Currently, China's automotive industry is in a critical period of transformation and upgrading, and the demand for high-quality statistical talents is more urgent. Taking this as an opportunity, this article delves into how to tailor the training path and implementation mechanism for a master's degree in statistics for the automotive industry. After a detailed analysis of the supply and demand relationship of master's degree talents in statistics in China, we have gained insight into the core issues in current talent cultivation and provided a solid theoretical foundation for future strategic adjustments. Furthermore, through in-depth field research of relevant enterprises in the automotive industry, we have explored the close relationship between statistical talents and the prosperity of the automotive industry, providing precise guidance for the direction of talent cultivation. Under the comparison and reference from an international perspective, this article proposes a training path optimization strategy that is both in line with China's national conditions and forward-looking, illuminating the path of cultivating a master's degree in statistics in China. Finally, when exploring the implementation mechanism, we carefully analyzed multiple dimensions such as motivation, coordination, performance evaluation, and guarantee, providing practical and operational measures for the training of Master of Statistics. In summary, the research results of this article not only provide strong support for the takeoff of China's automotive industry, but also inject strong impetus into the high-quality development of the automotive industry.

Xuetao Li, Qiong Wang, Chengying Yang, Yonghong Zhang. Research on the Training Path and Implementation Mechanism of Master's Degree in Statistics for the Demand of the Automotive Industry. International Journal of Social Sciences and Economic Management (2024), Vol. 5, Issue 1: 104-113. https://doi.org/10.38007/IJSSEM.2024.050112.

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