The difference between data analyst and data mining engineer

The position of data analyst focuses on "analysis" and the position of data mining engineer focuses on "mining".

1. Data analyst: based on business, find and analyze business problems through data analysis to support decision-making. Generally, the size of companies that recruit such positions is not too small, and the number of people may not be the only measure, but the business scale is definitely relatively large. On the contrary, companies with too small a business scale have nothing to analyze.

2. This position focuses on "analysis". First of all, you should have certain data sensitivity and mathematical foundation, and know what data indicators you need to look at under what data scale. Knowing the traditional data mining algorithms, we can use some tools to get the expected results. Of course, if you use tools, you need the company system to support some data analysis software, such as SPSS and Clementine. If not, to put it bluntly, getting an Excel spreadsheet is also called a data analyst in some companies. Of course, some data analysts Excel can play very smoothly, and Excel can be used to simulate the iterative process of a CTR prediction algorithm.

3, data mining engineer: partial technology, through the establishment of models, algorithms, predictions, etc. Provide some general solutions, of course, there are also some for a certain business. The focus of the post is "digging", so the requirement for people is to be familiar with the methods and tools of digging, or at least know what tools to use on what platform and how to solve what needs.

4. Simply put, it is responsible for receiving requirements and then outputting the results. Most data mining engineers in the company are passive. For example, Bi came to you and said, "I want the star data of 100, and I want to know when and what kind of films each of them made." At this time, you need to collect, process and output data. There may be some data visualization or algorithm work, but the requirements are not high.

5. Good programming background, suitable for being a data mining engineer. Good at math and business acumen, suitable for being a data analyst.