1. Descriptive statistical analysis: This is the most basic data analysis method, including calculating statistics such as average, median, mode and standard deviation, so as to understand the basic situation of data.

2. Exploratory data analysis: It shows the distribution and relationship of data intuitively through graphs and charts, and helps researchers find patterns and trends in the data.

3. Correlation analysis: Measure the relationship between two variables by calculating correlation coefficient, such as Pearson correlation coefficient and Spearman rank correlation coefficient.

4. Such as linear regression, multiple regression, etc.

5. Cluster analysis: grouping similar objects together for further analysis and understanding.

6. Factor analysis: Simplify the data by reducing the number of variables, while retaining as much information as possible.

7. Principal component analysis: reduce the dimension of the data by finding the main direction of change in the data.

8. Time series analysis: analyze data that changes with time and predict future trends.

9. Text analysis: processing and analyzing text data, such as word frequency statistics and emotional analysis.

10. Network analysis: modeling and analyzing the network structure, such as social network analysis and complex network analysis.

These are just some common data analysis and processing methods. In fact, there are many other methods and techniques, which need to be selected according to specific research problems and data types.