Informatization Policy (정보화정책)

National Information Society Agency (한국지능정보사회진흥원)
권호 표지
DOI QR코드

DOI QR Code

A Curriculum Study to Strengthen AI and Data Science Job Competency

AI·데이터 사이언스 분야 직무 역량 강화를 위한 커리큘럼 연구

  • Kim, Hyo-Jung (Graduate School of Information, Yonsei University) ;
  • Kim, Hee-Woong (Graduate School of Information, Yonsei University)
  • Received : 2021.02.05
  • Accepted : 2021.03.30
  • Published : 2021.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

According to the Fourth Industrial Revolution, demand for and interest in jobs in the field of AI and data science - such as artificial intelligence/data analysts - are increasing. In order to keep pace with this trend, and to supply human resources that can effectively perform such jobs in the relevant fields in a timely manner, job seekers must develop the competencies required by the companies, and universities must be in charge of training. However, it is difficult to devise appropriate response strategies at the level of job seekers, companies and universities, which are stakeholders in terms of supplying suitably competent personnel. Therefore, the purpose of this study is to determine which competencies are required in practice in order to cultivate and supply human talents equipped with the necessary job competencies, and to propose plans for the development of the required competencies at the university level. In order to identify the required competencies in the field of AI and data science, data on job postings on the LinkedIn site, the recruitment platform, were analyzed using text mining techniques. Then, research was conducted with the aim of devising and proposing concrete plans for competency development at the university level by comparing and verifying the results of the international graduate school curriculum in the field of AI and data science, and the interview results with the hiring managers, respectively, with the results of the topic model.

4차 산업혁명에 따라 AI, 데이터 분석가 등 AI·데이터 사이언스 분야의 일자리에 대한 수요와 관심이 증가하고 있다. 그에 발맞춰 효과적으로 해당 분야의 직무를 수행할 수 있는 인력을 적시에 공급하기 위해서 구직자는 회사가 요구하는 역량을 개발하고, 대학은 양성 교육을 담당하여야 한다. 하지만, 적절한 역량을 갖춘 인력 공급의 이해 당사자인 구직자, 회사 그리고 대학 차원에서 적절한 대응 전략 마련에 어려움을 겪고 있다. 따라서, 본 연구는 필요충분한 직무 역량을 가진 인재 양성 및 공급을 위해 실무에서 요구되는 역량이 무엇인지 알아보고, 대학 차원에서의 역량 개발 방안을 제안하는 것을 목적으로 한다. AI·데이터 사이언스 분야에서의 필요 역량을 파악하고자 채용 플랫폼 링크드인(LinkedIn) 사이트의 채용공고 데이터를 텍스트 마이닝 기법을 활용하여 분석하였다. 이후, 국제적인 AI·데이터 사이언스 분야 대학원 교육과정과 채용 담당자와의 인터뷰 결과를 각각 토픽 모델의 결과와 비교 및 검증하는 절차를 통해, 대학 차원에서의 활용할 수 있는 커리큘럼을 제안하는 것으로 연구를 진행하였다.

Keywords

Acknowledgement

This paper was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2018S1A3A2075114)

