Abstract
The ability to effectively match an employee’s skills and personality to a specific job offer is a great advantage for job seekers. Currently, the extraction from a textual dataset through a job advertisement has become an effective way of discovering knowledge from a given collection of vacancies. Thus, this paper attempted to determine the skills required by employers from fresh graduates with bachelor’s degree in Statistics. The source of data is from an online job advertisement platform extracted from September 2019 to February 2020. Text Mining analysis and Pearson Chi-square Test were employed for the analysis. The results show that the essential hard skills for graduates are the ability to work with data and equipped with computer skills. Meanwhile, communication skills, cross-functional and the ability of being a multi-tasker are the main soft skills required by employers. In addition, the results showed that soft skills are sought differently in product-based industry and service-based industry. The findings show some understandings regarding skills that are essential for statistics graduates.
Metadata
Item Type: | Article |
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Creators: | Creators Email / ID Num. Mohd Yusop, Noorezatty noorezatty@uitm.edu.my Mohd Falil, Amirah Afifah UNSPECIFIED Noorazhar, Nur Aisyah UNSPECIFIED Meli, Siti Nor Syafiqah UNSPECIFIED |
Subjects: | H Social Sciences > HD Industries. Land use. Labor > Labor. Work. Working class > College graduates H Social Sciences > HD Industries. Land use. Labor > Labor. Work. Working class > College graduates > Malaysia H Social Sciences > HF Commerce > Personnel management. Employment management > Communication |
Divisions: | Universiti Teknologi MARA, Pahang > Jengka Campus |
Journal or Publication Title: | Gading Journal of Science and Technology |
UiTM Journal Collections: | UiTM Journal > Gading Journal of Science and Technology (GADINGS&T) |
ISSN: | e-ISSN: 2637 - 0018 |
Volume: | 3 |
Number: | 2 |
Page Range: | pp. 197-202 |
Keywords: | Skills analysis, Employability skills, Text mining |
Date: | September 2020 |
URI: | https://ir.uitm.edu.my/id/eprint/46093 |