I suggest you learn some math. Whether it is classification, clustering, regression recommendation, etc. All kinds of algorithms can only be understood after all. With a little math foundation, you can also explain the results confidently. Although many python packages can be ported and the results can be generated, if you are accurate, you still need to def yourself. So if you want to do well in this industry, you can't say that math is too good, but at least it can't be too bad.

The relationship between Python data mining and mathematics is as follows:

1. Data mining is not to replace the traditional statistical analysis technology. On the contrary, it is the extension and expansion of statistical analysis methodology. Most statistical analysis techniques are based on perfect mathematical theory and superb skills, and the accuracy of prediction is still satisfactory, but the requirements for users are very high. With the continuous enhancement of computer's ability, the same function can be accomplished only by relatively simple and fixed methods by using the powerful computing power of computers.

2. Based on the file system: As we all know, the database management system (DBMS) of the database system is established. The problem now is data mining and statistics, and some data mining algorithms are statistical methods. Therefore, when it comes to the computer industry, people will be concerned about the combination (effectiveness) of data mining and a large number of data, and their data mining primitives (data mining languages) and accurate interfaces will only be considered when they are implemented in software. The optimization of algorithm performance leads to the establishment of some standards in data mining industry.

3. Data mining is still a part of machine learning and artificial intelligence, and its core is rules, which are statistical in data mining algorithms, but this technology itself does not belong to statistics. This is a rule that can be obtained by data mining algorithm. Before drawing such rules, the algorithm will analyze the data set, which includes many variables (fields in the database). Suppose there is 10, and "age" and "salary" are two of them. The algorithm will automatically extract these two variables according to historical data, and get such a rule. But for statistics, it is impossible to obtain, only quantitative probability relationship can be obtained, and the deduction of rules should not belong to the category of statistics.

To learn more about Python data mining, you can take a look at the course of CDA Data Analyst. This course not only cultivates students' hard data mining theory and Python data mining algorithm skills, but also cultivates students' soft data governance thinking, and makes project endorsement for you to enter famous enterprises.