Researched on Improving the Adaptability of Applied Talents in Terms of Integrated Circuit Industry Based on Data Mining
- DOI
- 10.2991/978-94-6463-010-7_111How to use a DOI?
- Keywords
- Applied Talents; Integrated Circuit; Industry; the Age of Big Data
- Abstract
In the age of big data talents of integrated circuit have to face to higher requirements from industry. The education goal of integrated circuit talents should reform relevantly. By data mining, this article deeply analyzes the law of talent adaptability under the influence of big data through discussing the development of integrated circuit technology at same time, and provides some solutions to improve the adaptability of applied talents in the integrated circuit industry. The counterplan includes the following aspect: to construct more cooperation mechanism between colleges and enterprises, to consummate curriculum system, to strengthen internship teaching, to deepen the cultivation of engineering practice abilities. All these methods can help the resolve of talents requirement in integrated circuit to meet the requirement of big data. Data mining would help administrators make scientific decision so as to provide targeted guidance.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Wenhua Liu PY - 2022 DA - 2022/12/02 TI - Researched on Improving the Adaptability of Applied Talents in Terms of Integrated Circuit Industry Based on Data Mining BT - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022) PB - Atlantis Press SP - 1091 EP - 1099 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-010-7_111 DO - 10.2991/978-94-6463-010-7_111 ID - Liu2022 ER -