Research on the Teaching Reform of Probability and Statistics Courses in the Big Data Era

With the rapid penetration of big data technology, the demand for data analysis capabilities in various industries has gradually changed. Probability and statistics serve as the core course for cultivating data analysis thinking, yet the traditional teaching model lags behind in terms of content, methods, and evaluation systems. Therefore, this paper, based on the new requirements for talents' capabilities in the big data era, analyzes the existing problems in the current teaching of probability and statistics courses, such as the over-emphasis on theoretical derivation in content, the lack of interactivity in teaching methods, and the singleness of assessment methods. From three dimensions—updating teaching content, innovating teaching methods, and reconstructing the assessment system—it proposes reform strategies, including integrating big data cases from multiple fields, strengthening the teaching of Python/R tools, promoting the project-driven model, and constructing process-oriented evaluation. These strategies aim to enhance the adaptability of the course to big data applications, cultivate students' core competitiveness in solving practical problems, and provide references for talent cultivation under the background of emerging engineering and emerging liberal arts.