Journal of Korean Library and Information Science Society (한국도서관정보학회지)

Korean Library and Information Science Society (한국도서관정보학회)
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Degree Programs in Data Science at the School of Information in the States

미국 정보 대학의 데이터사이언스 학위 현황 연구

  • 박형주 (충남대학교 문헌정보학과)
  • Received : 2022.05.25
  • Accepted : 2022.06.14
  • Published : 2022.06.30
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Abstract

This preliminary study examined the degree programs in data science at the School of Information in the States. The focus of this study was the data science degrees offered at the School of Information awarded by the 64 Library and Information Science (LIS) programs accredited by the American Library Association (ALA) in 2022. In addition, this study examined the degrees, majors, minors, specialized tracks, and certificates in data science, as well as the potential careers after earning a data science degree. Overall, eight Schools of Information (iSchools) offered 12 data science degrees. Data science courses at the School of Information focus on topics such as introduction to data science, information retrieval, data mining, database, data and humanities, machine learning, metadata, research methods, data analysis and visualization, internship/capstone, ethics and security, user, policy, and curation and management. Most schools did not offer traditional LIS courses. After earning the data science degree in the School of Information, the potential careers included data scientists, data engineers and data analysts. The researcher hopes the findings of this study can be used as a starting point to discuss the directions of data science programs from the perspectives of the information field, specifically the degrees, majors, minors, specialized tracks and certificates in data science.

본 연구의 목적은 문헌정보학 프로그램이 있는 정보 대학에서 수여하는 데이터사이언스 학위의 현황을 알아보는 것이다. 데이터 수집의 대상은, 2022년 미국도서관협회의 인가를 받은 문헌정보학 프로그램이 있는 64개의 대학에서 수여하는 데이터사이언스 학위였다. 분석의 대상은 각 대학의 데이터사이언스 학위 과정, 부전공, 세부 전공, 수료증, 취업 후 예상 진로, 취업률 등이었다. 교과 분석을 위해 미국 정보 대학에서 제시한 교과목 명, 교과 설명, 중점 교육 분야를 분석했다. 데이터사이언스를 학위 명으로 개설한 대학은 총 8개 정보 대학의 12개 학위였으며, 학사 학위 5개, 석사 학위 6개, 박사 학위 1개였다. 개설된 교과의 주제는 데이터사이언스 입문, 정보검색, 데이터마이닝, 데이터베이스, 데이터와 인문학, 머신 러닝, 메타데이터, 연구 방법론, 데이터 분석 및 시각화, 실습/캡스톤, 윤리 및 보안, 이용자, 정책, 큐레이션 및 관리였다. 대부분의 대학은 전통적인 문헌정보학 교과를 개설하지 않고 있었다. 정보 대학이 제시한 졸업 후 예상 취업 진로는 데이터사이언티스트, 데이터 엔지니어, 데이터 분석가 등이었다. 본 연구의 결과는 정보학의 관점에서 데이터사이언스 학위 과정, 세부 전공, 수료증 또는 교과과정 개발 및 개정을 위한 논의에 활용될 수 있는 기초 자료로 활용되기를 기대한다.

