A Bibliometric Analysis of Data Science: Trends, Contributions, and Research Developments

Authors

  • Muhamad Juliardi Universitas Budi Luhur
  • Ibnu Malik Universitas Budi Luhur

DOI:

https://doi.org/10.58812/wsis.v1i07.81

Keywords:

Data Science, Big Data, Data Analysis, Bibliometric Analysis, Publish or Perish, Mendeley, Vosviewer

Abstract

This research paper presents an analysis of the collected articles using the Publish or Perish software, consisting of the top 290 scientific articles listed on Google Scholar from 1947 to 2023. The study aims to explore the current state of the data science field in relation to technological advancements and the prevalence of big data. The findings reveal a significant and rapid development in the field, highlighting the importance of topics such as data science, big data, data analysis, and machine learning for further investigation.

References

J. M. Alonso, C. Castiello, and C. Mencar, A bibliometric analysis of the explainable artificial intelligence research field, vol. 853. Springer International Publishing, 2018. doi: 10.1007/978-3-319-91473-2_1.

O. B. Nielsen, “A Comprehensive Review of Data Governance Literature,” Issue Nr, vol. 8, no. 8, p. 3, 2017, [Online]. Available: http://aisel.aisnet.org/iris2017http://aisel.aisnet.org/iris2017/3

M. A. Waller and S. E. Fawcett, “Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management,” J. Bus. Logist., vol. 34, no. 2, pp. 77–84, 2013, doi: 10.1111/jbl.12010.

S. Barik, M. Moharana, S. Bhutia, N. Tripathy, and A. MOHAN, “Advances in data science, trends, and applications of artificial intelligence within the interaction between natural and artificial computation,” Neurocomputing, vol. 12, no. 06, pp. 314–343, 2022.

J. M. Górriz et al., “Artificial intelligence within the interplay between natural and artificial Computation: advances in data science, trends and applications,” Neurocomputing, 2020, doi: 10.1016/j.neucom.2020.05.078.

R. D. Peng and H. S. Parker, “Perspective on Data Science,” pp. 1–20, 2022.

L. Cao, “Data science: Challenges and directions,” Commun. ACM, vol. 60, no. 8, pp. 59–68, 2017, doi: 10.1145/3015456.

S. Khanra, A. Dhir, and M. Mäntymäki, “Big data analytics and enterprises : a bibliometric synthesis of the literature,” Enterp. Inf. Syst., vol. 00, no. 00, pp. 1–32, 2020, doi: 10.1080/17517575.2020.1734241.

F. Khan, S. A. Imtiaz, and S. Ahmed, “Review A bibliometric review and analysis of data-driven fault detection and diagnosis methods for process systems,” 2018, doi: 10.1021/acs.iecr.8b00936.

L. Ardito, V. Scuotto, M. Del Giudice, A. Messeni, and L. Ardito, “A bibliometric analysis of research on Big Data analytics for business and management analytics,” 2018, doi: 10.1108/MD-07-2018-0754.

D. Mishra, A. Gunasekaran, T. Papadopoulos, and S. J. Childe, “Big Data and supply chain management : a review and bibliometric analysis,” Ann. Oper. Res., 2016, doi: 10.1007/s10479-016-2236-y.

R. Heersmink and J. Van Den Hoven, “Bibliometric mapping of computer and information ethics,” pp. 241–249, 2011, doi: 10.1007/s10676-011-9273-7.

L. Cao, “Data science: A comprehensive overview,” ACM Comput. Surv., vol. 50, no. 3, 2017, doi: 10.1145/3076253.

I. Y. Song and Y. Zhu, “Big data and data science: what should we teach?,” Expert Syst., vol. 33, no. 4, pp. 364–373, 2016, doi: 10.1111/exsy.12130.

D. J. Power, “Data science: supporting decision-making,” J. Decis. Syst., vol. 25, no. 4, pp. 345–356, 2016, doi: 10.1080/12460125.2016.1171610.

I. Carmichael and J. S. Marron, “Data science vs. statistics: two cultures?,” Japanese J. Stat. Data Sci., vol. 1, no. 1, pp. 117–138, 2018, doi: 10.1007/s42081-018-0009-3.

M. M. de Medeiros, N. Hoppen, and A. C. G. Maçada, “Data science for business: benefits, challenges and opportunities,” Bottom Line, vol. 33, no. 2, pp. 149–163, 2020, doi: 10.1108/BL-12-2019-0132.

N. W. Grady, J. A. Payne, and H. Parker, “Agile big data analytics: AnalyticsOps for data science,” Proc. - 2017 IEEE Int. Conf. Big Data, Big Data 2017, vol. 2018-January, pp. 2331–2339, 2017, doi: 10.1109/BigData.2017.8258187.

G. Vicario and S. Coleman, “A review of data science in business and industry and a future view,” Appl. Stoch. Model. Bus. Ind., vol. 36, no. 1, pp. 6–18, 2020, doi: 10.1002/asmb.2488.

Z. Radovilsky, V. Hegde, A. Acharya, and U. Uma, “Skills Requirements of Business Data Analytics and Data Science Jobs: A Comparative Analysis,” Comp. Anal. J. Supply Chain Oper. Manag., vol. 16, no. 1, pp. 1–20, 2018, [Online]. Available: https://www.csupom.com/uploads/1/1/4/8/114895679/v16n1p5.pdf

C. V. Krishna, H. R. Rohit, and Mohana, “A review of artificial intelligence methods for data science and data analytics: Applications and research challenges,” Proc. Int. Conf. I-SMAC (IoT Soc. Mobile, Anal. Cloud), I-SMAC 2018, pp. 591–594, 2019, doi: 10.1109/I-SMAC.2018.8653670.

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Published

2023-07-31

How to Cite

Muhamad Juliardi, & Ibnu Malik. (2023). A Bibliometric Analysis of Data Science: Trends, Contributions, and Research Developments. West Science Interdisciplinary Studies, 1(07), 387–397. https://doi.org/10.58812/wsis.v1i07.81

Issue

Vol. 1 No. 07 (2023): West Science Interdisciplinary Studies

Section

Articles

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Copyright (c) 2023 Muhamad Juliardi, Ibnu Malik

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