Many students are confused: they want to be a data analyst, but they learn a lot of ESP software operations, read a lot of statistics and machine learning books, and run a lot of data sets. very basic data.
So is it a beginner? What is the level of a real data analyst? Explain the system today.
In essence, the problem comes from: the description of data analysis on the Internet is too ideal, and the work that originally required comprehensive skills is abstracted into a series of simple operations.
This creates an illusion - as long as I copy the code for the case, I will do a few SQL questions, and I will input the model code into Sklearn and run it again, even if it is data analysis.
But in fact, as a job, data analysis needs to work in a specific enterprise, face specific business problems, deal with specific system conditions, and deal with various colleagues, which requires far more than basic operations (as shown in the figure below).
What's more, as a new recruit, the most they do is running, which is dirty work.
Hiring you in and not doing the dirty work, why should the old birds do it? Originally holding the ideal of "data-driven business" and "becoming a data mobile number list scientist", if you can sweep the floor, wipe the table and empty the urine can, the huge psychological gap will definitely make newcomers. Can't accept it.
The only problem is: how to accumulate four skills in the boring basic work, and make yourself stand out as soon as possible.
If you don't understand the business, you can't analyze it, but the business itself has a very broad meaning, which is divided into five parts: business model, organizational structure, business process, business strategy, and implementation. It is unrealistic to expect newcomers to understand all of them at one time.