Data Science Talents Mining from Online Recruitment Market in China Based on Data Mining Technique
Keywords:data science, recruitment websites, Web Scrapping, grab data, demand analysis
As the country implements the big data strategy and accelerates the construction of a digital China, data science has entered a new and dynamic era, and the demand for data science talents in all walks of life is increasing. Many talent training departments have added undergraduates or degrees to data science talents, but it is still unclear whether they can meet social and economic development needs. This article aims to improve the quality and adaptability of data science talent training and conduct an in-depth analysis of the demand for data science talents. The technology used in this article is data mining technology. The data information of data science talents is crawled out of the demand information of data science talents on the recruitment website. The core content of network relationship visualization is proposed and analyzed through machine learning methods and text subject word extraction models. Achieve a comprehensive exploration of the demand for data science talents and provide a reference for talent training units to formulate data science talent training models.
De Gagne, J. C., Cho, E., Yamane, S. S., Jin, H., Nam, J. D., & Jung, D. (2021). Analysis of cyber incivility in posts by health professions students: descriptive twitter data mining study. JMIR Medical Education, 7(2), e28805. https://doi.org/10.2196/28805
Gao, L. (2015). Analysis of employment data mining for university student based on WEKA platform. Journal of Applied Science and Engineering Innovation, 2(4), 130-133
Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., ... & Kumar, V. (2017). Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on Knowledge and Data Engineering, 29(10), 2318-2331. https://doi.org/10.1109/TKDE.2017.2720168
Mabić, M., Dedić, F., Bijedić, N., & Gašpar, D. (2017). Data mining and curriculum development in higher education. In International Conference on Information Technology and Development of Education–ITRO. Pp. 1-6.