Article Sidebar

Download
Download Article

Main Article Content

Abstract

Digital business transformation and the 5.0 era of the industry have made data science develop rapidly, and it is much needed to capture the potential of big data as a basis for business decision-making. How the Faculty of Economics and Business integrates data science into the curriculum will be challenging, considering that data science requires an understanding of mathematics, statistics, and technology. This research attempts to define data science and design the integration of data science into economics and business. Through the literature review process, it can be understood that data science is a multidisciplinary science (Mathematics, statistics, and other sciences according to their respective fields). Based on this, integrating data science into economics and business can be carried out by designing a curriculum that considers conceptual aspects of theory and the technical continuity of the use of technology.

Keywords

Data Science Economics and Business Scientific Integration

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite
Palagan, G. P. (2025). Implementation of Data Science in Economics and Business. Golden Ratio of Data in Summary, 5(1), 163–172. https://doi.org/10.52970/grdis.v5i1.899
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Alghafiqi, B., & Munajat, E. (2022). Impact of Artificial Intelligence Technology on Accounting Profession. Berkala Akuntansi Dan Keuangan Indonesia,7 (2), 140-159. https://doi.org/10.20473/baki.v7i2.27934
  2. Anand, V., Bochkay, K., Chychyla, R., & Leone, A. (2020). Using Python for text analysis in accounting research. Foundations and Trends in Accounting,14 (3-4), 128-359. https://doi.org/10.1561/1400000062
  3. Bramwell, M. (2001). The future of data analysis. Scientific Computing and Instrumentation,18 (3), 20–22. https://doi.org/10.1214/aoms/1177704711
  4. Dewi, S., Al Kautsar, H. A., & Utami, D. Y. (2023). Prediction of Marketing Success of Banking Services Using the Logistic Regression Algorithm. Computer Science (CO-SCIENCE),3 (2), 118-125. https://doi.org/10.31294/coscience.v3i2.1931
  5. Donoho, D. (2017). 50 Years of Data Science. Journal of Computational and Graphical Statistics,26 (4), 745–766. https://doi.org/10.1080/10618600.2017.1384734
  6. Dumbill, E., Liddy, E. D., Stanton, J., Mueller, K., & Farnham, S. (2013). Educating the Next Generation of Data Scientists. Big Data,1 (1), 21–27. https://doi.org/10.1089/big.2013.1510
  7. Finzer, W. (2013). The Data Science Education Dilemma. Technology Innovations in Statistics Education,7 (2). https://doi.org/10.5070/t572013891
  8. Gayatri, G., Jaya, I. G. N. M., & Rumata, V. M. (2023). The Indonesian Digital Workforce Gaps in 2021-2025. Sustainability (Switzerland),15 (1). https://doi.org/10.3390/su15010754
  9. Grossi, V., Giannotti, F., Pedreschi, D., Manghi, P., Pagano, P., & Assante, M. (2021). Data science: a game changer for science and innovation. International Journal of Data Science and Analytics,11 (4), 263-278. https://doi.org/10.1007/s41060-020-00240-2
  10. Harahap, K., Husrizalsyah, D., Setiana, E., & Habibi, M. R. (2017). Development of XBRL Teaching Materials in Accounting Information Systems Course. Indonesian Journal of Accounting, Finance & Taxation, 5(2).
  11. Harahap, M., Lubis, Y., & Situmorang, Z. (2022). Business Marketing Analysis with Data Science: Customer Personality Segmentation based on K-Means Clustering Algorithm. Data Sciences Indonesia (DSI),1 (2), 76-88. https://doi.org/10.47709/dsi.v1i2.1348
  12. Henry Ejiga Adama, & Chukwuekem David Okeke. (2024). Digital transformation as a catalyst for business model innovation: A critical review of impact and implementation strategies. Magna Scientia Advanced Research and Reviews,10 (2), 256-264. https://doi.org/10.30574/msarr.2024.10.2.0066
