It is the end of 2020 in a blink of an eye, and the new year is beckoning again. Saying farewell to the old and welcoming the new, everyone will always care about the future. Today, we will systematically talk about the prospect of data analysts in 2021.
▌Question 1 : Is 2021 still suitable for entering the data analysis industry?
Answer: This is a false proposition!
There is no data analysis industry
There is no data analysis industry
There is no data analysis industry
Tell the important thing three times
Industry refers to: automotive, finance, telecommunications, retail, food, trade, these business types.
Data analysis is a job, and every industry will have this job more or less.
Data analysis is not an industry, and there is no such thing as a "unified industry standard".
The differences between different industries are quite clear.
Even within the same industry, such as the financial industry, securities, banks, Internet finance, underground banks, third-party credit agencies, collection companies...the differences are all above and below. Even a sub-industry, such as banks, internal headquarters and branches, personal banking and corporate business, and credit card centers, are very different.
Not to mention: the Internet industry, in 2021, no industry is not the Internet industry. E-commerce and new media are in almost all industries, and almost all traditional Internet companies have offline businesses (fresh food, O2O). The boundaries of the industry are very blurred.
Therefore, if you want to mix well in 2021, the first important thing is to carefully study the job market and see the specific business characteristics of each company. Instead of pretending to lie to yourself such as "the Internet industry is everywhere gold, everyone has a million annual salary" "I am collecting company statistics and I am also a financial industry data analyst". Remember to remember.
▌Question 2 : In 2021 , I want to transfer to a data analysis position. Is it difficult ?
Answer: The key to this question is who " I" is and where " I" is "transferred" from
Some types of work are inherently close to data analysis, while others are far away (as shown below)
Some industries naturally value data, so they accumulate a lot of personal abilities, and some industries are born with a lack of data, so personal abilities accumulate less, and those who accumulate more are definitely easier to transfer.
Personal conditions, the more suitable 996, the less difficult it is. The more data skills you know, the less difficult it is. Between the ages of 24-29, single, well-educated, good school, computer major, have experience in the industry, hadoop, spark, java, python, etc., it is definitely easier to Chinese and Kazakhstan. As for whether you can live without a few things, it depends on who you are competing with . It's kind of luck here. So you can look at the specific job requirements and evaluate your own gaps. You need to be specific to a position to know whether it is difficult or not.
▌Question 3 : 2021 , am I suitable for a data analysis position?
Answer: Whether it is suitable or not depends on what you want
If you want to improve wages, it’s best to quantify how much you want to increase to
If you want not to work overtime, consider the acceptable salary bottom line
If you want to find promotion opportunities, consider whether you can bring subordinates and bring projects
If you want to find industry outlets, check the specific business types and development trends
Once you have a clear goal, look for the corresponding position and company, and then see if you can face it, and try a few times to know if it is suitable. This is actually similar to data analysis. If you don't do ABtest, you can't draw conclusions from the analysis based on the road. Besides, finding a job is a process of seeing each other's eyes. If you want to see people, they don't necessarily see you.
Of course, some students will say: "You can't finish your work in one fight, 21 days and 0 bases enter the headline with an annual salary of one million!" Well, the idea is very good, but whether it can be realized, this question will be discussed separately.
▌Question 4 : However, I see the Internet saying that the threshold of data analysts is low, the salary is high, and the annual salary of a data scientist can be changed to one million yuan . Entering the top ten, the 30 -year-old is worth over 100 million!
Answer: You "online", you know, after all, everyone knows millions.
They are all saying that the three things of high wages and low thresholds will not exist at the same time. This is a basic common sense. (As shown below)
Truth 1: Highly paid data analysts have a low threshold. There are too many people who want to enter a big factory, and 99% of the people can be brushed down by simply fighting for background, experience, and qualifications, and they are not in the right to fight for skills.
Truth 2: Data work with low barriers to entry has a very slim future. Just be a statistic, pull the number, usually no one cares about data specialists everywhere.