References

  1. BCG (2020). "BCG on investing." March.
  2. Blei, D. (2003). "Latent dirichlet allocation." Journal of machine learning research, 3(4), 993-1022.
  3. Blei, D. (2012). "Probabilistic topic models." Communications of the ACM, 55(4), 24-27. https://doi.org/10.1145/2133806.2133815
  4. Cho, S., Shin, S. & Kang, D. (2018). "A Study on the Research Trends on Open Innovation using Topic Modeling." Informatization Policy, 25(3), 52-74 https://doi.org/10.22693/NIAIP.2018.25.3.052
  5. Choi, B. (2016). "Study on Core Competency Improvement of University Students and Its Related Factors based on the Desired Talents of Companies." Doctor's Thesis, Sogyung University.
  6. Choi, I. & Shin, E. (2019). "An Empirical Study of the Determinants of Successful Job Seeking of College Students." Korea Economic Education Association, 23(1), 23-49.
  7. Deming, D. & Kahn, L. (2018). "Skill Requirements across Firms and Labor Markets: Evidence from Job Postings for professionals." Journal of Labor Economics, 36(S1), 337-360.
  8. Desmet, P. & Hekkert, P. (2007). "Framework of Product Experience." International Journal of Design, 1(1), 13-23.
  9. Frey, C. & Osborne, M. (2017). "The Future of Employment: How Susceptible are Jobs to Computerisation?" Technological Forecasting and Social Change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019
  10. Guo, Y., Barnes, S. & Jia, Q. (2017). "Mining meaning from online ratings and reviews: tourist satisfaction analysis using latent dirichlet allocation." Tourism Management, 59, 467-483. https://doi.org/10.1016/j.tourman.2016.09.009
  11. Jung, Y. & Suh, Y. (2019). "Mining the voice of employees: A text mining approach to identifying and analyzing job satisfaction factors from online employee reviews." Decision Support Systems, 123, 1-12.
  12. Karakatsanis, L., AlKhader, W., MacCrory, F. & Alibasic, A. (2017). "Data mining approach to monitoring the requirements of the job market: A case study." Information Systems, 65, 1-6. https://doi.org/10.1016/j.is.2016.10.009
  13. Kim, D., Kang, J. & Lim, J. (2016). "Comparative Analysis of Job Satisfaction Factors, Using LDA Topic Modeling by Industries : The Case Study of Job Planet Reviews." Journal of Information Technology Services, 15(3), 157-171. https://doi.org/10.9716/KITS.2016.15.3.157
  14. Kim, J. & Huh, J. (2018). "Analysis of Occupation and Job Change in SW Field using Job Posting Data." The Korean Institute of Information Scientists and Engineers, 36(9), 15-21.
  15. Kim, H. & Lee, H. (2016). "Trend Analysis of Data Mining Research Using Topic Network Analysis." The Korean Society Of Computer And Information, 21(5), 141-148.
  16. Kim, S. (2020). "A Study on the Similarity between the Self-introduction and the Talent of the company Using Text Mining." Master's Thesis, Soongsil University.
  17. Korea Employment Information Service(KEIS). (2019). Future job prospects of the 4th industrial revolution. Seoul: Korea Employment Information Service(KEIS).
  18. Korea Employment Information Service(KEIS). (2020). Analysis of skill mismatch of IT technology workers in the IT service industry. Seoul: Korea Employment Information Service(KEIS).
  19. Lau, J. (2014). Machine reading tea leaves: automatically evaluating topic coherence and topic model quality. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics.
  20. Lee, J. (2020). "Forecasting Open Government Data Demand Using Keyword Network Analysis." Informatization Policy, 27(4), 24-46. https://doi.org/10.22693/NIAIP.2020.27.4.024
  21. Lee, S., Choi, S. & Kim, H. (2019). "An Exploratory Study of e-Learning Satisfaction: A Mixed Methods of Text Mining and Interview Approaches." Information Systems Review, 21(1), 39-59. https://doi.org/10.14329/isr.2019.21.1.039
  22. Lim, S., Yang, M., Han, D. & Park, J. (2020). "The Study of Job Market by Analysis of the Recruitment Advertisement for Occupational Therapy." Therapeutic Science for Rehabilitation, 9(2), 39-53. https://doi.org/10.22683/TSNR.2020.9.2.039
  23. Lin, C. & He, Y. (2009). Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM conference on Information and knowledge management.
  24. Mckensey (2020). "The recovery will be digital." August.
  25. Mimno, D. (2011). Optimizing semantic coherence in topic models. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing.
  26. Newman, M. (2010). Networks : An Introduction. Oxford University Press.
  27. Shin, J., Choi, J. & Koh, W. (2015). "A study on the Use of Learning Analytics in Higher Education: Focusing on the perspective of professors." Journal of Educational Technology, 31(2), 223-252. https://doi.org/10.17232/KSET.31.2.223
  28. Steyvers, M. & Griffiths, T. (2007). "Probabilistic topic models." Handbook of latent semantic analysis, 427(7), 424-440.
  29. Xianghua, F., Guo, L., Yanyan, G. & Zhiqiang, W. (2013). "Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon." KnowledgeBased Systems, 37, 186-195.
  30. Yun, Y. & Ji, H. (2015). "A development of Open Social Learning Platform for learning analytics and educational data mining." Journal of Institute of Information Scientists and Engineers, 23(12), 1349-1351.