  13. Hilpisch, Y. (2014). Python for Finance. In The FEBS journal (Vol. 281). Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/25139866%5Cnhttp://books.google.com/books?hl=en&lr=&id=OH9vAwAAQBAJ&oi=fnd&pg=PT15&dq=Python+for+Finance&ots=oqDUOB4rHS&sig=w4HUJ1kH7B5INOEKn3DVB-aSd6Y
  14. Kroese, D. P., Botev, Z. I., Taimre, T., & Vaisman, R. (2019). Data Science and Machine Learning. Data Science and Machine Learning, (October). https://doi.org/10.1201/9780367816971
  15. Li, G., Yuan, C., Kamarthi, S., Moghaddam, M., & Jin, X. (2021). Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis. Journal of Manufacturing Systems,60 (July), 692–706. https://doi.org/10.1016/j.jmsy.2021.07.007
  16. Maddalena, D. V., & Francesca, E. (2020). How universities fill the talent gap: The data scientist in the Italian case. African Journal of Business Management,14 (2), 53-64. https://doi.org/10.5897/ajbm2019.8885
  17. Maslianko, P. P., & Sielskyi, Y. P. (2021). Data science - definition and structural representation. System Research and Information Technologies,2021 (1), 61-78. https://doi.org/10.20535/SRIT.2308-8893.2021.1.05
  18. Mitchell, R. (2018). Ryan Mitchell Web Scraping with Python. Retrieved from www.allitebooks.com
  19. Nosratabadi, S., Mosavi, A., Duan, P., Ghamisi, P., Filip, F., Band, S. S., ... Gandomi, A. H. (2020). Data science in economics: Comprehensive review of advanced machine learning and deep learning methods. Mathematics,8 (10), 1-25. https://doi.org/10.3390/math8101799
  20. Radovilsky, Z., Hegde, V., Acharya, A., & Uma, U. (2018). Skills Requirements of Business Data Analytics and Data Science Jobs: A Comparative Analysis. Comparative Analysis Journal of Supply Chain and Operations Management,16 (1), 1-20. Retrieved from https://www.csupom.com/uploads/1/1/4/8/114895679/v16n1p5.pdf
  21. Ragazou, K., Passas, I., Garefalakis, A., Galariotis, E., & Zopounidis, C. (2023). Big Data Analytics Applications in Information Management Driving Operational Efficiencies and Decision-Making: Mapping the Field of Knowledge with Bibliometric Analysis Using R. Big Data and Cognitive Computing,7 (1). https://doi.org/10.3390/bdcc7010013
  22. Rosihan, R., Fhadli, M., & Usman, A. A. H. (2023). Classification of Creditworthiness Using the Decision Tree Method with Feature Selection (Case Study: PT. Adira Finance Ternate City Branch). Tambusai Education Journal,7, 21517-21524. Retrieved from https://www.jptam.org/index.php/jptam/article/view/9915
  23. Sargent, T. J., & Stachurski, J. (2023). Python Programming for Economics and Finance.
  24. Shamroukh, S., & Johnson, T. (2023). Using Factor Analysis to Determine the Factors Impacting Learning Python for Non-Technical Business Analytics Graduate Students. Journal of Data Analysis and Information Processing,11 (04), 512-535. https://doi.org/10.4236/jdaip.2023.114026
  25. Stoudt, S., Scotina, A. D., & Luebke, K. (2022). Supporting Statistics and Data Science Education with learnr. Technology Innovations in Statistics Education,14 (1). https://doi.org/10.5070/t514156264
  26. Suwandi, E., Xuan, T. Le, & Nelson, H. A. (2023). Analysis of Human Resource Analytics Function in a Company. Journal of Science and Technology, 4(3), 68–71.
  27. Tukey, J. W. (1972). Data analysis, computation, and mathematics. Quarterly of Applied Mathematics,30 (1), 51–65. https://doi.org/10.1090/qam/99740
  28. Van Tonder, C., Schachtebeck, C., Nieuwenhuizen, C., & Bossink, B. (2020). A framework for digital transformation and business model innovation. Management (Croatia),25 (2), 111-132. https://doi.org/10.30924/mjcmi.25.2.6
  29. WEF, W. E. F. (2019). Data science in the new economy: A new race for talent in the Fourth Industrial Revolution. World Economic Forum Annual Meeting 2019, 1(1), 1–22.

Similar Articles

<< < 21 22 23 24 25 26 27 28 29 30 > >> 

You may also start an advanced similarity search for this article.