Let me clarify the concept of financial freedom first. Financial freedom does not mean that you have enough money to spend, and then you can stop working and go sightseeing every day. Because people's desires have no boundaries, "enough flowers" is a very abstract goal.
In fact, financial freedom can be said to be more of a state of cultivation: obtaining labor income, having a certain amount of money, satisfying the mind, and realizing personal and social value at the same time, can be called financial freedom.
Focusing on the theme of making money, for people with some accumulation or background, it may be mainly fixed assets or the appreciation of equity in the company. So, for "ordinary people", how does the money come from?Ordinary people choose to "make do with"
Respondent@尼克六六
It is difficult for ordinary people to achieve financial freedom. I think the most reliable way is this: patiently invest enough time and energy to recognize yourself and try to find what you "most" want/like to do. Then, hone and refine that skill with insane intensity and long time spans to make yourself a leader in the field.
the reason is simple.
Because this society only recognizes the number one, and only remembers the number one. And once you become a leader in any small field, you will have a lot of resources from all walks of life for you to "share" and "replace". That's the key.
And there is a huge gap between ordinary people and wealth and freedom: "make do with it". Because of the pressure of life, the pressure of marriage, and the vision and limitations of the people around us, we ordinary people often "make do" with a so-so job that we don't like or hate. We don't have enough skills and experience. Too much room for improvement. Although you can always make progress slowly and your salary increases slowly, you are always muddling along in a state of boiling frogs in warm water .
Ordinary people who do this can hardly achieve financial freedom, and there is also a logical paradox : "When I am financially free/earn enough money, I can stop doing this and do what I like." But almost all people with financial freedom are still very willing to do the thing that makes their wealth free after their financial freedom.
Don't waste your energy on trivial matters
Answer master@张君
In my opinion, in today’s society, the way out for the poor lies in: devoting all their time and energy to a certain narrow field, becoming an expert in a subdivided field, and fully accumulating digital assets for themselves.
One characteristic of the poor is that they generally do not value time and energy, and feel that these things are worthless. Then spend energy on all kinds of trivial things, so that there is no spare energy to "get rid of poverty".
The "poor" I'm talking about here is not abject poverty. It's not like there's a last meal but no next meal. But the vast majority of people, they have jobs, they have time, but time is wasted.
For example, if you pay special attention to eating, you have to make particularly cumbersome meals, and it takes 1-2 hours to cook a meal;
For example, the partner is not suitable, nor does it break up or divorce. I always feel that I can make do with it, but it is not impossible;
For example, friends or relatives who are not very good, and they come and go frequently, must participate in those bloody plots;
Everyone will say, this is life. That can only mean that you are more inclined to choose life, but these things do not help "financial freedom". Because there is no technical content, you can only stay away from those people and things that make your brain hurt. Only when you have time can you use the time wisely.
Develop in depth in a narrow field
All-round development equals all-round mediocrity.
Today, you read a post about financial management and thought that you should learn financial management. After spending a lot of time, you found out that you are not this material at all. Tomorrow you watch the cooking video and go to learn to cook again. The day after tomorrow, you feel that you can learn insurance knowledge to make a fortune.
The vast majority of people are very easily led astray by the various information in front of them. In fact, if you want to engage in investment, you can’t define it as investment, you need to specify funds; if you want to engage in photography, you can’t just talk about photography, you need to specify local parent-child photography; You can't just talk about insurance, you have to be specific to the insurance of a 30-year-old code farmer on the Internet.
With a narrow positioning, coupled with the investment of all time and energy, it is possible for an ordinary poor person to accumulate a competitive advantage for himself. Position your circle of competence that suits you , and focus on your target audience, products, topics, channels, content, and skills.
Then what principles should be used to find the subdivision field that suits you? You can choose the intersection of these elements: what you like, what you do well and what others do not, and someone in the market will pay for this ability. That is, the intersection of interest circles, ability circles, and market circles.
