DATA SCIENCE STUDIES

a focus on storytelling

Authors

DOI:

https://doi.org/10.31510/infa.v20i2.1802

Keywords:

Data Science, Storytellign, Data Processing, Description of Experiences

Abstract

This work addresses the area of ​​Data Science through descriptive and exploratory analysis, and through interviews with a specialist in the area with the objective of presenting successful and unsuccessful cases to help companies understand and use the principles of Data Science of Data. The reporting of these cases is known as Storytelling, which according to the literature involves experiences told in order to be analyzed, revealing patterns, trends and challenges, as well as involving a practice that enables insights and knowledge for decision making. It is expected to show benefits, challenges and impacts that Data Science can cause in companies through case reports in order to better understand and choose the implementation of such a concept.

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References

AMARAL, F. Introdução a ciência de dados: mineração de dados e Big Data. Rio de Janeiro: Alta Books, 2016.

ANKERKAR, R. Artificial Intelligence for Business. Cham - Suiça: Springer, 2019.

BELL, G. Foreword. The fourth paradigm: data-intensive scientific discovery. In: HEY, T.; TANSLEY, S.; TOLLE, K. M. Redmond, WA: Microsoft Research, p. 11-14, 2009.

CIELEN et al. Introducing Data Science. New York – Estados Unidos: Manning, 2016.

DAVENPORT, T. H.; PATIL, D. J. Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, v. 90, n. 10, p. 70-128, 2012. Disponível em: https://www.researchgate.net/publication/232279315_Data_Scientist_The_Sexiest_Job_of_the_21st_Century. Acesso em: 30 ago. 2023.

DEAN, J. Big data, data mining, and machine learning: value creation for business leaders and practitioners. New Jersey – Estados Unidos: Wiley, 2014. DOI: https://doi.org/10.1002/9781118691786

FOG et al. Storytelling: Branding in Pratice. Berlim: Springer, 2010. DOI: https://doi.org/10.1007/978-3-540-88349-4

LEITE, F.; POSSA, A. Metodologia da Pesquisa Científica. 2. ed. Florianópolis: IFSC, 2013.

NETTO, A; MACIEL, F. Python para Data Science. Rio de Janeiro: Alta Books, 2021.

PYTON, Python. About. Python.org, 2023. Disponível em: https://www.python.org/about/. Acesso em: 30 ago. 2023.

SAMPIERI, R.; COLLADO, C.; LUCIO, M. Metodologia de Pesquisa. Porto Alegre: Penso, 2013.

SEVERINO, A. Metodologia da Pesquisa Científica. 23. ed. São Paulo: Cortez, 2007.

SHARDA et al. Business Intelligence e Análise de Dados para gestão do negócio. Porto Alegre: Bookman, 2019.

SILVA et al. Introdução à mineração de dados: Com aplicações em R. Rio de Janeiro: Elsevier, 2016.

SOUZA, A. S.; OLIVEIRA, G. S.; ALVES, L. H. A pesquisa bibliográfica: princípios e fundamentos. Monte Carmelo: FUCAMP, v. 20, n. 43, p. 64-66, 2021.

RASCHKA, S. Python Machine Learning. Birmingham – Reino Unido: Packt, 2015.

VANDERPLAS, J. Python Data Science Handbook. 1. ed. Sebastopol – Estados Unidos: Editora O’Reilly Media, 2016.

Published

2023-12-20

How to Cite

ARAÚJO, L. S.; DA CUNHA RAMOS, L. .; CHIMELLO MARINO, R. de C. DATA SCIENCE STUDIES: a focus on storytelling. Revista Interface Tecnológica, [S. l.], v. 20, n. 2, p. 195–207, 2023. DOI: 10.31510/infa.v20i2.1802. Disponível em: https://revista.fatectq.edu.br/interfacetecnologica/article/view/1802. Acesso em: 21 dec. 2024.

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Section

Tecnologia em Informática

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