What about data science? An analysis of the market based on Job posts
Resumo
A importância crescente da ciência de dados no cenário empresarial é indiscutível. Consequentemente, também se observa um crescimento na quantidade de anúncios de vagas relacionadas a essas profissões em ascensão. A análise automática desses dados pode trazer benefícios tanto para profissionais que desejam ingressar nessa área ou buscar oportunidades no mercado de trabalho, quanto para universidades e empresas. Neste contexto, o objetivo deste artigo é coletar e analisar trabalhos relacionados à ciência de dados provenientes de diferentes fontes, utilizando técnicas de mineração de texto. Para tal, foram analisados mais de 6.000 anúncios de vagas nas principais plataformas da área, como Indeed, NewScientist, Efinancialcareers e Pharmiweb. Os resultados obtidos fornecem insights valiosos sobre as principais tecnologias, habilidades comportamentais, sites de anúncios e requisitos gerais. As descobertas deste estudo têm o potencial de direcionar possíveis atualizações para o desenvolvimento de competências técnicas e interpessoais de acordo com as tendências do mercado de trabalho, auxiliando pessoas em busca de recolocação profissional e permitindo a construção de currículos mais alinhados com as demandas do mercado.
Palavras-chave:
Ciência de Dados, Análise Automática, Mercado de Trabalho
Referências
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Alibasic, A., Upadhyay, H., Simsekler, M. C. E., Kurfess, T., Woon, W. L., and Omar, M. A. (2022). Evaluation of the trends in jobs and skill-sets using data analytics: a case study. Journal of Big Data, 9(1):32.
Ao, Z., Horvath, G., Sheng, C., Song, Y., and Sun, Y. (2022). Skill requirements in job advertisements: A comparison of skill-categorization methods based on explanatory power in wage regressions. arXiv preprint arXiv:2207.12834.
Baird, A., Xia, Y., and Cheng, Y. (2022). Consumer perceptions of telehealth for mental health or substance abuse: a twitter-based topic modeling analysis. JAMIA open.
Brady, H. E. (2019). The challenge of big data and data science. Annual Review of Political Science, 22:297–323.
Cao, L. (2017). Data science: a comprehensive overview. ACM Computing Surveys (CSUR), 50(3):1–42.
Charmaz, K. and Thornberg, R. (2021). The pursuit of quality in grounded theory. Qualitative research in psychology, 18(3):305–327.
Cirqueira, D., Pinheiro, M. F., Jacob, A., Lobato, F., and Santana, Á. (2018). A literature review in preprocessing for sentiment analysis for brazilian portuguese social media. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI).
Costa, G. d. S., Couto, D. C., Junior, A. F. J., and Lobato, F. M. (2022). Feminismo e redes sociais online: uma análise de tweets sobre o dia internacional da mulher. In Anais do XI Brazilian Workshop on Social Network Analysis and Mining, pages 169–180. SBC.
de Almeida, G. R., Cirqueira, D. R., and Lobato, F. M. (2017). Improving social crm through eletronic word-of-mouth: a case study of reclameaqui. In Anais Estendidos do XXIII Simpósio Brasileiro de Sistemas Multimídia e Web, pages 107–110. SBC.
Debao, D., Yinxia, M., and Min, Z. (2021). Analysis of big data job requirements based on k-means text clustering in china. PloS one, 16(8):e0255419.
Di Battista, A., Grayling, S., and Hasselaar, E. (2023). Future of jobs report 2023. Technical report, World Economic Forum, Geneva, Switzerland.
Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794.
Grossi, V., Giannotti, F., Pedreschi, D., Manghi, P., Pagano, P., and Assante, M. (2021). Data science: a game changer for science and innovation. International Journal of Data Science and Analytics, 11:263–278.
Gurcan, F. (2019). Extraction of core competencies for big data: Implications for competency-based engineering education. International Journal of Engineering Education, 35(4):1110–1115.
Gurcan, F. and Cagiltay, N. E. (2019). Big data software engineering: Analysis of knowledge domains and skill sets using lda-based topic modeling. IEEE access.
Jagannathan, S., Ra, S., and Maclean, R. (2019). Dominant recent trends impacting on jobs and labor markets-an overview. International Journal of Training Research.
Khaouja, I., Kassou, I., and Ghogho, M. (2021). A survey on skill identification from online job ads. IEEE Access, 9:118134–118153.
Lobato, F., Poça, M., and Canto, V. (2023). Análise, otimização e acompanhamento de um serviço de psicologia universitário: uma abordagem baseada em ciência de dados. Revista da CGU, 15(27).
Paletta, F. C. and Moreiro González, J. A. (2021). A transformação digital e os impactos no mercado de trabalho: estudo dos anúncios de emprego na web para profissionais da informação no setor privado. Information research, 26(3).
Papoutsoglou, M., Ampatzoglou, A., Mittas, N., and Angelis, L. (2019). Extracting knowledge from on-line sources for software engineering labor market: A mapping study. IEEE Access, 7:157595–157613.
Reis, L. C. R., da Fonseca, M. I., et al. (2020). Big data: Um novo campo de atuação para bibliotecários. Prisma. Com, 2020(41):231–250.
Scully-Russ, E. and Torraco, R. (2020). The changing nature and organization of work: An integrative review of the literature. Human Resource Development Review, 19(1):66–93.
