biol       Date:  2015-03-02       Impact factor: 28.824

4.  Software Carpentry: lessons learned.

Authors:  Greg Wilson
Journal:  F1000Res       Date:  2014-02-19

5.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.

Authors:  Michael I Love; Wolfgang Huber; Simon Anders
Journal:  Genome Biol       Date:  2014       Impact factor: 13.583

6.  Teaching bioinformatics in concert.

Authors:  Anya L Goodman; Alex Dekhtyar
Journal:  PLoS Comput Biol       Date:  2014-11-20       Impact factor: 4.475

7.  From trainees to trainers to instructors: Sustainably building a national capacity in bioinformatics training.

Authors:  Annette McGrath; Katherine Champ; Catherine A Shang; Ellen van Dam; Cath Brooksbank; Sarah L Morgan
Journal:  PLoS Comput Biol       Date:  2019-06-27       Impact factor: 4.475

8.  A global perspective on evolving bioinformatics and data science training needs.

Authors:  Teresa K Attwood; Sarah Blackford; Michelle D Brazas; Angela Davies; Maria Victoria Schneider
Journal:  Brief Bioinform       Date:  2019-03-22       Impact factor: 11.622

9.  Integrated analysis of multimodal single-cell data.

Authors:  Yuhan Hao; Stephanie Hao; Erica Andersen-Nissen; William M Mauck; Shiwei Zheng; Andrew Butler; Maddie J Lee; Aaron J Wilk; Charlotte Darby; Michael Zager; Paul Hoffman; Marlon Stoeckius; Efthymia Papalexi; Eleni P Mimitou; Jaison Jain; Avi Srivastava; Tim Stuart; Lamar M Fleming; Bertrand Yeung; Angela J Rogers; Juliana M McElrath; Catherine A Blish; Raphael Gottardo; Peter Smibert; Rahul Satija
Journal:  Cell       Date:  2021-05-31       Impact factor: 41.582

10.  Ad hoc efforts for advancing data science education.

Authors:  Orianna DeMasi; Alexandra Paxton; Kevin Koy
Journal:  PLoS Comput Biol       Date:  2020-05-07       Impact factor: 4.475

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