Requirements of data analyst: \ x0d \ x0d \ 1, bachelor degree or above in computer, statistics, mathematics and other related majors; \x0d\ 2。 Have profound knowledge of statistics and data mining, be familiar with related technologies of data warehouse and data mining, and be able to skillfully use SQL；; \x0d\ 3。 At least three years working experience in mass data mining and analysis related projects, and participated in relatively complete data collection, collation, analysis and modeling; \x0d\ 4。 Sensitive to business and business logic, familiar with traditional industry data mining background, understanding market characteristics and user needs, background in Internet-related industries, and experience in website user behavior research and text mining are preferred; \x0d\ 5。 Good logical analysis skills, organizational communication skills and team spirit; \x0d\ 6。 Innovative, enthusiastic and willing to accept challenges. \x0d\x0d\ 1, rigorous and responsible attitude \ x0d \ rigorous and responsible attitude is one of the essential qualities of a data analyst. Only with a rigorous and responsible attitude can the data be objective and accurate. In the enterprise, the data analyst can be said to be the doctor of the enterprise. They find the crux and problems for enterprises through the analysis of enterprise operation data. A qualified data analyst should have a rigorous and responsible attitude, maintain a neutral position, objectively evaluate the problems existing in the process of enterprise development, and provide effective reference for decision makers; We should not be influenced by other factors to change the data and conceal the problems existing in the enterprise, which is very unfavorable to the development of the enterprise and may even cause serious consequences. And for the data analyst himself, the future is ruined, and the data analysis results made from now on will be questioned, because you are no longer a trustworthy person and have lost trust in front of colleagues, leaders and customers. Therefore, as a data analyst, we must hold a rigorous and responsible attitude, which is also the most basic professional ethics. \x0d\\x0d\ 2。 Everyone has a strong curiosity \x0d\ but as a data analyst, this curiosity should be stronger, and we should actively discover and dig the truth hidden in the data. In the minds of data analysts, there should be countless "why", why it is such a result, why it is not such a result, what is the reason for this result, why the result is not as expected, and so on. This series of questions should be put forward in data analysis, and through data analysis, give yourself a satisfactory answer. The better the data analyst, the harder it is to satisfy his curiosity. After answering a question, he will throw a new question and continue his research. Only in this spirit can we be sensitive to the data and conclusions, and then follow the traces to discover the truth behind the data. \x0d\\x0d\ 3。 Clear logical thinking \x0d\ Data analysts need meticulous thinking and clear logical reasoning ability in addition to their curiosity to explore the truth. I remember a master said: structure is king. What is structure? Structure is what we often call logic. No matter what you say or write, you must be organized and purposeful, and you can't grab your eyebrows and beard, regardless of priorities. \x0d\ Usually, the business problems we face when we engage in data analysis are complex. We should consider the complicated causes, analyze all kinds of complicated environmental factors we are facing, and choose the best direction among several development possibilities. This requires us to have enough knowledge of the facts, and at the same time, we need to really sort out the overall and local structures of the problem, and after in-depth thinking, sort out the logical relationship between the structures. Only in this way can we truly find the answers to business problems objectively and scientifically. \x0d\\x0d\ 4。 Be good at imitating \x0d\ When doing data analysis, it is important to have your own ideas, but you should also learn from past experience, which can help data analysts grow rapidly. Therefore, imitation is an effective way to improve academic performance quickly. Imitation here mainly refers to other people's excellent analytical ideas and methods, rather than directly "copying". Successful imitation needs to understand the essence of other people's methods, understand their analytical principles and touch the essence through the surface. We should be good at turning these essences into our own knowledge, otherwise we can only "imitate all the time and never surpass them". \x0d\ x0d \ 5。 Be brave in innovation \ x0d \ You can learn from other people's successful experiences through imitation, but the imitation time should not be too long, and it is suggested to sum up after each imitation and put forward improvement or even innovation. Innovation is the spirit that an excellent data analyst should possess. Only by continuous innovation can we improve our analytical level, let ourselves analyze problems from a higher angle, and bring more value to the whole research field and even society. The current analytical methods and research topics are ever-changing, and it is impossible to solve new problems well by sticking to the rules. \x0d\\x0d\ Skill requirements: \x0d\\x0d\ 1, understand business. \x0d\ The premise of data analysis is to understand the business, that is, to be familiar with the industry knowledge, the company's business and processes, and it is best to have your own unique opinions. If it is divorced from the industry cognition and the company's business background, the analysis result will only be an off-line kite with little use value. \x0d\2。 Understand management. \x0d\ On the one hand, it is the requirement of building a data analysis framework. For example, to determine the analytical thinking, we need to use theoretical knowledge such as marketing and management to guide. If you are not familiar with management theory, it is difficult to build a data analysis framework, and subsequent data analysis is also difficult. On the other hand, the function is to put forward guiding analysis suggestions for the conclusion of data analysis. \x0d\3。 Understand analysis. \x0d\ refers to mastering the basic principles of data analysis and some effective data analysis methods, and flexibly applying them to practical work, so as to effectively analyze data. The basic analysis methods are: comparative analysis, grouping analysis, cross analysis, structural analysis, funnel diagram analysis, comprehensive evaluation analysis, factor analysis, matrix correlation analysis and so on. Advanced analysis methods include: correlation analysis, regression analysis, cluster analysis, discriminant analysis, principal component analysis, factor analysis, correspondence analysis, time series and so on. \x0d\4。 Understand the tools. \x0d\ refers to mastering common tools related to data analysis. Data analysis method is a theory, and data analysis tool is a tool to realize the theory of data analysis method. Faced with more and more huge data, we can't rely on calculators for analysis, but must rely on powerful data analysis tools to help us complete data analysis. \x0d\5。 Know how to design. \x0d\ Understanding design is to effectively express the analysis views of data analysts with charts and make the analysis results clear at a glance. The design of charts is a big problem, such as the choice of graphics, layout design, color matching and so on. These all need to master certain design principles.