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Development of a Job Advertisement Analysis for Assessing Data Science Competencies Cover

Development of a Job Advertisement Analysis for Assessing Data Science Competencies

Open Access
|Sep 2023

References

  1. Almaleh, A, et al. 2019. Align My Curriculum: A Framework to Bridge the Gap between Acquired University Curriculum and Required Market Skills. Sustainability, 11(9): 2607. DOI: 10.3390/su11092607
  2. Bloom, BS. 1956. Taxonomy of educational objectives: The classification of educational goals. New York: David McKay Company.
  3. Boselli, R, et al. 2018. WoLMIS: a labor market intelligence system for classifying web job vacancies. Journal of Intelligent Information Systems, 51(3): 477502. DOI: 10.1007/s10844-017-0488-x
  4. Breitfuss, G, et al. 2019. The Data-Driven Business Value Matrix-A Classification Scheme for Data-Driven Business Models. Bled eConference, 19. DOI: 10.18690/978-961-286-280-0.42
  5. Cao, L. 2017. Data science: challenges and directions. Communications of the ACM, 60(8): 5968. DOI: 10.1145/3015456
  6. Dadzie, AS, et al. 2018. Structuring visual exploratory analysis of skill demand. Journal of Web Semantics, 49: 5170. DOI: 10.1016/j.websem.2017.12.004
  7. Debortoli, S, Müller, O and vom Brocke, J. 2014. Comparing business intelligence and big data skills. Business & Information Systems Engineering, 6(5): 289300. DOI: 10.1007/s12599-014-0344-2
  8. Djumalieva, J, Lima, A and Sleeman, C, et al. 2018. Classifying occupations according to their skill requirements in job advertisements. Economic Statistics Centre of Excellence Discussion Paper, 4: 2018.
  9. Donoho, D. 2017. 50 years of data science. Journal of Computational and Graphical Statistics, 26(4): 745766. DOI: 10.1080/10618600.2017.1384734
  10. Harper, R. 2012. The collection and analysis of job advertisements: A review of research methodology. Library and Information Research, 36(112): 2954. DOI: 10.29173/lirg499
  11. Hattingh, M, et al. 2019. Data Science Competency in Organisations: A Systematic Review and Unified Model. Proceedings of the South African Institute of Computer Scientists and Information Technologists 2019, 18. DOI: 10.1145/3351108.3351110
  12. Khaouja, I, et al. 2019. Building a soft skill taxonomy from job openings. Social Network Analysis and Mining, 9(1): 119. DOI: 10.1007/s13278-019-0583-9
  13. Khobreh, M, et al. 2015. An ontology-based approach for the semantic representation of job knowledge. IEEE Transactions on Emerging Topics in Computing, 4(3): 462473. DOI: 10.1109/TETC.2015.2449662
  14. Von Konsky, B, Miller, C and Jones, A. 2016. The skills framework for the information age: Engaging stakeholders in curriculum design. Journal of Information Systems Education, 27(1): 37.
  15. Lima, A, Bakhshi, B, et al. 2018. Classifying occupations using web-based job advertisements: an application to STEM and creative occupations. Economic Statistics Centre of Excellence Discussion Paper, 8: 2018.
  16. Mandinach, EB, et al. 2015. Ethical and appropriate data use requires data literacy. Phi Delta Kappan, 96(5): 2528. DOI: 10.1177/0031721715569465
  17. Murawski, M and Bick, M. 2017. Demanded and imparted big data competences: towards an integrative analysis. In Proceedings of the 25th European Conference on Information Systems (ECIS), Guimarães, Portugal, June 5–10, 2017, 13751390.
  18. National Academies of Sciences, Engineering, Medicine (NASEM). 2018. Envisioning the data science discipline: the undergraduate perspective: interim report. National Academies Press.
  19. Ridsdale, C, Rothwell, J, Smit, M, Ali-Hassan, H, Bliemel, M, Irvine, D, Kelley, D, Matwin, S and Wuetherick, B. 2015. Strategies and best practices for data literacy education. Knowledge synthesis report. SSHRC. DOI: 10.13140/RG.2.1.1922.5044
  20. Saltz, J, Armour, F and Sharda, M. 2018. Data science roles and the types of data science programs. Communications of the Association for Information Systems, 43(1): 33. DOI: 10.17705/1CAIS.04333
  21. SFIA. 2018. The global skills and competency framework for a digital world. Available at: https://sfia-online.org/en/sfia-7 [Last accessed on 4 April 2021].
  22. Shirani, A. 2016. Identifying Data Science and Analytics Competencies Based on Industry Demand. Issues in Information Systems, 17(4).
  23. Sibarani, E, et al. 2020. Skills and Recruitment Ontology.
  24. Sibarani, EM, et al. 2017. Ontology-guided job market demand analysis: a cross-sectional study for the data science field. Proceedings of the 13th International Conference on Semantic Systems, 2532. DOI: 10.1145/3132218.3132228
  25. Silveira, CC, et al. 2020. What is a Data Scientist? Analysis of core soft and technical competencies in job postings. Revista Inovação, Projetos e Tecnologias–IPTEC, 8(1): 2539. DOI: 10.5585/iptec.v8i1.17263
  26. Wowczko, IA. 2015. Skills and vacancy analysis with data mining techniques. Informatics, 2(4): 3149. DOI: 10.3390/informatics2040031
  27. Zhao, M, et al. 2015. SKILL: A system for skill identification and normalization. Proceedings of the twenty-ninth AAAI conference on artificial intelligence, 40124017. DOI: 10.1609/aaai.v29i2.19064
Language: English
Submitted on: Aug 25, 2021
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Accepted on: Apr 11, 2023
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Published on: Sep 7, 2023
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2023 Jan Vogt, Thilo Voigt, Annika Nowak, Jan M. Pawlowski, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.