Brazilian Data Scientists: Revealing their Challenges and Practices on Machine Learning Model Development-Reference-Cited by-同舟云学术

Brazilian Data Scientists: Revealing their Challenges and Practices on Machine Learning Model Development

Published:2020-12 Issue: Volume: Page:
ISSN:
Container-title:19th Brazilian Symposium on Software Quality
language:
Short-container-title:

Author:

Correia João Lucas¹,Pereira Juliana Alves²,Mello Rafael³,Garcia Alessandro²,Fonseca Baldoino¹,Ribeiro Márcio¹,Gheyi Rohit⁴,Kalinowski Marcos²,Cerqueira Renato⁵,Tiengo Willy¹

Affiliation:

1. Federal University of Alagoas, Brazil

2. Pontifical Catholic University of Rio de Janeiro, Brazil

3. Federal Center for Technological Education of Rio de Janeiro, Brazil

4. Federal University of Campina Grande, Brazil

5. IBM Research Brazil, Brazil

Funder

117875

373892/2019-00

140185/2020-8

427787/2018-1

434969/2018-4

426005/2018-0

22520-7/2016

421306/2018-1

311442/2019-6

312149/2016-6

88882.452283/2019-01

309844/2018-5

175956

88887.373933/2019-00

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3439961.3439971

Reference21 articles.

1. Modelling of CO2 separation from gas streams emissions in the oil and gas industries;Baghban A.;Petroleum Science and Technology,2016

2. Ciara Byrne. 2017. Development Workflows for Data Scientists. O’Reilly Media. Ciara Byrne. 2017. Development Workflows for Data Scientists. O’Reilly Media.

3. D Cohen and B Crabtree. 2006. Qualitative research guidelines project. D Cohen and B Crabtree. 2006. Qualitative research guidelines project.

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