Journal of the Korean Society for Library and Information Science (한국문헌정보학회지)

Korean Society For Library And Information Science (한국문헌정보학회)
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An Analysis of Data Science Curriculum in Korea

데이터과학 교육과정에 대한 분석적 연구

  • 이혜원 (서울여자대학교 사회과학대학 문헌정보학과) ;
  • 한승희 (서울여자대학교 사회과학대학 문헌정보학과)
  • Received : 2020.02.04
  • Accepted : 2020.02.19
  • Published : 2020.02.28
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Abstract

In this study, in order to analyze the current status of the data science curriculum in Korea as of October 2019, we conducted an analysis of the prior studies on the curriculum in the data science field and the competencies required for data professional. This study was conducted on 80 curricula and 2,041 courses, and analyzed from the following perspectives; 1) the analysis of the characteristics of data science domain, 2) the analysis of key competencies in data science, 3) the content analysis of the course titles. As a result, data science program in Korea has become a research-oriented professional curriculum based on an academic approach rather than a technical, vocational, and practitional view. In addition, it was confirmed that various courses were established with a focus on statistical analysis competency, and interdisciplinary characteristics based on information technology, statistics, and business administration were reflected in the curriculum.

이 연구에서는 2019년 10월 현재 우리나라에 개설된 데이터과학 교육과정의 현황을 분석하기 위해 먼저, 데이터과학 분야의 교육과정을 분석한 기존 연구와 데이터과학 분야 전문가에게 요구되는 역량에 대한 분석을 진행하였고, 이를 바탕으로 우리나라에 개설된 80개의 교육과정과 2,041개의 교과목을 대상으로 학문 영역 특징 기반 분석, 데이터 전문가 역량 기반 분석과 교과목명 내용 분석을 실시하였다. 분석 결과, 우리나라에서의 데이터과학 전공 교육은 기술과 직업 실무적 관점보다는 학문적 접근을 바탕으로 한 연구 중심의 전문적 교육과정으로 자리 잡았으며, 통계적 분석 역량을 중심으로 많은 교과가 개설되었고, 정보기술, 통계학, 경영학을 중심으로 한 학제적 특성이 교육과정에 반영되었음을 확인하였다.

Keywords

References

  1. Kang, J. H. 2016a. "Study on the Current Status of Data Science Curriculum in Library and Information Science and its Direction" Journal of Korean Library and Information Science Society, 47(3): 343-363. https://doi.org/10.16981/kliss.47.3.201609.343
  2. Kang, J. H. 2016b. Study on the Competency of Data Science Curriculum. Korean Society for Library and Information Science Society Occasional Papers Series, May 2016, 년 5월 27일, Pusan: Pusan National University, 105-116.
  3. Ministry of Education. 2016. Improvement of Undergraduate Degree for Raising Talents with Creativity-Innovation. Sejong: Ministry of Education.
  4. Kim, Y. H, Kim, Y. J. and Kim, Y. S. 2008. "The Structure of Production and Diffusion of Knowledge in Korean Communication Studies." Korean Journal of Journalism & Communication Studies, 52(1): 117-140.
  5. Park, S. H. and Lee, H. C. 2018. "The Traditional Market Activation Factor Derivation Research through Social Big Data - Focused on Seoul City Mangwon market and Suyu market." The Seoul Institute, 19(3): 1-18.
  6. Seo, Y. Jin. 2019. "A Student Survey on Interdisciplinary Major - A Preliminary Study for the Implementation of Convergent Curriculum." Korean Journal of General Education, 13(3): 229-247.
  7. Yoo, S. R. 2018. "A Diagnostic Analysis of LIS Curriculum from the Meta-literacy Perspective." Journal of The Korean Society For Library and Information Science, 52(2): 191-220. https://doi.org/10.4275/KSLIS.2018.52.2.191
  8. Yi, M. H. 2016. "A Study on the Curriculums of Data Science." Journal of The Korean Biblia Society For Library and Information Science, 27(1): 263-290. https://doi.org/10.14699/kbiblia.2016.27.1.263
  9. Lee, S. C. 2019. "Development of Big Data Curriculum in University" The Korea Contents Association Review, 17(2): 29-33.
  10. National Information Society Agency. 2014. NIA Bigdata Curriculum Reference Model. Daegu: National Information Society Agency.
  11. Agarwal, S. 2018. Understanding the Data Science Lifecycle. [online] [cited 2019. 12. 21.]
  12. Bussaban, K. and Waraporn, P. 2015. "Preparing undergraduate students majoring in Computer Science and Mathematics with Data Science perspectives and awareness in the age of Big Data." Social and Behavioral Sciences, 197: 1443-1446.
  13. Dhar, V. 2013. "Data science and prediction." Communications of the ACM, 56(12): 64-73. https://doi.org/10.1145/2500499
  14. Gil, Y. 2014. "Teaching Parallelism Without Programming:A Data Science Curriculum for Non-CS Students." IEEE, 42-48.
  15. Hayashi, C. 1998. "What is Data Science? Fundamental Concepts and a Heuristic Example." Data Science, Classification, and Related Methods, 40-51. doi:10.1007/978-4-431-65950-1_3
  16. iSchool@Syracuse. Data Science Competency. [online] [cited 2019. 12. 13.]
  17. Khan, H. R. and Du, Y. 2018. What is a Data Librarian?: A Content Analysis of Job Advertisements for Data Librarians in the United States Academic Libraries, IFLA WLIC 2018. [online] [cited 2020. 1. 9.]
  18. Liz L., Mattern, E., Acker A. and Langmead, A. 2015. "Applying Translational Principles to Data Science Curriculum Development." iPres 2015. [online] [cited 2020. 1. 3.]
  19. PCMI(The Park City Institute). 2016. Curriculum Guidelines for Undergraduate Programs in Data Science. [online] [cited 2019. 12. 23.]
  20. Sidhu, R. 2019. Life Cycle of a Data Science Project; The major steps involved in tackling a real-world data science problem. [online] [cited 2020. 1. 2.]
  21. Tang, R. and Sae-Lim, W. 2016. "Data science programs in U.S. higher education: An exploratory content analysis of program description, curriculum structure, and course focus." Education for Information, 32(2): 269-290. https://doi.org/10.3233/EFI-160977
  22. The Team Data Science Process(TDSP). The Team Data Science Process Lifecycle. [online] [cited 2020. 1. 11.]
  23. Thomas, C. V. L. and Urban, R. J. 2018 "What Do Data Librarians Think of the MLIS? Professionals' Perceptions of Knowledge Transfer, Trends, and Challenges" College & Research Libraries, 79(3): 401-423. https://doi.org/10.5860/crl.79.3.401
  24. Valiance Solutions. Lifecycle of Data Science Projects. [online] [cited 2019. 12. 29.]
  25. Zawadzki, K. 2014. Is Data Science a buzzword? Modern Data Scientist defined; Marketing Distillery. [online] [cited 2020. 1. 2.]
  26. Zhang, J., Fu, A., Wang, H. and Yin, S. 2017. "The development of data science education in China from the LIS perspective." International Journal of Librarianship, 2(2): 3-17.