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Analysis of Skills and Qualifications Required in Data Scientist Job Postings Based on the Pareto Analysis Perspective Using Text Mining

Author

Listed:
  • Erkan Işığıçok

    (Bursa Uludağ University, Faculty of Economics and Administrative Sciences, Department of Econometrics, Bursa, Turkiye)

  • Sadullah Çelik

    (Aydın Adnan Menderes University, Nazilli Faculty of Economics and Administrative Sciences, Department of International Trade and Finance, Aydın, Turkiye)

  • Dilek Özdemir Yılmaz

    (Bursa Uludağ University, Faculty of Economics and Administrative Sciences, Social Sciences Institute, Bursa, Turkiye)

Abstract

Today, there are more job posts than ever before, making it incredibly challenging for job searchers to find the position that best suits them. To overcome this difficulty, text mining methods can be used to extract information such as job titles, required skills, and required experience, and to analyze job postings. This information can also be used to match job seekers with the most relevant job postings. The main purpose of this research is to determine which skills, techniques, subjects, fields, and so on should be prioritized by job seekers. For this purpose, 200 data scientist job postings from Turkey and 200 data scientist job postings from the USA are analyzed. According to the results, employers who have announced their interest in hiring a Data Scientist prefer people who are experts in Machine Learning, Data Science, Python, SQL, R, Statistics, and Mathematics, people with BSc, MSc, and PhD education levels, people with 3+ years of work experience, and people who know Visualization, Data Mining, Prediction, NLP, and Clustering techniques. For this reason, it is recommended that people who want to become data scientists in TR or the USA improve themselves in these techniques, skills, and experiences to be accepted to data scientist position jobs more easily.

Suggested Citation

  • Erkan Işığıçok & Sadullah Çelik & Dilek Özdemir Yılmaz, 2023. "Analysis of Skills and Qualifications Required in Data Scientist Job Postings Based on the Pareto Analysis Perspective Using Text Mining," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(39), pages 10-25, December.
    • Handle: RePEc:ist:ekoist:v:0:y:2023:i:39:p:10-25
    DOI: 10.26650/ekoist.2023.39.1256697
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    References listed on IDEAS

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    1. Costa, Carlos & Santos, Maribel Yasmina, 2017. "The data scientist profile and its representativeness in the European e-Competence framework and the skills framework for the information age," International Journal of Information Management, Elsevier, vol. 37(6), pages 726-734.
    2. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    3. Jonathan Benchimol & Sophia Kazinnik & Yossi Saadon, 2022. "Text mining methodologies with R: An application to central bank texts," Post-Print emse-03953759, HAL.
    4. Alzate, Miriam & Arce-Urriza, Marta & Cebollada, Javier, 2022. "Mining the text of online consumer reviews to analyze brand image and brand positioning," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    5. Gary King & Patrick Lam & Margaret E. Roberts, 2017. "Computer‐Assisted Keyword and Document Set Discovery from Unstructured Text," American Journal of Political Science, John Wiley & Sons, vol. 61(4), pages 971-988, October.
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