Keywords

References

  1. Bae, Jinhee, Kim, Kyoung Huy, Kim, Keunkon, Seo, Hye Seok, & Lee, Jeong Mee (2012). A study on effectiveness of project learning in social welfare education. Korean Journal of Social Welfare Education, 18, 1-22.
  2. Bae, Jinhee, Kim, Se Ryeong, & Jung, Jaemin (2021). A study on the development of convergence subjects to improve social problem solving ability. Korean Journal of Social Welfare Education, 56, 131-157. https://doi.org/10.31409/KJSWE.2021.56.131
  3. Chang, Youngjae (2017). The direction of data science education in the fourth industrial revolution era: focusing on understanding of artificial intelligence and data initiative. The Journal of Integrated Humanities, 9(10), 155-180.
  4. Cho, Wooje & Yu, Mi Rim (2018). Creating value for education through big data analysis education programs. The Korea Journal of Big Data, 3(2), 123-130. https://doi.org/10.36498/kbigdt.2018.3.2.123
  5. Jang, Duk-Hyun (2015). iSchool movement: a critical discourse analysis. Journal of Korean Library and Information Science Society, 46(1), 135-154. https://doi.org/10.16981/kliss.46.1.201503.135
  6. Jung, Seungwha & Do, Jaewoo (2019). A case study on operation of big data educational program. Journal of Education & Culture, 25(5), 621. https://doi.org/10.24159/joec.2019.25.5.621
  7. Kang, Ji Hei (2016). 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
  8. Kim, Hyo-Jung & Kim, Hee-Woong (2021). A curriculum study to strengthen AI and data science job competency. Information Policy, 28(2), 34-56. https://do.org/10.22693/NIAIP.2021.28.2.034
  9. Kim, Misung, Choi, Myoungsook, & Lee, Sung-Ha (2021). An analysis of domestic research trends on university convergence education: focusing on academic papers published in registered journals from 2011 to 2020. The Korean Journal of Educational Methodology Studies, 33(1), 77-99. https://doi.org/10.17927/tkjems.2021.33.1.77
  10. Kim, Yongmin (2018). Data Science Education Program to Improve Computational Thinking and Creativity. Doctoral dissertation, Jeju National University. Republic of Korea.
  11. Lee, Hee Yong (2020). A study on the present state of convergence curriculum and convergence methodology: in humanities, social sciences and arts. Korean Journal of Arts Education, 18(4), 135-154.
  12. Lee, Hee Yong, Yoon, Ahyoung, & Kim, Jae-Deuk (2014). A study on the present state of humanities and arts convergence education in Korean universities. Studies in Humanities and Social Sciences, 44, 183-222. https://doi.org/10.17939/hushss.2014..44.007
  13. Lee, Hyewon & Han, Seunghee (2020). An analysis of data science curriculum in Korea. Journal of the Korean Society for Library and Information Science, 54(1), 365-385. https://doi.org/10.4275/KSLIS.2020.54.1.365
  14. Leem, Jung-Hoon (2020). A study on the educational applicability of team teaching for facilitating convergence education in higher education. Journal of Educational Innovation Research, 30(3), 23-51. https://doi.org/10.21024/pnuedi.30.3.202009.23
  15. Ministry of Science and ICT (2022, January 27). (4th revision) Announcement of integration of new tasks for nurturing scientific and technological innovation talents in 2022. Available: https://www.msit.go.kr/bbs/view.do?sCode=user&mId=129&mPid=128&bbsSeqNo=100&nttSeqNo=3177549
  16. Ministry of Science and ICT & Korea Data Agency (2022). A summary of the main results of the study on the status of the data industry in 2021. Available: https://www.kdata.or.kr/kr/board/info_01/boardView.do?bbsIdx=33172
  17. Park, Hyoungjoo (2022a). An examination of core competencies for data librarians. Journal of the Korean Biblia Society for Library and Information Science, 33(1), 301-319. https://doi.org/10.14699/kbiblia.2022.33.1.301
  18. Park, Hyoungjoo (2022b). An examination of the course syllabi related to data science at the ALA-accredited library and information science programs. Journal of the Korean Society for Information Management, 39(1), 119-143. https://doi.org/10.3743/KOSIM.2022.39.1.119
  19. Park, Jiin & Park, Ji-Hong (2021). A study on the job duties and competencies of data librarians: using job advertisement analysis in the United States. Journal of the Korean Biblia Society for Library and Informaton Science, 32(3), 145-162. https://doi.org/10.14699/kbiblia.2021.32.3.145
  20. SungKyunKwan University (2019). Convergence major in data science (school year 2019). Available: https://lis.skku.edu/lis/community/under_notice.do?mode=download&articleNo=50833&attachNo=35390
  21. Yi, Myongho (2016). A study on the curricullum 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
  22. Youn, Jin Young, Kim, Yu Mi, So, Jae Hwan, & Kim, Yeon Hyeoung (2019). A study on the media art STEAM education program using data science and artificial intelligence. The Korean Society of Science & Art, 37(5), 265-276. https://doi.org/10.17548/ksaf.2019.12.30.265
  23. American Library Association. (2022, March 25). Directory of institutions offering ALA-accredited master's programs in library and information studies. Available: https://www.ala.org/educationcareers/accreditedprograms/directory
  24. Anselmo, J. (2021). A social science major is coming to UMD in fall 2022. Available: https://dbknews.com/2021/04/25/a-social-data-science-major-is-coming-to-umd-in-fall-2022/
