Implementation of Data Science in Economics and Business
Article Sidebar
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.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
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
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
Bramwell, M. (2001). The future of data analysis. Scientific Computing and Instrumentation,18 (3), 20–22. https://doi.org/10.1214/aoms/1177704711
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
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
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
Finzer, W. (2013). The Data Science Education Dilemma. Technology Innovations in Statistics Education,7 (2). https://doi.org/10.5070/t572013891
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
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
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).
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
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
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
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
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
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
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
Mitchell, R. (2018). Ryan Mitchell Web Scraping with Python. Retrieved from www.allitebooks.com
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
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
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
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
Sargent, T. J., & Stachurski, J. (2023). Python Programming for Economics and Finance.
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
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
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.
Tukey, J. W. (1972). Data analysis, computation, and mathematics. Quarterly of Applied Mathematics,30 (1), 51–65. https://doi.org/10.1090/qam/99740
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
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.