Entry level data analyst, what skills should I master

Many students are confused: they want to be a data analyst. As a result, they learn a lot of ESP software operation, read a lot of statistics, machine learning books and run a lot of data sets. As a result, they get data every day after they enter the job – and they are still very basic data. In essence, the problem comes from the following: the description of data analysis on the Internet is too idealistic, which abstracts the work that needs comprehensive skills into a series of simple operations. < / P > < p > thus creating an illusion – as long as I copy the code of the case, I can do a few SQL problems, and I will input the model code into sklearn to run it once, which is data analysis. In fact, as a job, data analysis needs to work in specific enterprises, face specific business problems, deal with specific system conditions, and deal with various colleagues, which requires more than basic operations. < / P > < p > if you are invited to come in and not do dirty work, why should the old birds do it? Originally, with the ideal of “Data-Driven business” and “becoming a data scientist”, the huge psychological gap must be unacceptable to new people. < / P > < p > if you don’t understand the business, you can’t make a fart by analyzing it. However, the meaning of the business itself is very wide. It can be divided into five parts: business model, organizational structure, business process, business strategy and implementation. It is unrealistic to expect new people to understand all of them at one time. How many departments do we have? Which department / group do I connect with at present? What are they most concerned about? What are they doing recently? Where are the data they need? These five questions are very simple. If you take a look at OA, you can understand them by chatting with the receiver when you need data. < / P > < p > first of all, no enterprise is so strict as to write all the business processes into SOP, so if you want to truly understand the business, you must communicate specifically; if you want to make in-depth analysis and influence the decision-making, you should start with a good relationship with the business, and communication is essential at ordinary times. The indicators and judgment criteria often concerned by the business can be understood. This is the biggest difference between doing work and learning textbooks. In reality, no one is ready to feed things into their mouths, so they have to do it by themselves. < / P > < p > there is no standard, which means there is no analysis conclusion; the standard is fuzzy and changeable, which means that the judgment of right and wrong will be completely reversed, the analysis experience can not be accumulated, the model can not standard positive and negative samples, let alone the training model; if you want to do in-depth analysis, you will have no way to start. Because the standard problem is often ignored, even many old people who have worked for five years always say “this habit is good”. As for what the habit is, it is not clear at all. < / P > < p > even a lot of online teaching data analysis courses teach: “falling is not good, rising is good, falling must be high!” I wonder if these teachers have ever sat in the office of a serious company. < / P > < p > when the standard involves two evaluation dimensions, it is necessary to master two cross evaluation methods; as for the evaluation of three or more dimensions, it is no longer a requirement of entry level, but more complex dimension reduction methods or comprehensive evaluation methods are needed. < / P > < p > if you have the ability to find standards, you can take the initiative when communicating with the business, and reflect your professional ability; in this way, you can identify whether the business is fishing in troubled waters, muddling around, hiding from the sky and stealing the bell; only in this way can we accumulate analysis experience, thus laying the groundwork for in-depth analysis. < / P > < p > data analysis is only an auxiliary department, which needs to be able to come to work, carry the banner and set up projects, so it is easy to see the credit; therefore, the core work ability of data analysts is how to save up independent projects, but it is too far away for newcomers to do independent projects. < / P > < p > it is a proof of workload; it can prove that you are really working, and can be used as evidence when writing year-end summary, progress report and promotion report. It is a standard data retrieval template. It can greatly avoid mistakes, confusion and repetitive work caused by random counting by business parties, so as to reduce meaningless overtime and unprovoked black pot. It can record the problems interested by the business side and pave the way for future projects. This is also very easy to ignore the new link; because all online classes, textbooks, articles will not mention this stubble! < / P > < p > causes the newcomers to mistakenly think that there are unified standards for data analysis work all over the world; as a result, they fail to confirm the demand with the business, and they are exhausted and not flattered; even the basic running number work is not well organized, and what projects they want is a dream. < / P > < p > data analysis is not a high paid and quick work; the dirty work of data analysis is far more than “thinking” and “model”; data analysis is auxiliary, and no one pays attention to you; it is common that data analysis is chased by business; the “algorithm” and programming code in business are two things. Congratulations, you have successfully come to the real world from the Internet. This is the real working environment. If you can face the reality, you have passed the first level of psychological construction. < / P > < p > it’s normal that many once fanatical newcomers will be scared off and will change to other jobs; because in essence, there are many people who hold the idea of “getting promoted and getting rich”, and there are few who hold the idea that “I just love data analysis, and I can stick to it even if my monthly salary is 1500”. < / P > < p > data analysis ability can also help you to do better in other positions. Students who want to continue to do, they will seriously hone their skills and continue to improve. < / P > < p > be able to introduce your work calmly without complaining or getting rich overnight. Can promote the business to propose standard demand list, and provide data accurately and timely according to the demand list. When reporting work in the middle / end of the year, he clearly stated that he had made 240 demands, of which the largest was operation demand, and 70% was activity demand. He proposed five suggestions to upgrade the temporary demand to Bi, and promoted the product online. Based on the demand of 240 copies, we can find that the overall working condition of this year’s operation is good / bad, there are 20 bad scenarios, and the impact on the indicators is XXX. Based on the above, the deep-seated problem may be XXX. If you can clearly explain the above four points in front of the mirror, you will be considered as a complete beginner and have great potential for advanced development. < / P > < p > this is the core symbol of becoming an intermediate data analyst. If you are interested and pay attention to Mr. Chen, we will continue to share the advanced skill requirements. Please look forward to it. Privacy Policy

Author: zmhuaxia