  25. Brady, E. H. (2019). The challenge of big data and data science. Annual Review of Political Science, 22, 297-323. https://doi.org/10.1146/annurev-polisci-090216-023229
  26. Burton, M. & Lyon, L. (2017). Data science in libraries. Bulletin of the Association for Information Science and Technology, 43(4), 33-35. https://doi.org/10.1002/bul2.2017.1720430409
  27. Bush, V. (1945). As we may think. Atlantic Monthly, 176(1), 101-108.
  28. Cervone, H. F. (2015). Informatics and data science: an overview for the information professional. Digital Library Perspectives, 32(1), 7-10. https://doi.org/10.1108/DLP-10-2015-0022
  29. Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64. https://doi.org/10.1145/2500499
  30. Executive Office of the President (2016). The federal big data research and development strategic plan. Available: https://www.nitrd.gov/PUBS/bigdatardstrategicplan.pdf
  31. Griffith, B. C. (1980). Key Papers in Information Science. New York: White Plains.
  32. Harris-Pierce, R. L. & Liu, Y. Q. (2012). Is data curation education at library and information science schools in North America adequate? New Library World, 113(11/12), 598-613. https://doi.org/10.1108/03074801211282957
  33. Indiana University Purdue University - Indianapolis (2021). Available: https://tableau.bi.iu.edu/t/prd/views/FirstDestination2016/CareerOutcomesGroup?iframeSizedToWindow=true&%3Aembed=y&%3AshowAppBanner=false&%3Adisplay_count=no&%3AshowVizHome=no#1#1
  34. Indiana University Purdue University - Indiannapolis [n.d.]. Data Science B.S. (online) + applied data science M.S. Available: https://soic.iupui.edu/degrees/accelerated-bachelors-masters/data-science-ads
  35. iSchools Inc. (2022). Directory. Available: https://ischools.org/Directory
  36. Kellam, L. & Thompson, K. (2016). Introduction to Databrarianship: the Academic Data Librarian in Theory and Practice. Chicago, IL: Association of College and Research Libraries.
  37. Kim, J., Warga, E., & Moen, W. (2013). Competencies required for digital curation: an analysis of job advertisements. International Journal of Digital Curation, 66-83. https://doi.org/10.2218/ijdc.v8i1.242
  38. Li, S., Zhuang, X., Xing, W., & Guo, W. (2013). The cultivation of scientific data specialists: development of LIS education oriented e-science service requirements. Library Hi Tech, 31(4), 700-724. https://doi.org/10.1108/LHT-06-2013-0070
  39. Lyon, L. & Brenner, A. (2015). Bridging the data talent gap: positioning the iSchool as an agent for change. International Journal of Digital Curation, 10(1), 111-122. https://doi.org/10.2218/ijdc.v10i1.349
  40. Lyon, L., Mattern, E., Acker, A., & Langmead, A. (2015). Applying translational principles to data science curriculum. Proceedings of the iPres. Chapel Hill. Available: http://d-scholarship.pitt.edu/id/eprint/27159
  41. Macelil, M. (2015). Creating tomorrow's technologists: contrasting information technology curriculum in north American library and information science graduate programs against Code4lib job listings. Journal of Education for Library and Information Science, 56(3), 198-212. https://doi.org/10.3138/jelis.56.3.198
  42. Robinson, L. (2009). Information science: communication chain and domain analysis. Journal of Documentation, 65(4), 578-591. https://doi.org/10.1108/00220410910970267
  43. Semeler, A., Pinto, A., & Rozados, H. (2019). Data science in data librarianship: core competencies of a data librarian. Journal of Librarianship and Information Science, 51(3), 771-780. https://doi.org/10.1177/0961000617742465
  44. Shi, Y., Yu, P. S., Zhu, Y., & Tian, Y. (2014). Explore new field of data science under big data era: preface for ICDS 2014. Procedia Computer Science, 30, 1-3. https://doi.org/10.1016/j.procs.2014.05.374
  45. Stanton, J. M. (2012). Data science: what's in it for the new librarian? Available: https://ischool.syr.edu/data-science-whats-in-it-for-the-new-librarian/
  46. Syracuse University [n.d.]. Career outlook. Available: https://ischool.syr.edu/careers/career-outlook
  47. Tang, R. & Sae-Lim, W. (2017). Data science programs in U.S. higher education: an interview with the authors. Journal of eScience Librarianship, 6(1), e1105. https://doi.org/10.7191/jeslib.2017.1105
  48. University of Illinois - Urbana Champaign [n.d.-a]. iSchool illini success 2018-2020. Available: https://ischool.illinois.edu/sites/default/files/documents/MSLIS%20first%20desti nations%202018-20.pdf
  49. University of Illinois - Urbana Champaign [n.d.-b]. BS in Information Science + Data Science. Available: https://ischool.illinois.edu/degrees-programs/undergraduate/bs-data-science
  50. University of Michigan (2022). Career and salary information. Available: https://www.si.umich.edu/employers/career-and-salary-information#:~:text=In%20recent%20rankings%2C%20U%2DM%20schools,satisfaction%20(95%25%20overall)
  51. Wang, L. (2018). Twinning data science with information science in schools of library and information science. Journal of Documentation, 74(6), 1243-1257. https://doi.org/10.1108/JD-02-2018-0036
  52. Zawadzki, K. (2014). Is data science a buzzword? Available: https://mywebvault.wordpress.com/2017/05/18/is-data-science-a-buzzword-modern-data-scientist-defined-marketing-distillery/
  53. Zhu, Y. & Xiong, Y. (2015). Towards data science. Data Science Journal, 14(8), 1-7. https://doi.org/10.5334/dsj-2015-008