Theory: It is the basis of analysis.

1) basic knowledge of data analysis, at least know how to do trend analysis, comparative analysis and subdivision, otherwise you won't get a data;

2) Basic statistical knowledge, at least understand basic statistics. Knowing the definition and applicable conditions of these statistics, statistical methods can make the analysis process more rigorous and the conclusions more convincing;

3) Interest in data and other knowledge, the more the better, makes the analysis process interesting.

Practice: It can be said that 90% analytical ability is cultivated through practice.

1) defines the purpose of the analysis. If there is no clear ultimate goal of analysis before analysis, it is easy to be bypassed by data, and finally you don't even know what your conclusion is for;

2) Look at the data in combination with the business. The data comes from business operations, and the analysis must of course return to business. Being familiar with the business can make the data look more thorough;

3) Understand the definition and acquisition of data. It's best to start with how the data was originally obtained. Of course, the statistical logic and laws of indicators must be memorized, otherwise it is easy to be deceived by data.

4) Finally, it is a necessary process to constantly look at and analyze the data. Usually, an experienced non-data analysis operator has a much deeper understanding of data than a data analyst who just came in. That's the reason.