IDEAS home Printed from https://ideas.repec.org/a/lum/rev14e/v4y2019i1p21-32.html
   My bibliography  Save this article

Employers’ Requirements for Data Scientists - an Analysis of Job Posts

Author

Listed:
  • Monica Mihaela Maer Matei

    (National Scientific Research Institute for Labour and Social Protection)

  • Anamaria Beatrice Aldea

    (Researcher, National Scientific Research Institute for Labour and Social Protection, Bucharest)

Abstract

Technological development and innovation are the main drivers of jobs transformations leading to skill mismatch. One very dynamic domain, dealing with these issues is data science. Generally, a data scientist has to work with big data in a scientific and creative manner. To reduce the drawbacks of a sparse matching between educational offer and the new requirements of the labour market is essential to understand real time job market requirements. The most relevant data source for such an investigation is represented by online job market portals. Nowadays, with the increasing digitalisation of society, these portals are considered to improve transparency and signalling in labour markets. Moreover, the potential of the textual vacancy data from Romanian online recruiting platforms has not been exploited up to now. Following these arguments, in order to understand employers’ requirements for data science jobs in Romania, we develop an analysis of textual data extracted from job advertisements dedicated to data scientists. Mainly the data analysis will involve the investigation of term frequencies and associations combined with relevant visualization tools. The research will reveal the employers’ needs and will support training providers like universities to adapt curricula and training programmes so that they provide what the labour market requires. Moreover, the findings of this research could support young people in making better training choices, signal important trends related to occupations and skills.

Suggested Citation

  • Monica Mihaela Maer Matei & Anamaria Beatrice Aldea, 2019. "Employers’ Requirements for Data Scientists - an Analysis of Job Posts," Logos Universalitate Mentalitate Educatie Noutate - Sectiunea Stiinte Economice si Administrative/ Logos Universality Mentality Education Novelty - Section: Economical and Administrative Sciences, Editura Lumen, Department of Economics, vol. 4(1), pages 21-32, December.
    • Handle: RePEc:lum:rev14e:v:4:y:2019:i:1:p:21-32
    DOI: https://doi.org/10.18662/lumeneas/10
    as

