1, "data analysis" focuses on observing data, and "data mining" focuses on discovering "knowledge rules" KDD (knowledge discovery in database) from data.

2. The conclusion of "data analysis" is the result of human intelligence activities, and the conclusion of "data mining" is the knowledge law discovered by machines from learning sets (or training sets and sample sets).

3. The conclusion of "data analysis" is applied to people's intellectual activities, and the knowledge rules discovered by "data mining" can be directly applied to prediction.

4. "Data analysis" can't establish a mathematical model, so it needs manual modeling, while "data mining" directly completes mathematical modeling. For example, the essence of traditional cybernetic modeling is to describe the functional relationship between input variables and output variables. "Data Mining" can automatically establish the functional relationship between input and output through machine learning. According to the "rules" obtained by KDD, given a set of input parameters, a set of outputs can be obtained.

If you want to know more about the difference between data mining and data analysis, you can consult CDA certification center. CDA industry standards are jointly formulated by industry experts, scholars and well-known enterprises in the international data field, and are revised and updated every year, which ensures the openness, authority and cutting-edge of the standards. Those who pass the CDA certification examination can obtain CDA certification in both Chinese and English.