This is what works for you, and a diagram to show it looks like this:
The specific method you can refer to is to first collect relevant basic data and see how the people you follow are making money. These can be recorded in an excel sheet.
When you have enough basic data, turn the above 4 points into screening conditions. According to these conditions, these people will tick whichever of the above elements meets the above, and then the remaining all ticked are the fields you can try first. or direction.
Some people may think that it is very boring and tedious to stay in a small field all the time. In fact, this just shows that you have not found your circle of interest and ability. You must do what you are good at and do well, so that you can be happy and have a competitive advantage.
What if I don't have the ability to become an expert in a niche worth doing?
There is a cruel fact that not everyone can become a segmented expert in a certain field. The vast majority of people neither think nor act , but at the same time they don't want to live too badly. At this time, the most suitable strategy for most people is the "follow strategy".
Choose a skill with "money way"
According to Liepin data, the average monthly salary of data analysts is 22,322 yuan, and the monthly salary of fresh graduates is 10k+.
Specific to different levels to data analysts, the difference is relatively large. Based on my research on friends who work in first-tier cities around me, the monthly salary of junior data analysts is about 5k-10k. The monthly salary of an intermediate data analyst is about 7k-20k+. The monthly salary of a senior data analyst is about 15k-30k+. (1k=1000 yuan here).
If you can become a general manager or a data scientist in the end, then an annual salary of one million is not a dream. According to the "Data Competition Platform Kaggle 2020 Annual Report", the salary of data scientists in the United States is the highest, with an average salary of about 120,000-150,000 US dollars (about 780,000-980,000 RMB).
Moreover, "data analysis" itself is not a fixed position, but a general ability in the era of big data and artificial intelligence. Let me ask, which industry can do without data now? With data, who can do without analysis?
It will be more intuitive to look at the following data:
Any industry is divided into 3 stages as you develop further: primary, intermediate, and advanced. This is the same as playing a game, climbing up level by level.
Therefore, if you enter this industry with a zero foundation, you should also plan for these three stages, and take a seat according to the requirements of each stage below.
1. Junior Data Analyst
Work content:
Skilled use of Excel and PPT is required. Analyze the requirements raised by the product manager. Then use PPT to make an analysis report.
For example, in an Internet education institution interviewed by members of the community, their requirement was to use Excel to sort out the information of students buying courses, to see which course is the most popular and so on.
Core skills to master:
Excel, PPT, descriptive statistical analysis, business knowledge
Monthly salary: The approximate salary of this position is about 5000-10000 before tax in the first-tier cities
Common job titles include:
Data analyst, data operation, business analysis, strategic analysis, business analysis, market industry analysis.
2. Intermediate data analyst:
Work content:
Data extraction, writing analysis reports.
Responsible for and support related reports of various departments, monitor data fluctuations and abnormalities, identify problems, and output special analysis reports.
It is necessary not only to know technology but also to understand business. By discovering problems, analyzing problems, and drawing conclusions, we can support the company's decision-making.
Core skills required:
Excel, PPT, statistical probability, business knowledge, familiar with SQL
monthly salary:
The approximate salary of this kind of position is about 7000-20000+ before tax in the first-tier cities
3. Senior Data Analyst
Work content:
Process data, analyze data, build models, and predict through programming.
Core skills required:
Statistics (inferential statistical analysis, A/B testing), familiar with SQL, programming language Python or R
monthly salary:
The approximate salary of this position is about 15,000-30,000+ before tax in first-tier cities
After figuring out the difference between each position, as well as understanding your own foundation, knowing yourself and the enemy, you will have direction and confidence in learning and changing careers. The key is to work in your "best field".
Faced with different positions, we need to choose a car based on our own experience and personal ability, so as to share the dividends of the artificial intelligence era. Data analyst is a more suitable direction to get into the car, because it has relatively low starting threshold, large market demand, and good room for future job development. The zero-based recommendation starts with primary data analysis.
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