Exploration and Construction of Professional Curriculum System in the Field of Intelligent Manufacturing Based on Data Analysis

Authors

  • Jun Pan
  • Qiang Gao
  • Wei Liu
  • Wenli Gao

DOI:

https://doi.org/10.54097/5t4yfd36

Keywords:

intelligent manufacturing, python, data analysis, professional courses system.

Abstract

Intelligent manufacturing is an important means of developing strategic emerging industries and accelerating the formation of a modern industrial system. Currently, many universities are upgrading their related majors or offering new majors such as Intelligent Manufacturing Engineering to provide talent for the field of intelligent manufacturing. Due to the lack of integrated resources between industry and education in the field of intelligent manufacturing, it is difficult for universities to strengthen the correlation between the professional curriculum system and the industry when opening new majors or upgrading majors. With the integration of new generation information technology and manufacturing technology, the country plans to transform traditional enterprises into digital, information-based, and intelligent enterprises, and cultivate innovative, technical, and skilled talents that adapt to the development of intelligent manufacturing. It has become a new development strategy given to vocational colleges by the times. This article first obtains information on professional courses related to intelligent manufacturing and artificial intelligence offered in the region, and then uses Python data analysis technology to conduct statistical analysis on the collected professional courses. The analysis results provide reference and inspiration for universities to offer majors related to intelligent manufacturing.

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References

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Published

28-09-2024

How to Cite

Pan, J., Gao, Q., Liu, W., & Gao, W. (2024). Exploration and Construction of Professional Curriculum System in the Field of Intelligent Manufacturing Based on Data Analysis. Journal of Education, Humanities and Social Sciences, 38, 306-312. https://doi.org/10.54097/5t4yfd36