Moderator.
Welcome Jirong as an outstanding student of CDA to participate in our interview today, Jirong is now working as a data analyst in a company, can say hello to everyone.

Jirong Zhang.

Hello everyone, I am Zhang Jirong, I graduated from Hebei University of Engineering in 2017, before that I was engaged in sales management, and then I am currently transferred to the position of data analyst.



Question 1 :.

When I saw this information, I thought of the first question, what made you switch from sales to data analysis industry?

Zhang Jirong.

One is because I saw a current windfall. The trend of the digital economy, and many traditional enterprises are doing digital transformation, is a future home direction.

Another is because at that time to do sales management on the time with the team is quite pleased, it is always patting the head to make decisions, or hope that they have some scientific methodology or theoretical system to work, to improve efficiency.

So at that time, after I had the opportunity to contact some data analysts' related knowledge, I felt I was still very interested, and my three years' sales experience made me have a keen sense and analysis of the business logic behind the numbers, so I decided to switch to a specialized data analysis position at that time, hoping to be able to move from a more single point of view of sales, and then to a position with clearer objectives, to a higher I hope I can move from a single point of view of sales and a position with clear objectives to a higher level of analysis from the perspective of company-wide operations, and then to play to my strengths and specialties.



Question 2.

We all know that changing career will encounter great difficulties, can you tell us what is the biggest difficulty you encountered in the process of changing career.



Zhang Jirong.

I think the biggest difficulty is a change in thinking. If people want to break the original work habits and way of thinking, it is actually more difficult.

The job of sales management is a clear target position, only back performance indicators, and then some management actions will follow the feeling to go.

That transferred to the data analysis position, the way of thinking from the original single point to sales performance as the goal, to change to the overall picture of thinking. It is not just a single dimension, and then the data analysis position is not just a single dimension of writing code to handle data.

In fact, we also need to look at the company's global cost rules of some of these difficult operational issues, so if it is still this single-minded analysis, it will be problematic, so this shift from single-minded thinking to multi-minded thinking is still quite challenging.



Question 3.

I understand that you also attended the study in CDA, many students are asking how to apply the theoretical knowledge and data analysis tools learned to work, this experience can tell you a little.



Zhang Jirong.

The theoretical knowledge is actually a preliminary knowledge for me to establish the grid of data analyst.

The general direction is that data needs to be combined with the business is a process of laying the foundation, the actual job to the real business to be more complex, but also continue to learn and supplement knowledge.

Some specific help will be a lot, take two small points for example.

It is the course or theory learned some Pareto analysis, in my current company and learning and at the time of learning, in fact, different scenarios and business, when it was to do some TO C of this analysis, but I am now actually doing TO B, my status quo is to analyze the relationship between sales behavior return visits and renewal rates, but the logic is the same, although the business is different. This kind of if I did not have the theoretical foundation at the time, to go to encounter this kind of problems in practice, I was not able to start.

The second is that when I was studying the analysis report, the teacher talked about a business process and the structure according to the total division, which is also very helpful for my thinking and inspiration in the work. I am grateful to CDA for the theoretical knowledge and help in this study.



Question 4.

As a practitioner who has successfully switched to data analysis, what career advice and experience do you have to share with students?



Zhang Jirong.

I actually saw a lot of people who want to transform, but can not make up their mind, so they have been dragging on not satisfied with the status quo, and then but it is difficult to change, so it has been in the current position to continue to swing.

How about a successful transition? In fact, the decision to transform and do data analysis process is the same. I first go to research, looking around a variety of channels, you can get from friends, all kinds of websites, Zhihu, Shake various channels to understand what you want to transform the post is, what it is like, and then what the job content is, and then analyze whether they are interested and good at it.

If you determine the goal of transformation, do not worry about whether you can learn, and then whether you can stick to it so that there is just some worry, and then I think the process of reaching the goal is a process of overcoming difficulties.

When I was learning SQL and Python, I was struggling to learn because I had no programming foundation, so I would refuse all socializing and recreational activities on my days off to brush up on my code all day.

Some time ago when I started, I saw MySQL, the database I run one by one will report an error, it is still quite upset, but also did not give up, and told myself that others learn 1 time that I will learn 10 times, others learn 10 times I will learn 100 times, that is, again stupid can learn.

So that time is quite dark, when you find yourself more and more skilled, at that time the sense of achievement is bursting, on the basic statements of some code now there is no problem, but also to tutor colleagues around.

I think there is persistence, perseverance, and determination to overcome difficulties in order to transition to success, otherwise it is based on your ability to match your ambition, so if you really want to transition, move first.



Question 5.

Just mentioned that the transformation is to first convince themselves, in fact, there are many people around me are saying I want to learn data analysis, but most people are just talking about it, no real determination, you are the first to convince themselves, when they have made up their minds to set the goal, ready for the psychological preparation, and then?



Zhang Jirong.

Determine the goal, and then after the psychological preparation is the specific method.

I learned that there are two kinds, one is self-study, self-study for self-learning ability and self-discipline of students is OK. Then another is to enroll in a class, I was thinking that the efficiency of enrolling in a class will be higher.

CDA did give me a lot of help, because it is not like other institutions that pay more attention to the use of such tools, it is still very important for me to develop the thinking of data analysis, so this is also to let me work later on some time, and then also in the use of the course of some of the way of thinking, it is still very helpful to me.

Moderator: Thanks for the recognition, I think data thinking is a soul of data analysis.

Zhang Jirong: Yes, and I think the employment teacher is an extra surprise, including after I started, the employment teacher will still go to teach me some work-related things.

Because I was doing sales management before, and then the team atmosphere will be relatively simple, and now some functional positions in many things will be a little bit complicated, so sometimes will still talk with the employment teacher, and then he will also go to answer my questions, like a friend, so I think this is an additional value, it is quite good.



Question 6.

We shared our experience of changing careers before, now can you share your experience in the actual work process?



Zhang Jirong.

This problem is still quite a lot, but in fact, this position is the need for continuous progress. Can not solve is to learn how to solve.

For example, SQL code, you will find that there will always be more professional number warehouse engineers write better than you, you use 100 lines of code to write out a table, the number warehouse engineers with 50 implementation to write out, the result is actually the same. So that this position is the need to constantly optimize, and then constantly to optimize your code, optimize your work, optimize your methodology. Then learn to update the content of this position to continue to do things, so to continue to learn.

The second is that data can be deceptive, on the indicator caliber is not the same, out of the results is a huge difference, for example, we have done before sales return and renewal rate of the relationship between such special. If we were to use the quantity dimension to look at the frequency of return visits, the conclusion would be that the higher the number of return visits, the higher the number of renewal rates.

But if you look at the renewal amount dimension, it is just the opposite, the results are actually strongly related to your company's business, and the conclusions drawn from the different indicators are completely different, so it is very important to talk to your demand side about the caliber of its indicators so as not to influence the decision.

Remember not to start working after you receive the requirements, you must talk to the demand side clearly about the purpose of the requirements and the caliber of the indicators. If you encounter requirements that you think are unreasonable, you can put forward your own views and give the demand side with professional advice.



Moderator.

Thank you Jirong for taking the precious time off to join our interview. I believe that Jirong's transformation experience can inspire many partners to make up their mind to successfully transform into data analysts.

Data, as a new production factor, has been integrated into all aspects of social management, including the way of production, lifestyle and social governance.

That's it for our interview today, bye.