Research on Reconstruction of Practical Teaching System Based on Employment Quality under the Background of Big Data—A Case of Our School's Applied Statistics Major

Abstract

In the current statistical teaching system in China, there are disadvantages such as the disconnection between the teaching content and the era of big data, the lack of statistical practice courses, limited practical teaching resources, single practice teaching methods, and weak practice teaching. Starting from the practical ability required by applied statistics professionals in the era of big data, the employment quality of the 375 statistics majors who graduated from our school in 2012-2019 and its impact on the faculty structure, curriculum structure, and practical teaching of the statistics department during school investigate and analyze attitudes in other areas. First of all, using AHP-BP neural network method (AHP-BP) to establish a set of college students employment quality evaluation model, the maximum relative error is 0.00116. Then, according to the evaluation value of employment quality, the graduates are divided into high employment quality graduates and low employment quality graduates. Through comparative analysis, it is found that graduates with high employment quality are significantly less satisfied with the teaching practice courses offered during the school period than those with low employment quality (x2=35.032, p = 0.000). The graduates with high employment quality think that the college needs to improve in terms of internship, faculty, teaching facilities, teaching materials, professional course content and arrangements, which is significantly higher than the low employment quality. Finally, three specific measures for restructuring the practical teaching system of applied statistics are proposed.