What is the employment prospects for the artificial intelligence industry? What are the salary benef

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1. Don't be impatient, recognize your current level and make a lot of money. Data analysts tend to focus on business, while data mining tends to focus on technology. For those who want to enter this industry with a zero foundation, it is recommended to start learning data analysis at a beginner's level and not start with Python or machine learning because the road needs to be taken step by step, and it is impossible to understand Python or machine learning with a zero foundation.

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And I see too many people who are impatient with zero foundation. If they are impatient to learn knowledge that their current abilities cannot reach, it will waste time. 2. Building your core competitiveness with industry experience. Many new professionals think that by learning such advanced tools, they can make a lot of money. In fact, tools are only used to solve industry problems. What truly makes you valuable is the years of work experience and business skills accumulated in a certain industry, This is something that others cannot replace.

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Let me show you the job requirements for a data analysis supervisor. The work experience in the job requires years of accumulation, and the tools only require Excel and analytical thinking, without any high-level and difficult tools to learn.

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Let's take a look at the recruitment requirement for a business data analysis with an annual salary of 500000 yuan. Where does the "industry judgment" come from? You still need to focus on a specific niche industry to accumulate.

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So, my suggestion is that after learning data analysis, one should go to work and accumulate work and business experience, which cannot be mastered solely by learning. Thirdly, how to choose a suitable position for oneself? 1. Learn knowledge that suits your current abilities, understand your foundation, make it easier to transition to a different position, and determine which position you are more suitable for.

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Many people start to focus on machine learning as soon as they start without any foundation. This is not right because you don't have a foundation in statistics, mathematics, and many professional terms cannot understand that data science is an interdisciplinary field. In addition to computer related knowledge, you also need to have a foundation in statistics, mathematics, and certain business knowledge, so it can be used for a lifetime.

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Career development goals: learn a little every day and gradually accumulate progress.

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By understanding the differences between different positions, as well as understanding one's own foundation, knowing oneself and the other, one can have direction and confidence in learning and changing careers. The most crucial thing is to work in one's "best field". The so-called "best areas" are the three overlapping areas you love, what you excel in, and what society needs.

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2. How do newcomers grow? Faced with different positions, we need to combine our own experience and personal abilities to choose a car to share the dividends of the artificial intelligence era. Analysts are a more suitable direction for getting on the car because their starting threshold is relatively low, the market demand is large, and there is good room for future job development. It is recommended to start with basic data analysis.

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3. Becoming a person who never gives up at a critical moment. I have observed people around me, whether they are classmates, colleagues, or entrepreneurial partners, and found that most people are more likely to give up when they reach a critical moment. However, those who ultimately persevere succeed in the end. All growth comes from a point of perseverance at that critical moment. Most people are passionate about finding a pile of information at the beginning, but they don't want to solve difficulties, They gave up at the moment of progress, so they never felt the pleasure of success.

May you become a person who never gives up at a critical moment in this industry. Data analysis guide: Helping you develop the internet industry has a "money" path www.zhihu.com xen/market/remix/pay_ column/1338179624246439937。

​www.zhihu.com/xen/market/remix/paid_ column/1338179624246439937

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