How COVID-19 Impacted Data Science: a Topic Retrieval and Analysis from GitHub Projects’ Descriptions

  • Amanda C. R. Tavares Universidade Federal de Minas Gerais (UFMG)
  • Natércia A. Batista Universidade Federal de Minas Gerais (UFMG)
  • Mirella M. Moro Universidade Federal de Minas Gerais (UFMG) http://orcid.org/0000-0002-0545-2001

Resumo


We present a data-driven research over code repositories that are data science oriented. The goal is to compare their topics of interest and evolution over the COVID-19 pandemic period by analyzing Jupyter Notebook and Python projects from a year before and during the pandemic. We employ a state-of-art algorithm to find topics based on the repositories descriptions, and compare the performance of tuning its hyperparameters for better accuracy.

Palavras-chave: Data Science, GitHub, Python, Jupyter Notebooks, COVID-19

Referências

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Tavares, A. C. R., Batista, N. A., and Moro., M. M. (2021). Greed: Github repositories and descriptions. Zenodo. DOI 10.5281/zenodo.5138079

Wang, L. et al. (2020). When the open source community meets covid-19: Characterizing covid-19 themed github repositories. ArXiv, 2010.12218.
Publicado
04/10/2021
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TAVARES, Amanda C. R.; BATISTA, Natércia A.; MORO, Mirella M.. How COVID-19 Impacted Data Science: a Topic Retrieval and Analysis from GitHub Projects’ Descriptions. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 36. , 2021, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 325-330. ISSN 2763-8979. DOI: https://doi.org/10.5753/sbbd.2021.17893.