Machine Learning Process to Determine the Social Demand for IT Professional Jobs
Descripción del Articulo
Machine learning is a branch of artificial intelligence that uses scientific computing, mathematics and statistics through automated techniques to solve problems based on classification, regression and clustering. Social demand refers to the need for service and product of the professional training...
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Formato: | artículo |
Fecha de Publicación: | 2023 |
Institución: | Universidad Nacional Mayor de San Marcos |
Repositorio: | Revistas - Universidad Nacional Mayor de San Marcos |
Lenguaje: | español inglés |
OAI Identifier: | oai:ojs.csi.unmsm:article/21643 |
Enlace del recurso: | https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643 |
Nivel de acceso: | acceso abierto |
Materia: | machine learning process clustering k-means social demand IT professionals proceso machine learning demanda social profesionales de TI |
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Machine Learning Process to Determine the Social Demand for IT Professional JobsProceso de machine learning para determinar la demanda social de puestos de empleo de profesionales de TIMamani Rodriguez, ZoraidaMamani Rodriguez, Zoraidamachine learning processclusteringk-meanssocial demandIT professionalsproceso machine learningclusteringk-meansdemanda socialprofesionales de TIMachine learning is a branch of artificial intelligence that uses scientific computing, mathematics and statistics through automated techniques to solve problems based on classification, regression and clustering. Social demand refers to the need for service and product of the professional training process, expressed by interest groups, aimed at contributing to national development, as established by the quality assurance policy of university higher education and national licensing and accreditation models. In this context, this paper conducts research based on job positions of IT professionals posted n web portals, designs a machine learning process with an unsupervised approach, extracts occupational profiles, designs a multidimensional model, applies k-means clustering when determining clusters of job positions by similarity, and reports the results obtained.El machine learning es una rama de la inteligencia artificial que utiliza la computación científica, las matemáticas y la estadística a través de técnicas automatizadas para resolver problemas basados en clasificación, regresión y clustering. La demanda social refiere a la necesidad de servicio y producto del proceso de formación profesional, que expresan los grupos de interés, orientada a contribuir al desarrollo nacional, tal como lo establecen la política de aseguramiento de la calidad de la educación superior univeristaria y los modelos de licenciamiento y acreditación nacional. En ese contexto el presente trabajo realiza una investigación a partir de los puestos de empleo de profesionales de TI publicados en los portales web, diseña un proceso de machine learning con enfoque no supervisado, extrae los perfiles ocupacionales, diseña un modelo multidimensional, aplica clustering k-means en la determinación de conglomerados de los puestos de empleo por similitud y expone los resultados obtenidos.Facultad de Ingeniería Industrial, Universidad Nacional Mayor de San Marcos2023-01-24info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdftext/htmltext/htmlaudio/mpegaudio/mpeghttps://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/2164310.15381/idata.v25i2.21643Industrial Data; Vol. 25 No. 2 (2022); 275-300Industrial Data; Vol. 25 Núm. 2 (2022); 275-3001810-99931560-9146reponame:Revistas - Universidad Nacional Mayor de San Marcosinstname:Universidad Nacional Mayor de San Marcosinstacron:UNMSMspaenghttps://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643/19236https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643/19237https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643/19238https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643/19239https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643/19240https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643/1924110.15381/idata.v25i2.21643.g19078Derechos de autor 2022 Zoraida Mamani Rodriguezhttps://creativecommons.org/licenses/by/4.0/deed.es_ESinfo:eu-repo/semantics/openAccessoai:ojs.csi.unmsm:article/216432023-01-24T11:36:31Z |
dc.title.none.fl_str_mv |
Machine Learning Process to Determine the Social Demand for IT Professional Jobs Proceso de machine learning para determinar la demanda social de puestos de empleo de profesionales de TI |
title |
Machine Learning Process to Determine the Social Demand for IT Professional Jobs |
spellingShingle |
Machine Learning Process to Determine the Social Demand for IT Professional Jobs Mamani Rodriguez, Zoraida machine learning process clustering k-means social demand IT professionals proceso machine learning clustering k-means demanda social profesionales de TI |
title_short |
Machine Learning Process to Determine the Social Demand for IT Professional Jobs |
title_full |
Machine Learning Process to Determine the Social Demand for IT Professional Jobs |
title_fullStr |
Machine Learning Process to Determine the Social Demand for IT Professional Jobs |
title_full_unstemmed |
Machine Learning Process to Determine the Social Demand for IT Professional Jobs |
title_sort |
Machine Learning Process to Determine the Social Demand for IT Professional Jobs |
dc.creator.none.fl_str_mv |
Mamani Rodriguez, Zoraida Mamani Rodriguez, Zoraida |
author |
Mamani Rodriguez, Zoraida |
author_facet |
Mamani Rodriguez, Zoraida |
author_role |
author |
dc.subject.none.fl_str_mv |
machine learning process clustering k-means social demand IT professionals proceso machine learning clustering k-means demanda social profesionales de TI |
topic |
machine learning process clustering k-means social demand IT professionals proceso machine learning clustering k-means demanda social profesionales de TI |
description |
Machine learning is a branch of artificial intelligence that uses scientific computing, mathematics and statistics through automated techniques to solve problems based on classification, regression and clustering. Social demand refers to the need for service and product of the professional training process, expressed by interest groups, aimed at contributing to national development, as established by the quality assurance policy of university higher education and national licensing and accreditation models. In this context, this paper conducts research based on job positions of IT professionals posted n web portals, designs a machine learning process with an unsupervised approach, extracts occupational profiles, designs a multidimensional model, applies k-means clustering when determining clusters of job positions by similarity, and reports the results obtained. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-01-24 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643 10.15381/idata.v25i2.21643 |
url |
https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643 |
identifier_str_mv |
10.15381/idata.v25i2.21643 |
dc.language.none.fl_str_mv |
spa eng |
language |
spa eng |
dc.relation.none.fl_str_mv |
https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643/19236 https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643/19237 https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643/19238 https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643/19239 https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643/19240 https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/21643/19241 10.15381/idata.v25i2.21643.g19078 |
dc.rights.none.fl_str_mv |
Derechos de autor 2022 Zoraida Mamani Rodriguez https://creativecommons.org/licenses/by/4.0/deed.es_ES info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Derechos de autor 2022 Zoraida Mamani Rodriguez https://creativecommons.org/licenses/by/4.0/deed.es_ES |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf text/html text/html audio/mpeg audio/mpeg |
dc.publisher.none.fl_str_mv |
Facultad de Ingeniería Industrial, Universidad Nacional Mayor de San Marcos |
publisher.none.fl_str_mv |
Facultad de Ingeniería Industrial, Universidad Nacional Mayor de San Marcos |
dc.source.none.fl_str_mv |
Industrial Data; Vol. 25 No. 2 (2022); 275-300 Industrial Data; Vol. 25 Núm. 2 (2022); 275-300 1810-9993 1560-9146 reponame:Revistas - Universidad Nacional Mayor de San Marcos instname:Universidad Nacional Mayor de San Marcos instacron:UNMSM |
instname_str |
Universidad Nacional Mayor de San Marcos |
instacron_str |
UNMSM |
institution |
UNMSM |
reponame_str |
Revistas - Universidad Nacional Mayor de San Marcos |
collection |
Revistas - Universidad Nacional Mayor de San Marcos |
repository.name.fl_str_mv |
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repository.mail.fl_str_mv |
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1795238304110608384 |
score |
13.871978 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).