Data analysis and data mining have different thinking modes. Generally speaking, data analysis is based on objective data for continuous verification and hypothesis, while data mining has no hypothesis, but you should also give your judgment standard according to the output of the model.
When we often do analysis, data analysis needs more thinking, and more structured and MECE thinking methods are used, similar to the assumptions in the program.
Analytical framework (hypothesis)+objective question (data analysis) = conclusion (subjective judgment)
However, data mining is mostly large, comprehensive, multifaceted and sophisticated. The more data, the more accurate the model, the more variables and the clearer the relationship between the data.
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Data analysis depends more on business knowledge, while data mining focuses more on the realization of technology, and the requirements for business are slightly reduced. Data mining often requires a larger amount of data, and the greater the amount of data, the higher the requirements for technology. Strong programming ability, mathematical ability and machine learning ability are required. From the results, data analysis focuses more on the presentation of results and needs to be interpreted in combination with business knowledge. The result of data mining is a model, through which we can analyze the law of the whole data and realize the prediction of the future at one time, such as judging the characteristics of users and what kind of marketing activities users are suitable for. Obviously, data mining goes deeper than data analysis. Data analysis is a tool to transform data into information, while data mining is a tool to transform information into cognition.