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Human Resource Analytics on Data Science Employment Based on Specialized Skill Sets with Salary Prediction.

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Τίτλος: Human Resource Analytics on Data Science Employment Based on Specialized Skill Sets with Salary Prediction.
Συγγραφείς: Tee Zhen Quan, Raheem, Mafas
Πηγή: International Journal of Data Science (IJoDS); Jun2023, Vol. 4 Issue 1, p40-59, 20p
Θεματικοί όροι: Data science, Big data, Career development, Vocational guidance, Wages, Forecasting
Περίληψη: The research aims to perform meaningful human resource analysis on data science employment using the strong influences of specialized skills set with assisting salary prediction. With explosive big data development, a data science job shortage has occurred with high accurate recruitment demand to hire suitable professionals for specific data science roles. To achieve such outcomes, the current data science employment trends were analyzed based on a secondary dataset. Useful analytics insights for job securement and better career development were provided through the main dashboard. Besides, the significant in-demand data science skill variables were also identified for further effective model building. Particularly, certain data pre-processing techniques were performed extensively to prepare and optimize the dataset for the mentioned human resource analytics purposes. The ensemble model was selected as the most suitable salary prediction model with the lowest Average Squared Error (ASE) on validation. Despite the low prediction accuracy caused by numerous filtered skill variables, the salary prediction model's main objective was to interpret the relationships between input variables and the target salary levels variable. Overall, the results from both the human resource analytic dashboard and salary prediction model were tally where a detailed analytic report was provided to encourage different data science roles with specific and effective career development guidance, using salary as the motivation key. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Data Science (IJoDS) is the property of INSIGHT - Indonesian Society for Knowledge & Human Development and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: Human Resource Analytics on Data Science Employment Based on Specialized Skill Sets with Salary Prediction.
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  Data: <searchLink fieldCode="AR" term="%22Tee+Zhen+Quan%22">Tee Zhen Quan</searchLink><br /><searchLink fieldCode="AR" term="%22Raheem%2C+Mafas%22">Raheem, Mafas</searchLink>
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  Data: International Journal of Data Science (IJoDS); Jun2023, Vol. 4 Issue 1, p40-59, 20p
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  Data: <searchLink fieldCode="DE" term="%22Data+science%22">Data science</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Career+development%22">Career development</searchLink><br /><searchLink fieldCode="DE" term="%22Vocational+guidance%22">Vocational guidance</searchLink><br /><searchLink fieldCode="DE" term="%22Wages%22">Wages</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink>
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  Label: Abstract
  Group: Ab
  Data: The research aims to perform meaningful human resource analysis on data science employment using the strong influences of specialized skills set with assisting salary prediction. With explosive big data development, a data science job shortage has occurred with high accurate recruitment demand to hire suitable professionals for specific data science roles. To achieve such outcomes, the current data science employment trends were analyzed based on a secondary dataset. Useful analytics insights for job securement and better career development were provided through the main dashboard. Besides, the significant in-demand data science skill variables were also identified for further effective model building. Particularly, certain data pre-processing techniques were performed extensively to prepare and optimize the dataset for the mentioned human resource analytics purposes. The ensemble model was selected as the most suitable salary prediction model with the lowest Average Squared Error (ASE) on validation. Despite the low prediction accuracy caused by numerous filtered skill variables, the salary prediction model's main objective was to interpret the relationships between input variables and the target salary levels variable. Overall, the results from both the human resource analytic dashboard and salary prediction model were tally where a detailed analytic report was provided to encourage different data science roles with specific and effective career development guidance, using salary as the motivation key. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Data Science (IJoDS) is the property of INSIGHT - Indonesian Society for Knowledge & Human Development and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.18517/ijods.4.1.40-59.2023
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 20
        StartPage: 40
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      – SubjectFull: Data science
        Type: general
      – SubjectFull: Big data
        Type: general
      – SubjectFull: Career development
        Type: general
      – SubjectFull: Vocational guidance
        Type: general
      – SubjectFull: Wages
        Type: general
      – SubjectFull: Forecasting
        Type: general
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              Text: Jun2023
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              Y: 2023
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