Let's first look at the elements needed to find a job:
- Personal interest
- Income
- Entry requirements
- Long-term development
- Sense of achievement and value brought by work
1. Personal Interest#
Personal interest is a very abstract term, just like data analysis. However, it does play a significant role, and in many cases, most people do not know how to determine their interest in data analysis.
So, in the early stages, you can rely on feelings or intuition to assist in judgment.
Take myself as an example: I graduated from university in 2017 and wondered why I chose data analysis. At first, I applied to various positions without knowing exactly what I would be doing. After interviewing with three companies in the education, finance, and publishing industries, I started to think further.
Firstly, I was proficient in Excel during university and learned a lot through various channels. I even scored full marks in my SPSS exam, although I haven't used it since starting work. I conducted data analysis for my university thesis by collecting and analyzing survey data. Even the only time I made money from a game, the foundation was based on Excel and involved some data analysis.
With so much evidence, I felt that I liked Excel, so I searched on Zhihu (a Chinese question-and-answer website) to see what jobs could be done with Excel. That's when I came across the keyword "data analyst." It was actually the first time I had seen this job title, and then I followed the trail and looked at its job responsibilities and future development. That's when I decided to pursue this career.
Later on, I interviewed with three companies related to data analysis, and one of them hired me as a data specialist.
Therefore, if you have obvious historical support, it is relatively easy to determine whether you have an interest. If you can't determine, then start by considering whether you dislike it.
I personally believe that interest is the most important factor because no matter how small the matter is, you can keep going and find your own world. The remaining factors can be considered after considering interest.
2. Income#
Income may be the most concerning factor for everyone.
Firstly, data analysts belong to positions related to the Internet, and in general, the longer the work experience, the higher the income (in the first five years of work).
In terms of average salary in the industry, the order is roughly: development > product = data analysis > operations. Data analysis is in the middle position and is relatively stable due to the lack of performance-related pay.
Specifically, in Beijing (excluding major companies), the basic salary for fresh graduates is around 8k, with a fluctuation of 2k. With 1 year of experience, the salary is around 10k, 3 years of experience is around 15k, and 5 years of experience is around 25k, with a fluctuation of 5k.
In non-first-tier cities, the salary is reduced by 2-5k at each stage.
Although income is influenced by the overall environment, it is ultimately independent for each individual. How much you can earn depends on how you interact with the world.
Due to the nature of the Internet, salaries in this field are naturally higher than those in ordinary positions.
3. Entry Requirements#
The requirements for entry are constantly changing.
Five years ago, you could join with just Excel skills or even just interest. Now there are more standardized requirements, and they may change in the future, but the framework will remain the same.
Education: Full-time undergraduate degree (associate degree also has opportunities, but with slightly weaker competitiveness).
Major: Computer science, statistics, mathematics, psychology. Engineering majors are preferred, but there are also opportunities for liberal arts majors, although with slightly weaker competitiveness.
Skills: Essential: SQL, Excel, visualization (BI). Bonus: Python.
Thinking: Logical thinking, rigor, and divergent thinking are also considered.
Business: Familiarity with the Internet and digital life. Some business thinking and abilities are also required.
Language: There are no specific requirements, and language proficiency will not be a hindrance. Bonus: English CET-4 or CET-6.
Learning ability: Strong proactive learning ability (although it may sound like a cliché on a resume, it is actually very important in practice).
Stress resistance: This requirement may become less important after national adjustments.
Experience: For mid to senior-level positions, experience is required. For entry-level positions, it is not a major concern.
Among all the requirements, the skills section is the most likely to be determined by individuals. Therefore, striving to learn skills can greatly improve one's competitiveness. Once you enter the field, skills become a cornerstone. For mid to senior-level positions, other requirements become more important.
The difficulty of entry is directly proportional to income: development > product = data analysis > operations.
Another external influencing factor is competition. With the development of the Internet, it has entered a mature stage, and various positions have become standardized. Even entry-level positions face significant competition. On the other hand, there is still a demand gap for mid to senior-level positions.
4. Long-term Development#
Due to the need for certain business experience, the longer the work experience in data analysis, the more valuable it becomes. The downside is that if you specialize in one industry, you may lose the opportunity to enter another industry. However, the underlying skills are transferable, and given enough time, it is possible to switch to any industry.
In addition to the demand within the Internet industry itself, as digitization continues to penetrate society, there are also opportunities to expand into other major industries.
Even if the position of a data analyst disappears many years later, data analysis can still be a powerful skill that enhances your core competitiveness in other jobs.
For example, if you look at the requirements for operations, product management, and business management positions today, many of them require data analysis skills and thinking.
Therefore, data analysis can be considered a versatile position.
In terms of career advancement, it mainly involves becoming a team leader or being responsible for business. If you transition to data mining, your income will indeed increase, but it will be more like a career change.
Furthermore, due to exposure to business, understanding of business, and analysis of business, data analysts have a relatively clear understanding of business models, marketing, and business processes. This can be very helpful for starting your own business or partnering with others.
5. Sense of Achievement and Value Brought by Work#
This can only be experienced after starting work.
Many people leave their jobs because they feel that what they are doing lacks value. The Internet, in particular, is a place where it is difficult to generate value. The planning of ideas must have value, but it is challenging to implement them. For example, if no one uses your products and services, you will feel a sense of loss.
Among the positions mentioned earlier, data analysis is the one that is least likely to feel a sense of value. Additionally, the combination of business value is very complex, and even if you have made contributions, it is difficult to determine which part belongs to you.
Furthermore, data analysts often receive analysis projects that are difficult to produce results or projects that are difficult to explain clearly. Therefore, self-doubt may arise.
In companies where business is not well-established or the level of data-driven decision-making is low, you may find yourself with nothing to do and need to take the initiative to find tasks.
In the early stages of growth, the lack of a sense of achievement can be a significant factor, so it is important to anticipate this. The ability to learn and high self-initiative are crucial in overcoming this challenge.
Therefore, analysis should not be done just for the sake of analysis or for the sake of work. If we forget that we are detectives in the data, the world will have one more meaningless job.