The following content is the experience shared by students on Zhihu:
I just finished 3 rounds of interviews with a company this week and got an offer for a data analysis post. Although the position has not changed, I got an offer on the premise of a one-year gap year and cross-bank job hunting.
From this perspective, I would like to share some personal opinions, hoping to help students who want to develop in the direction of data analysis.
1. Your future development direction / your personal career plan / your understanding of the work of data analysis...
*These are questions that are often asked during interviews. The essence is that the recruiter cares about whether your positioning is consistent with the position they are recruiting for, and it is also the future career path that you need to clarify.
The direction can be roughly divided into two categories: technology or business .
(For this, you can first go to the major recruitment platforms to find JDs in various data analysis-related positions, and compare the commonalities and specific requirements, and you will have more substantial feelings.)
1) In terms of technical direction, take mining, algorithm modeling, data scientists, etc.
There will be very high requirements for academic qualifications, majors, programming, statistics, machine learning algorithm models, etc.
Therefore, I, who was not a major, would not apply for positions in this area. (But those who are interested can study and study by themselves and do project practice, which is also a bonus item on the resume)
2) There are many business directions, including financial analysis, sales, supply chain, operations, products, digital transformation, etc.
Generally, it is required to have a deeper understanding and analysis of related businesses, and be able to communicate and express logically and clearly. It is best to have relevant industry experience when applying for a job, or experience in implementing successful projects.
What if there is no relevant experience? Then sort out your previous work experience/project experience during study as a frame, and you must understand the underlying business logic of each of your work links.
Although the industries are different, from a business point of view, the underlying logic is common and can be used for reference. If you can clearly explain the logic of the business model and the ideas and steps of the project, then you have demonstrated some of the characteristics you should have as a data analyst.
2. Tools for data analysis (that is, to examine the mastery of the tools during the interview)
The more common ones, from easy to difficult: Excel, SQL, Python.
Here is the recommended course of Mr. Monkey (WeChat public account search "monkey data analysis" to learn for yourself)
In addition, the more common ones are: R and other commonly used software for statistical analysis, BI and other software for Dashboard.
If you look at the requirements of JD more, you will gradually understand, and each direction has more personalized requirements.
3. Books on data analysis (partial business class, examine the understanding of business during the interview)
There are a lot of books, read more, think more, understand more, practice more. Personally recommend 2 books:
"Data Analysis Thinking: Analytical Methods and Business Knowledge": There are many introductions to business models and index systems in various industries. I helped a lot when I faced that cross-industry position and strongly recommended it.
"Business and Economic Statistics": Strongly recommended, the theory of statistical probability is solid, and there are many practical business cases. It is best to watch the Chinese and English versions together .
4. Personal face-to-face experience (take the cross-bank offer as an example)
0) Major premise: During the epidemic, on-site interviews are not available, so phone interviews and video interviews are the main ones. Because there are display projects with codes attached to the personal resume, there are no links such as handwritten SQL questions. But it is still recommended to read more SQL interview questions and consolidate the SQL foundation, which is very useful for interviews and future work.
1) HR phone interview: Find out whether the information on the resume is true, pay more attention to what you did during the gap period such as the gap year, and dig deeper into the reasons for leaving/changing jobs. Initially introduce the content of the recruitment position, and both parties have the intention to make an appointment with the business side.
2) The video side of the business:
① Ask carefully about the mastery of data analysis tools, mainly to see if they can complete the job of the recruiting position.
② Self-report the work content and project content on the resume. It is about the content of the work, and the essence is to show the personal understanding of the business, the understanding of the underlying logic of the business, and whether you have the ability to complete the project independently.
③The level of understanding of the recruiter's industry and whether other requirements after arrival can match. The main thing is to confirm whether there is an idea for developing in a new industry, and whether it can quickly keep up with the pace after crossing industries.
3) The video side of the business:
About the same as one side.
4) HR communicates salary and benefits and entry time by phone, and sends offer.
5. Some personal thoughts
Data analysis is a relatively popular position, but it is still a relatively basic functional position. Compared with business positions that can directly bring profits, the work pressure may be less, but it will rarely become a core pillar position of a company, unless It is Party B's company mainly engaged in data-related businesses.
The future development trend may gradually become the basic literacy requirements for migrant workers such as English and office tools.
There are still many years to go before retirement in the workplace. Along the way, you will encounter a middle-aged crisis such as 35 and 40, or a crisis of layoffs in small and micro enterprises or even large state-owned enterprise chains under similar epidemics.
Introductory data analysis is just the beginning. How to make career planning still needs to take one step and three steps, and adjust the goal from time to time, maintain the ability to learn at any time, and deeply understand the underlying business logic of the business.
Let's encourage each other, come on.
Author: jiangyun
Source: Zhihu
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