    Download full text from publisher

    File URL: https://lumenpublishing.com/journals/index.php/lumeneas/article/view/2271
    Download Restriction: no
    File URL: https://libkey.io/https://doi.org/10.18662/lumeneas/10?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Brad Hershbein & Lisa B. Kahn, 2018. "Do Recessions Accelerate Routine-Biased Technological Change? Evidence from Vacancy Postings," American Economic Review, American Economic Association, vol. 108(7), pages 1737-1772, July.
    2. Stefan Debortoli & Oliver Müller & Jan Brocke, 2014. "Comparing Business Intelligence and Big Data Skills," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 289-300, October.
    3. Pater, Robert & Szkola, Jaroslaw & Kozak, Marcin, 2019. "A method for measuring detailed demand for workers' competences," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 13, pages 1-30.
    4. Monica Mihaela Maer-Matei & Cristina Mocanu & Ana-Maria Zamfir & Tiberiu Marian Georgescu, 2019. "Skill Needs for Early Career Researchers—A Text Mining Approach," Sustainability, MDPI, vol. 11(10), pages 1-17, May.
    5. Modestino, Alicia Sasser & Shoag, Daniel & Ballance, Joshua, 2016. "Downskilling: changes in employer skill requirements over the business cycle," Labour Economics, Elsevier, vol. 41(C), pages 333-347.
    6. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    7. Ahood Almaleh & Muhammad Ahtisham Aslam & Kawther Saeedi & Naif Radi Aljohani, 2019. "Align My Curriculum: A Framework to Bridge the Gap between Acquired University Curriculum and Required Market Skills," Sustainability, MDPI, vol. 11(9), pages 1-13, May.
    8. Alina Lavrinenko & Natalia Shmatko, 2019. "Twenty-First Century Skills in Finance: Prospects for a Profound Job Transformation," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 13(2), pages 42-51.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Grinis, Inna, 2017. "The STEM requirements of "non-STEM" jobs: evidence from UK online vacancy postings and implications for skills & knowledge shortages," LSE Research Online Documents on Economics 85123, London School of Economics and Political Science, LSE Library.
    2. Kapelyuk, Sergey & Karelin, Iliya, 2023. "Digital Skills: Classification, Empirical Estimates of the Demand," MPRA Paper 119644, University Library of Munich, Germany.
    3. José Azar & Emiliano Huet & Ioana Marinescu & Bledi Taska & Till von, 2024. "Minimum Wage Employment Effects and Labour Market Concentration," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(4), pages 1843-1883.
    4. Mary A. Burke & Alicia Sasser Modestino & Shahriar Sadighi & Rachel B. Sederberg & Bledi Taska, 2019. "No Longer Qualified? Changes in the Supply and Demand for Skills within Occupations," Working Papers 20-3, Federal Reserve Bank of Boston.
    5. Emilio Colombo & Alberto Marcato, 2021. "Skill Demand and Labour Market Concentration: Theory and Evidence from Italian Vacancies," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis2104, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).
    6. Bloom, Nicholas & Davis, Steven J. & Hansen, Stephen & Lambert, Peter John & Sadun, Raffaella & Taska, Bledi, 2023. "Remote work across jobs, companies and space," LSE Research Online Documents on Economics 121302, London School of Economics and Political Science, LSE Library.
    7. Peter Q. Blair & David J. Deming, 2020. "Structural Increases in Skill Demand after the Great Recession," NBER Working Papers 26680, National Bureau of Economic Research, Inc.
    8. Guo, Junjie, 2022. "The persistent impact of multiple offers," Labour Economics, Elsevier, vol. 74(C).
    9. Richard Friberg & Mark Sanctuary, 2020. "Exchange rate risk and the skill composition of labor," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 156(2), pages 287-312, May.
    10. Hemelt, Steven W. & Hershbein, Brad & Martin, Shawn & Stange, Kevin M., 2023. "College majors and skills: Evidence from the universe of online job ads," Labour Economics, Elsevier, vol. 85(C).
    11. Robert E. Hall & Marianna Kudlyak, 2022. "Why Has the US Economy Recovered So Consistently from Every Recession in the Past 70 Years?," NBER Macroeconomics Annual, University of Chicago Press, vol. 36(1), pages 1-55.
    12. Barbara Ermini & Luca Papi & Francesca Scaturro, 2022. "Over-education and the great recession. The case of Italian Ph.D graduates," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 76(3), pages 17-28, July-Sept.
    13. Lyu, Wenjing & Liu, Jin, 2021. "Soft skills, hard skills: What matters most? Evidence from job postings," Applied Energy, Elsevier, vol. 300(C).
    14. Azar, José & Marinescu, Ioana & Steinbaum, Marshall & Taska, Bledi, 2020. "Concentration in US labor markets: Evidence from online vacancy data," Labour Economics, Elsevier, vol. 66(C).
    15. Marcus Dillender, 2019. "Computerization of White Collar Jobs," Upjohn Working Papers 19-310, W.E. Upjohn Institute for Employment Research.
    16. Pater, Robert & Szkola, Jaroslaw & Kozak, Marcin, 2019. "A method for measuring detailed demand for workers' competences," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 13, pages 1-30.
    17. Alekseeva, Liudmila & Azar, José & Giné, Mireia & Samila, Sampsa & Taska, Bledi, 2021. "The demand for AI skills in the labor market," Labour Economics, Elsevier, vol. 71(C).
    18. Murillo Campello & Gaurav Kankanhalli & Pradeep Muthukrishnan, 2020. "Corporate Hiring under COVID-19: Labor Market Concentration, Downskilling, and Income Inequality," NBER Working Papers 27208, National Bureau of Economic Research, Inc.
    19. Bhuller, Manudeep & Kostøl, Andreas & Vigtel, Trond Christian, 2019. "How Broadband Internet Affects Labor Market Matching," Memorandum 10/2019, Oslo University, Department of Economics.

    More about this item

    Keywords

    labor market; data scientist; text mining; job posts;
    All these keywords.

    JEL classification:

    • A23 - General Economics and Teaching - - Economic Education and Teaching of Economics - - - Graduate

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:lum:rev14e:v:4:y:2019:i:1:p:21-32. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Antonio Sandu (email available below). General contact details of provider: https://edituralumen.ro .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.