When I saw these comment posts and experience articles, many of them were about how to learn the basic skills of data analysis in the above picture, such as Excel, SQL, Python, etc., which listed a lot of learning methods, platforms, and software. It is true that this is a shortcut to quickly get started with junior Internet data analysts, but it is not recommended to use the fast method on the road of growth of data analysts. I recommend building the underlying logic and knowledge of data analysis. After solid basic skills, you will be able to work in the workplace later. go further.
Data analysts are a field that is easy to enter and difficult to master. For students who want to enter the industry, they think that they only need to know SQL, Excel and basic data analysis methods, but this is not far away; such abilities cannot be called A data analyst can only be a [data specialist] [data operation].
For the high-end positions of data analysis, not only the technology of data analysis is needed, but also the knowledge of business, strategy, finance, operation and other aspects, as well as thinking ability and influence. In the future, data analysis skills will become one of the necessary skills for high-end jobs, and everyone relies on data to make decisions.
Let me talk about how to quickly grow into a qualified data analyst based on my personal growth experience, experience of interviewing hundreds of data analysts, and internal data analyst training courses and foreign university business data analysis courses.
For students who need to take data analysis as a lifelong career in the long run, it is recommended to read all of them, which will be more helpful to you.
Data Analyst job description:
I have sorted out the work content of data analysts in most business directions in China. You can clearly see in the picture that the first four tasks are spent a lot of time by many data analysts, but frankly speaking, the gold content is really low. , highly substitutable, without its own core competitiveness, if you change careers and do these jobs unchanged, needless to say, there will be a crisis at the age of 35, and it may be replaced by more advanced software tools at the age of 30.
For example, maybe you used to be an operator, and you did a good job in your original position, but when your head became hot, you felt that operations had no future, and you wanted to switch to data analysis. Find a training class and start looking for a job after the class. After finding a job, it turns out that what you do every day after work is more like a tool person than what you did when you were doing operations, so you can only fall into a dilemma.
Let me talk about the conclusion first: If you are not from a major or have a great love for data, it is not recommended to transfer to a data analysis position, but you can use data analysis thinking to help your career development.
Why this conclusion?
Because in fact, it can be seen that the importance of data has become increasingly prominent, whether in enterprises or society.
The government promotes the construction of big data, and enterprises strengthen digital transformation. Under this general trend, data analysis thinking is no longer just a "professional" for data analysts. Positions including sales, marketing, operations, planning, and products all need to pass data analysis. To help their own work, and even the financial, legal, and human resources in the background began to need data analysis to improve efficiency.
My suggestion is to go in three steps:
Step 1: Use spare time to learn data analysis thinking and tools;
Step 2: At work, develop your own data thinking and use data analysis tools to exercise;
The third step: output the data analysis conclusions in a valuable way, so that they can really assist their career development.
Always believe that thinking and tools are complementary, thinking>tools
Data analysis is not only a profession, but also a necessary skill for contemporary professionals. To put it bluntly, everyone should be a data analyst. To put it bluntly, in today's workplace with serious introversion, how can you reflect your value if you don't learn some hard skills?
Therefore, in the future development, data analysts will gradually differentiate, and some people (mainly fresh graduates) will carry out data collection, data integration, and understanding of statistical business (data operation). If you perform well in this link and show strong analytical thinking, you can perform data analysis work as part of the business and advance to a data analyst.
The ability of a data analyst should be: starting from big data, realizing a closed loop of problem discovery, analysis, and problem solving, and promoting business growth and efficiency improvement.
Among them, the ability to control data, the thinking habit of analyzing problems, communication skills and expressive ability are required. Among these abilities, the ability to retrieve data is the easiest to be replaced. If you find that 80% of your work is dealing with data, it is difficult for this ability to support the improvement of analytical thinking. It will gradually become a job of counting warehouses (there is no meaning of comparing the quality of the work).
If you have accumulated a certain amount of data analysis thinking in your daily data analysis work, then this is your unique wealth, and your irreplaceability will gradually increase.
Analytical thinking is slowly cultivated in the use of tools
Analytical thinking is not only slowly accumulated in the analysis business scene, but also can use some auxiliary tools to exercise thinking ability. What reflects the thinking ability of a data analyst is his use of analysis methods. Data analysis methods include many .
Most of them lead to the same goal by different routes , and they are all closed loops of [ discovering problems] [analyzing problems] [solving problems ].
It is all about finding causes and producing results. Let’s give an example of [ causal analysis ] method in data analysis :
Just like the data analysis tool exemplified in the first picture - [Guanhe Causality] . This product is to assist data analysts in discovering the causal relationship between data. The causal relationship between things is complicated, and it cannot be completed by relying on analytical tools alone, and it needs to add the user's thinking and judgment.
[Guanhe Causality] can only analyze the association rules between data and display them in the form of a graph structure, but cannot directly conduct causal analysis and draw conclusions.
Therefore, even these software that are very close to human judgment need to combine the analytical thinking of data analysts.
[ Cause and effect analysis ] The use of such methods in business scenarios can exercise the thinking ability of a data analyst, but if you have no thoughts in the early stage and cannot find the causal relationship, you can use [Guanhe causality] to discover association rules The tools assist analysts to discover potential rules in the data, and use these rules to conduct in-depth causal analysis.
In short, there are a hundred Hamlets in the eyes of a hundred people, and a hundred data analysts have a hundred ways to get started quickly, but all changes are inseparable. The above is my humble opinion. I hope it can inspire you, so bookmark If so, give it a thumbs up!