What is data mining? What's the difference between data mining and traditional analysis methods?

Datamining (English: data mining) is also translated as data mining and data mining. This is a step of knowledge discovery (KDD) in the database. Data mining generally refers to the process of finding hidden information from a large number of data through algorithms. Data mining is usually related to computer science, and the above goals are achieved through statistics, online analytical processing, information retrieval, machine learning, expert system (relying on past empirical rules), pattern recognition and other methods.

The essential difference between data mining and traditional data analysis (such as query, report and online application analysis) is that data mining is to mine information and discover knowledge without clear assumptions. The information obtained by data mining should have three characteristics: unknown in advance, effective and practical.

For more information about data mining, the course of consulting CDA data analysts is recommended. The course content of CDA data analyst takes into account the cultivation of horizontal ability to solve data mining process problems and vertical ability to solve data mining algorithm problems. Students are required to think from the root of data governance, explore business problems through digital working methods, and then choose business process optimization tools or algorithm tools through proximate cause analysis and macro root cause analysis, instead of "adjusting algorithm packages when encountering problems". Click to make an appointment for a free audition class.