1. data cleaning: this is the first step of data processing, mainly to remove noise and abnormal values in data, including missing values, duplicate values and erroneous values.

2. Data conversion: the original data is converted into a format suitable for analysis, such as digitization, standardization and normalization.

3. Data integration: Data from different sources are integrated to form a unified data set. This may involve data selection, merging, transformation and other operations.

4. Data dimension reduction: By reducing the dimension of data, the efficiency of data analysis is improved. The common dimensionality reduction methods are principal component analysis (PCA) and linear discriminant analysis (LDA).

5. Data sampling: When the amount of data is too large, some data can be selected for analysis through sampling. Sampling methods include random sampling, stratified sampling and cluster sampling.

6. Data modeling: According to the research objectives, select the appropriate model to analyze the data. Common data modeling methods include regression analysis, cluster analysis and association rule analysis.

7. Data visualization: Visualize the results of data analysis through charts and other forms to help understand and interpret the data.

8. Data verification: verify the processed data to ensure its accuracy and reliability.

The above are common raw data processing methods, and the specific processing methods need to be determined according to the characteristics of the data and the research objectives.