Data mining is the most important thing is logical thinking, math a lot of nature is not important, that is, to extract information, and data to do to find the law, generalization or calculation can be. With math scores good or bad, can solve simple mathematical calculations, and have good thinking skills can be.

Data is essentially a collection of massive amounts of data, and data is always inseparable from numbers and math. As a person with poor math foundation, it is normal to worry that poor math will encounter more difficulties in learning big data. But in fact, learning big data, do not have to bother about whether the math is good or not, good math can play a certain role in helping, but not bad math is not good to learn big data. If you have to be entangled in the mathematical foundation for the study of big data to help, the following knowledge of these related mathematical disciplines in big data will have a certain usefulness, you can target to understand and master these knowledge 1 probability theory and mathematical statistics 2 discrete mathematics 3 linear algebra 4 optimization methods

Want to know more about data mining, recommended on the CDA data analyst course. The course content balances the development of horizontal ability to solve data mining process problems and vertical ability to solve data mining algorithmic problems. Students are required to have the thinking from the root of data governance, through digital work methods to explore business issues, through proximate cause analysis, macro root cause analysis and other means, and then choose business process optimization tools or algorithmic tools, rather than "encountering problems with the algorithm package". Real understanding of business thinking, project thinking, to be able to meet the problem to solve the problem. Click to book a free trial lesson

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