PLoS Computational Biology (Nov 2021)

The experience of teaching introductory programming skills to bioscientists in Brazil

  • Luíza Zuvanov,
  • Ana Letycia Basso Garcia,
  • Fernando Henrique Correr,
  • Rodolfo Bizarria,
  • Ailton Pereira da Costa Filho,
  • Alisson Hayasi da Costa,
  • Andréa T. Thomaz,
  • Ana Lucia Mendes Pinheiro,
  • Diego Mauricio Riaño-Pachón,
  • Flavia Vischi Winck,
  • Franciele Grego Esteves,
  • Gabriel Rodrigues Alves Margarido,
  • Giovanna Maria Stanfoca Casagrande,
  • Henrique Cordeiro Frajacomo,
  • Leonardo Martins,
  • Mariana Feitosa Cavalheiro,
  • Nathalia Graf Grachet,
  • Raniere Gaia Costa da Silva,
  • Ricardo Cerri,
  • Rommel Thiago Juca Ramos,
  • Simone Daniela Sartorio de Medeiros,
  • Thayana Vieira Tavares,
  • Renato Augusto Corrêa dos Santos

Journal volume & issue
Vol. 17, no. 11

Abstract

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Computational biology has gained traction as an independent scientific discipline over the last years in South America. However, there is still a growing need for bioscientists, from different backgrounds, with different levels, to acquire programming skills, which could reduce the time from data to insights and bridge communication between life scientists and computer scientists. Python is a programming language extensively used in bioinformatics and data science, which is particularly suitable for beginners. Here, we describe the conception, organization, and implementation of the Brazilian Python Workshop for Biological Data. This workshop has been organized by graduate and undergraduate students and supported, mostly in administrative matters, by experienced faculty members since 2017. The workshop was conceived for teaching bioscientists, mainly students in Brazil, on how to program in a biological context. The goal of this article was to share our experience with the 2020 edition of the workshop in its virtual format due to the Coronavirus Disease 2019 (COVID-19) pandemic and to compare and contrast this year’s experience with the previous in-person editions. We described a hands-on and live coding workshop model for teaching introductory Python programming. We also highlighted the adaptations made from in-person to online format in 2020, the participants’ assessment of learning progression, and general workshop management. Lastly, we provided a summary and reflections from our personal experiences from the workshops of the last 4 years. Our takeaways included the benefits of the learning from learners’ feedback (LLF) that allowed us to improve the workshop in real time, in the short, and likely in the long term. We concluded that the Brazilian Python Workshop for Biological Data is a highly effective workshop model for teaching a programming language that allows bioscientists to go beyond an initial exploration of programming skills for data analysis in the medium to long term. Author summary Bioscientists analyzing research data deal with challenges because most lack computer science background, such as programming skills, making it difficult to process their data and communicate with data analysts. Over the last few years (2017 to 2020), we assembled interdisciplinary teams of graduate and undergraduate students to develop the Brazilian Python Workshop for Biological Data. These short courses aimed to teach programming skills in a real-world setting. They were offered in Portuguese to facilitate accessibility to both Brazilians and foreigners doing research in Brazil. We accomplished these goals by emphasizing both basic programming skills and foundational concepts, alongside hands-on activities designed with biological datasets. Importantly, we were supported by experienced faculty. Although the first editions were in-person, we reformulated the 2020 edition to an online version due to the Coronavirus Disease 2019 (COVID-19) pandemic. During the online 2020 edition, we taught using a variety of tools to facilitate synchronous and asynchronous communication between participants and organization and to engage participants in activities that promoted their active participation and networking. We used digital notebooks and encouraged students to put into practice shareable and reproducible research. In 2020, we also performed online surveys with participants that helped us to implement real-time improvements and perspectives of future changes based on the students’ feedback. Our workshop comprises a model for future initiatives.