Digital-intelligent technologies have been widely applied in ecological environment monitoring and governance, putting forward new requirements for the data analysis ability of environmental engineering professionals. The course Environmental Information and Statistical Analysis serves as an important carrier to cultivate students’ capabilities in acquiring, processing, and analyzing environmental information. However, practical teaching still has prominent problems including weak connection of knowledge modules, insufficient practical training, loose combination between teaching contents and actual environmental issues, and evaluation methods that fail to reflect students’ data analysis proficiency. Centering on the talent training demands of environmental engineering, this paper systematically optimizes training objectives, teaching contents, practical tasks, and evaluation modes with the whole process of environmental data analysis as the main line. A teaching model driven by real environmental issues and centered on cultivating data analysis ability is constructed. During course implementation, remote sensing, geographic information system (GIS), and environmental statistical analysis are organically integrated to guide students to complete the full workflow of data acquisition, data processing, spatial analysis, statistical interpretation, and engineering decision-making, and standardize the application of artificial intelligence-assisted learning. Based on score analysis of 193 students from the academic years 2023 to 2025, the results show that the average total course score reaches 86.19 points, the average usual score is 91.05 points, and the average final exam score is 81.33 points. Students show high learning participation, yet there is still room for improvement in their ability to comprehensively analyze and independently solve environmental problems. Practices prove that course reform shall further strengthen the in-depth integration of professional knowledge, data analysis methods, and engineering practice. Through case-driven teaching, practical training and multi-dimensional evaluation, students’ data analysis and engineering application capabilities can be effectively fostered.