Truth 3: Data analysis and data science are not the same job! The skill tree is completely different. Moreover, data science is more inward-looking than data analysis, and it is basically the top one among graduates.
As for why there are so many people propaganda: low threshold, high salary. Let’s put it this way, the last time Teacher Chen heard someone say this, it was at the MLM conference that a host of oily noodles asked Grandma Li, who was cured of cancer by XX, to share.
By the way, these people also like to use the following words:
"Thank XX teacher for taking me into XX industry"
"I am a two, 21 days 0 basic counterattack to enter XX"
"I want to create a happy life for myself and my family"
"I can get a lot of money by learning a bunch of XXXX skills"
A little more clear-headed, you know that these are support. But why is there always a market for these magic drugs? Because styled, people, when in a hurry, always willing to believe that there is such a mysterious force. "What if it's true". So get used to it.
▌Question 5 : But, do I hear others say that it is the digital age? Then data analysis must be a super skill. I use data analysis, and everyone else kneels and shouts that it is invincible.
Answer: Not at all!
The so-called digital transformation is a combination of a series of systems such as digital production lines, digital media, digital interactive channels, digital marketing methods, and operation methods derived from the system. It's not like opening your mouth and saying "I think" before, but now opening your mouth and saying "I saw the count" is digitization. In this system, the use of digital technology to transform traditional production lines, innovative media and interactive forms, and data-based management, planning, and execution are far more important than "analyzing a number". The value of the number itself far exceeds that of analysis .
Therefore, even in the digital age, data analysis is still a supporting position, a supporting role, and a job that can be fired first when the business is not doing well. Data-driven business refers to: "Boss uses data to drive business." It does not mean that you run a number and write a ppt, and the directors and managers of various departments in sales, operations, supply chain, and marketing will bow to you. Thinking too much, it's normal to be urged to count.
▌Question 6 : So, what about a reliable personal growth plan?
Answer: The most important thing to do in 2021 is to stop listening to nonsense.
Especially stop listening to those who majored in civil engineering / mechanics/ biology, graduated from the second university, moved bricks on the construction site, 21 days 0 basic self-study excel , sql , python entered the Internet factory with a monthly income of 30,000, and became a data scientist in half a year. Ten thousand SSP offer , now share the book list for learning data analysis skills .
Think carefully (as shown below)
The reason why finding a job is called "finding" a job is that you have to find a job. Why not call it a "blow" job, because it's useless to just listen to others talking about the interview. Why is it not called "learning" work, because learning is impossible to learn, do not collect information carefully, do not go to match, just hope to learn, you will never finish learning.
Just like data analysis, the first step is to collect data. To prepare for a job, the first step is to collect target information carefully. The more you collect, the better. Just like Abtest for data analysis, you almost have to try the collection, record the process, and analyze the problem. This is the quality that a reasonable data analyst should have.
Thinking down-to-earth, collecting facts and data carefully, and analyzing specific issues in detail are the right ways to break the game. It is also the basic quality that a qualified data analyst should have. If you can’t even do this, there are no facts and figures in your mouth, and your mouth is full of "I think", "I heard others say", "I thought" "It shouldn't have been"... Then really, this character is not suitable for statistics. Analysis, haha.
Some students may ask: I have already joined the job, how long can I do data analysis? Will you encounter a 35-year-old crisis! If you are interested, I will be watching this collection of 60. We will share the next one, so stay tuned.
Original selection:
The ultimate question of data analysis: Under multiple influencing factors, what is the attribution? !
The annual work plan for data analysis is reasonable in this way!
Finding a job in data analysis, why is it so easy for others, so difficult for me
Data analysis is not an industry, not a specialized job, but basic skills that everyone needs to master. Improve your data analysis ability and learn the video course "A Guide to Business Analysis" by Mr. Chen . In the course of study, you can also join a group of students to discuss issues with Teacher Chen one-on-one to improve your ability.
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