Silva, L. E., Schneider, E. T. R., Gumiel, Y. B., da Luz, M. A. P., Paraiso, E. C., Moro, C., et al. (2021). Experiments on portuguese clinical question answering. In Anais da X Brazilian Conference on Intelligent Systems. SBC.
Ternikov, A. (2022). Soft and hard skills identification: insights from it job advertisements in the cis region. PeerJ Computer Science, 8.
Wirth, R. and Hipp, J. (2000). Crisp-dm: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, volume 1, pages 29–39. Manchester.
Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., and He, L. (2022). A survey of human-inthe-loop for machine learning. Future Generation Computer Systems, 135:364–381.
Alibasic, A., Upadhyay, H., Simsekler, M. C. E., Kurfess, T., Woon, W. L., and Omar, M. A. (2022). Evaluation of the trends in jobs and skill-sets using data analytics: a case study. Journal of Big Data, 9(1):32.
Ao, Z., Horvath, G., Sheng, C., Song, Y., and Sun, Y. (2022). Skill requirements in job advertisements: A comparison of skill-categorization methods based on explanatory power in wage regressions. arXiv preprint arXiv:2207.12834.
Baird, A., Xia, Y., and Cheng, Y. (2022). Consumer perceptions of telehealth for mental health or substance abuse: a twitter-based topic modeling analysis. JAMIA open.
Brady, H. E. (2019). The challenge of big data and data science. Annual Review of Political Science, 22:297–323.
Cao, L. (2017). Data science: a comprehensive overview. ACM Computing Surveys (CSUR), 50(3):1–42.
Charmaz, K. and Thornberg, R. (2021). The pursuit of quality in grounded theory. Qualitative research in psychology, 18(3):305–327.
Cirqueira, D., Pinheiro, M. F., Jacob, A., Lobato, F., and Santana, Á. (2018). A literature review in preprocessing for sentiment analysis for brazilian portuguese social media. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI).
Costa, G. d. S., Couto, D. C., Junior, A. F. J., and Lobato, F. M. (2022). Feminismo e redes sociais online: uma análise de tweets sobre o dia internacional da mulher. In Anais do XI Brazilian Workshop on Social Network Analysis and Mining, pages 169–180. SBC.
de Almeida, G. R., Cirqueira, D. R., and Lobato, F. M. (2017). Improving social crm through eletronic word-of-mouth: a case study of reclameaqui. In Anais Estendidos do XXIII Simpósio Brasileiro de Sistemas Multimídia e Web, pages 107–110. SBC.
Debao, D., Yinxia, M., and Min, Z. (2021). Analysis of big data job requirements based on k-means text clustering in china. PloS one, 16(8):e0255419.
Di Battista, A., Grayling, S., and Hasselaar, E. (2023). Future of jobs report 2023. Technical report, World Economic Forum, Geneva, Switzerland.
Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794.
Grossi, V., Giannotti, F., Pedreschi, D., Manghi, P., Pagano, P., and Assante, M. (2021). Data science: a game changer for science and innovation. International Journal of Data Science and Analytics, 11:263–278.
Gurcan, F. (2019). Extraction of core competencies for big data: Implications for competency-based engineering education. International Journal of Engineering Education, 35(4):1110–1115.
Gurcan, F. and Cagiltay, N. E. (2019). Big data software engineering: Analysis of knowledge domains and skill sets using lda-based topic modeling. IEEE access.
Jagannathan, S., Ra, S., and Maclean, R. (2019). Dominant recent trends impacting on jobs and labor markets-an overview. International Journal of Training Research.
Khaouja, I., Kassou, I., and Ghogho, M. (2021). A survey on skill identification from online job ads. IEEE Access, 9:118134–118153.
Lobato, F., Poça, M., and Canto, V. (2023). Análise, otimização e acompanhamento de um serviço de psicologia universitário: uma abordagem baseada em ciência de dados. Revista da CGU, 15(27).
Paletta, F. C. and Moreiro González, J. A. (2021). A transformação digital e os impactos no mercado de trabalho: estudo dos anúncios de emprego na web para profissionais da informação no setor privado. Information research, 26(3).
Papoutsoglou, M., Ampatzoglou, A., Mittas, N., and Angelis, L. (2019). Extracting knowledge from on-line sources for software engineering labor market: A mapping study. IEEE Access, 7:157595–157613.
Reis, L. C. R., da Fonseca, M. I., et al. (2020). Big data: Um novo campo de atuação para bibliotecários. Prisma. Com, 2020(41):231–250.
Scully-Russ, E. and Torraco, R. (2020). The changing nature and organization of work: An integrative review of the literature. Human Resource Development Review, 19(1):66–93.
Silva, L. E., Schneider, E. T. R., Gumiel, Y. B., da Luz, M. A. P., Paraiso, E. C., Moro, C., et al. (2021). Experiments on portuguese clinical question answering. In Anais da X Brazilian Conference on Intelligent Systems. SBC.
Ternikov, A. (2022). Soft and hard skills identification: insights from it job advertisements in the cis region. PeerJ Computer Science, 8.
Wirth, R. and Hipp, J. (2000). Crisp-dm: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, volume 1, pages 29–39. Manchester.
Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., and He, L. (2022). A survey of human-inthe-loop for machine learning. Future Generation Computer Systems, 135:364–381.
Publicado
25/09/2023
Como Citar
BARATA, Barbara A. P.; JUSTINO, Adrielson F.; JACOB JUNIOR, Antonio F. L.; LOBATO, Fábio M. F..
What about data science? An analysis of the market based on Job posts. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 824-838.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2023.234486.
