The experience of teaching introductory programming skills to bioscientists in Brazil
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...
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| Published in | PLoS computational biology Vol. 17; no. 11; p. e1009534 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
San Francisco
Public Library of Science
11.11.2021
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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.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. 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. 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. 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. 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. |
| Audience | Academic |
| Author | Winck, Flavia Vischi Martins, Leonardo Frajacomo, Henrique Cordeiro Cavalheiro, Mariana Feitosa Basso Garcia, Ana Letycia Bizarria, Rodolfo Medeiros, Simone Daniela Sartorio de Cerri, Ricardo Correr, Fernando Henrique Tavares, Thayana Vieira Casagrande, Giovanna Maria Stanfoca Esteves, Franciele Grego da Silva, Raniere Gaia Costa Zuvanov, Luíza Ramos, Rommel Thiago Juca Corrêa dos Santos, Renato Augusto Riaño-Pachón, Diego Mauricio Grachet, Nathalia Graf Pinheiro, Ana Lucia Mendes Margarido, Gabriel Rodrigues Alves da Costa, Alisson Hayasi Thomaz, Andréa T. Filho, Ailton Pereira da Costa |
| AuthorAffiliation | 12 Department of Genetics, Evolution, Microbiology and Immunology, Institute of Biology, University of Campinas, Campinas, Brazil 9 Regulatory Systems Biology Lab, Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Brazil 15 Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China 18 Department of Genetics and Evolution, Federal University of São Carlos, São Carlos, Brazil 10 Barretos Cancer Hospital, Barretos, Brazil 6 Department of Computer Science, Federal University of São Carlos, São Carlos, Brazil 11 Paulista School of Medicine, Federal University of São Paulo, São Paulo, Brazil 14 Roche Sequencing Solutions, Pleasanton, California, United States of America 16 Institute of Biological Sciences, Federal University of Pará, Belém, Brazil 7 School of Natural Sciences, Universidad del Rosario, Bogotá, Colombia 4 |
| AuthorAffiliation_xml | – name: 10 Barretos Cancer Hospital, Barretos, Brazil – name: 2 Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil – name: 5 Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil – name: 7 School of Natural Sciences, Universidad del Rosario, Bogotá, Colombia – name: SIB Swiss Institute of Bioinformatics, SWITZERLAND – name: 18 Department of Genetics and Evolution, Federal University of São Carlos, São Carlos, Brazil – name: 20 Institute of Biology, State University of Campinas, Campinas, Brazil – name: 1 São Carlos Institute of Physics, University of São Paulo, São Carlos, Brazil – name: 15 Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China – name: 8 Computational, Evolutionary and Systems Biology Lab, Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Brazil – name: 17 Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Brazil – name: 11 Paulista School of Medicine, Federal University of São Paulo, São Paulo, Brazil – name: 6 Department of Computer Science, Federal University of São Carlos, São Carlos, Brazil – name: 16 Institute of Biological Sciences, Federal University of Pará, Belém, Brazil – name: 3 Department of General and Applied Biology, São Paulo State University, Rio Claro, Brazil – name: 9 Regulatory Systems Biology Lab, Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Brazil – name: 14 Roche Sequencing Solutions, Pleasanton, California, United States of America – name: 12 Department of Genetics, Evolution, Microbiology and Immunology, Institute of Biology, University of Campinas, Campinas, Brazil – name: 13 Genomics for Climate Change Research Center, University of Campinas, Campinas, Brazil – name: 4 Center of the Study of Social Insects, Department of General and Applied Biology, Institute of Biosciences of Rio Claro, São Paulo State University, Rio Claro, Brazil – name: 19 School of Pharmaceutical Sciences of Ribeirao Preto, University of São Paulo, Ribeirão Preto, Brazil |
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| CitedBy_id | crossref_primary_10_1093_biomethods_bpad008 crossref_primary_10_1109_ACCESS_2022_3211973 crossref_primary_10_1016_j_jocs_2022_101642 |
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| Copyright | COPYRIGHT 2021 Public Library of Science 2021 Zuvanov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 Zuvanov et al 2021 Zuvanov et al |
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| DOI | 10.1371/journal.pcbi.1009534 |
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| PublicationDate_xml | – month: 11 year: 2021 text: 20211111 day: 11 |
| PublicationDecade | 2020 |
| PublicationPlace | San Francisco |
| PublicationPlace_xml | – name: San Francisco – name: San Francisco, CA USA |
| PublicationTitle | PLoS computational biology |
| PublicationYear | 2021 |
| Publisher | Public Library of Science Public Library of Science (PLoS) |
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| Title | The experience of teaching introductory programming skills to bioscientists in Brazil |
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