Preliminary analysis of data science talents in China’s online recruitment market using Web scrapping tool

Yang, Shiwei

QR Code Link :
Type :	article
Subject :	T Technology (General)
Main Author :	Yang, Shiwei
Additional Authors :	Ashardi Abas
Title :	Preliminary analysis of data science talents in China’s online recruitment market using Web scrapping tool

Place of Production :	Tanjong Malim
Publisher :	Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :	2021
Corporate Name :	Universiti Pendidikan Sultan Idris

Abstract : Universiti Pendidikan Sultan Idris

China implements a big data strategy, accelerates the construction of a digital China, and data science has entered a new and dynamic era. There is an increasing demand for data science talents from all walks of life. The main purpose is to obtain the market demand for data science talents, understand the demand for the data science talent market, and analyze the demand for data science talents. The research method uses Web Scrapping Tool to grab data science talent demand information from major domestic recruitment websites and then obtain data science talent demand through information analysis. Major findings preliminary assessed the data talent market in China from the aspects of geographic demand for data talents, company size, industry demand, and salary. Future research can use crawling information to join machine learning algorithms, in-depth study of the internal connection of China's data science talent needs, and provide suitable training programs for universities.

References

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.

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to previous page