1, bachelor degree or above in computer, statistics, mathematics and other related majors;
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；;
3. More than three years working experience in mass data mining and analysis related projects, and participated in relatively complete data collection, collation, analysis and modeling;
4. Sensitive to business and business logic, familiar with the background of traditional industry data mining, understanding the market characteristics and user needs, background in Internet-related industries, and experience in website user behavior research and text mining are preferred;
5. Have good logical analysis skills, organizational communication skills and team spirit;
6. Innovative, enthusiastic and willing to accept challenges.
1, rigorous and responsible attitude
Rigorous and responsible is one of the essential qualities of data analysts. 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.
2. Strong curiosity
Everyone has curiosity, 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.
3. Clear logical thinking
In addition to a curiosity to explore the truth, data analysts also need to have meticulous thinking and clear logical reasoning ability. 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.
Usually, the business problems we face when we are engaged 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.
4. Be good at imitation
When doing data analysis, it is important to have your own ideas, but it is also very necessary to learn from the past, 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".
5. Be brave in innovation
We can learn from other people's successful experience through imitation, but the imitation time should not be too long. It is suggested that after each imitation, we should sum up and propose improvements and even innovations. 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.