Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 52 , 01 December 2023
Open
Access | Article
Whether the Salary of Data Scientists Can Be Predicted: New Evidence
* Author to whom correspondence should be addressed.
Abstract
This study uses exploratory data analysis and an exploratory prediction model to examine data scientist salaries. The study examines salary determinants, salary trends, and a forecast model for data scientist salaries. The collection includes wage estimates, job descriptions, company evaluations, and industry data. Descriptive statistics and visualizations reveal variable distributions and trends. Linear regression is used to estimate salaries using geography, industry, firm rating, and job description. However, the original model has a large prediction error, requiring refining. The findings present significant implications for job seekers, companies, and policymakers, necessitating a thorough understanding and response from all these stakeholders. Addressing issues related to data availability and biases becomes imperative, as these could potentially distort the insights and the resulting decisions. Therefore, it's crucial to emphasize and encourage future research in this area to ensure a more equitable and comprehensive approach to employment and policy development.
Keywords
data science, machine learning, salary
References
1. Data scientist: The sexiest job of the 21st Century. Retrieved from https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century, last access on 2023/8/7.
2. Quan, T. Z., and Raheem, M. (2022) Salary Prediction in Data Science Field Using Specialized Skills and Job Benefits–A Literature. Journal of Applied Technology and Innovation, 6(3), 70-74.
3. De Lucia, C., Pazienza, P., and Bartlett, M. (2020) Does good ESG lead to better financial performances by firms? Machine learning and logistic regression models of public enterprises in Europe. Sustainability, 12(13), 5317.
4. Xu, J., Lu, Z., and Xie, Y. (2021) Loan default prediction of Chinese P2P market: a machine learning methodology. Scientific Reports, 11(1), 18759.
5. Jafari, A., Rouhanizadeh, B., Kermanshachi, S., and Murrieum, M. (2020) Predictive analytics approach to evaluate wage inequality in engineering organizations. Journal of Management in Engineering, 36(6), 04020072.
6. Bhattacharyya, A., Yoon, S., Weidner, T. J., and Hastak, M. (2021) Purdue index for construction analytics: Prediction and forecasting model development. Journal of Management in Engineering, 37(5), 04021052.
7. Quan, T. Z., and Raheem, M. (2023) Human Resource Analytics on Data Science Employment Based on Specialized Skill Sets with Salary Prediction. International Journal of Data Science, 4(1), 40-59.
8. Smaldone, F., Ippolito, A., Lagger, J., and Pellicano, M. (2022) Employability skills: Profiling data scientists in the digital labour market. European Management Journal, 40(5), 671-684.
9. Simsek, S., Albizri, A., Johnson, M., Custis, T., and Weikert, S. (2021) Predictive data analytics for contract renewals: a decision support tool for managerial decision-making. Journal of Enterprise Information Management, 34(2), 718-732.
10. Chen, Y., Leung, C. K., Li, H., Shang, S., Wang, W., and Zheng, Z. (2021). A data science solution for supporting social and economic analysis. In 2021 IEEE 45th Annual Computers, Software, and Applications Conference, 1689-1694.
11. Meyer, M. A. (2019). Healthcare data scientist qualifications, skills, and job focus: a content analysis of job postings. Journal of the American Medical Informatics Association, 26(5), 383-391.
12. Baumeister, F., Barbosa, M. W., and Gomes, R. R. (2020) What Is Required to Be a Data Scientist?: Analyzing Job Descriptions With Centering Resonance Analysis. International Journal of Human Capital and Information Technology Professionals, 11(4), 21-40.
13. Chang, H. C., Wang, C. Y., and Hawamdeh, S. (2019) Emerging trends in data analytics and knowledge management job market: extending KSA framework. Journal of Knowledge Management, 23(4), 664-686.
14. Halwani, M. A., Amirkiaee, S. Y., Evangelopoulos, N., and Prybutok, V. (2022) Job qualifications study for data science and big data professions. Information Technology & People, 35(2), 510-525.
15. Tambe, P., Cappelli, P., and Yakubovich, V. (2019) Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15-42.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-151-3
- ISBN (Online)
- 978-1-83558-152-0
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/52/20230690
- Copyright
- 01 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited