Briefly describe the difference between data mining and traditional analysis methods.

The biggest difference between data mining and traditional analysis methods lies in the requirement of computer programming ability.

In many cases, as data analysis, you need to use molding analysis tools, such as EXCEL, SPSS, or SAS, R. A person who knows nothing about programming and coding can become a good data analyst, because in general, several tools included in OFFICE can meet most data analysis requirements.

Data mining needs a programming foundation. First, most of the current data mining and related graduate students belong to the computer department; Second, in the recruitment position, the job title of large domestic companies is mostly "data mining engineer". In terms of understanding the industry, data analysts should have a deep understanding of the industry they are engaged in and be able to closely combine data with their own business. For example, if I give you a business report, you can draw a picture of the current business situation in your mind and see where there are problems. But engaged in data mining, the requirements for the industry are not necessarily so high. Professional knowledge requires data analysts to pay more attention to the business level, while data mining engineers pay more attention to the technical level.

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