1. Concepts of big data analysis of agricultural carbon emissions: Students will learn the applicable problems of big data analysis and related basic knowledge. 2. Carbon emission data collection and cleaning: Students will learn how to collect large amounts of data from different sources and learn data cleaning techniques, including handling missing values, handling outliers, etc. 3. Carbon data exploration and visualization: Students will learn how to use statistical methods and data visualization tools to explore the structure, correlation and trends of data. Students will learn to use charts, graphs, and statistical indicators to present data in order to better understand and convey the information of the data. 4. Carbon data modeling and prediction: Students will learn how to perform carbon calculations, data modeling and predictive analysis, and will learn to select appropriate models, train and evaluate models, and use models for carbon data prediction and trend forecasting. 5. Carbon reduction data analysis tools: Students will learn to identify carbon emission hotspots and learn how to conduct carbon data analysis in these tools to accurately calculate carbon dioxide equivalent tons and economic benefits. 6. Carbon inventory practice cases, special projects, and low-carbon diagnosis.