In the past 3 months, I have been busy with various business trips and projects. I invested in a company at the beginning of the month and received an offer. The basic salary has nearly doubled. I am quite happy. This industry really needs to understand both technology and business. Add a little luck. . .
Start by clarifying who I am and how I got two jobs in 3 months of self-study. I will write it in 5 parts:
- Entering university
- Initial data analysis
- start to learn
- hoarding data
- start looking for a job
1: Entering university for the first time:
Hello everyone, my name is Xingang. I graduated from an engineering college in 2018, majoring in mathematics and applied mathematics. When I took the college entrance examination in 2014, I originally applied for civil engineering, but I was transferred to mathematics. At that time, I really didn’t know what mathematics majors could do, including meeting a few funny teachers who couldn’t explain clearly. He pointed out two directions for us: 1. Inter-professional postgraduate entrance examination 2. Take another degree. At the beginning of admission, a teacher showed us the development of our seniors and came to the conclusion that 100% of them did not engage in professional-related matters except those who took the postgraduate entrance examination. This made me even more convinced that there is no future in studying this major, so my heart began to give up learning.
The mathematics major is different from other majors. It is completely incomprehensible after a little distraction in class, and the next few classes are all like this. In the first class, the teacher of mathematical analysis gave us a slap in the face: to prove that 1+1=2, hey hexiu proved two blackboards, but of course we didn't understand it at all. Describe my mood at the time with one sentence: fuck it, I'll go to hell. How could it be that easy? In a department of 30 people, a fixed seat is formed in the class, and no one can see it clearly at a glance. I was forced to study mathematics for 4 years.
Note that I was forced to study mathematics for 4 years. Since I was forced, of course I did not study it seriously. Sadly, now that I've discovered the importance of learning math, I'm starting to make up for it again, a real sin.
2: Initial data analysis:
After graduating, I did a lot of things. I went to VIVO to sell mobile phones, went to a health care product company as a lecturer, and later went to a pharmaceutical company in Shenzhen to do investment promotion. Later, I was dispatched to Beijing as an investment promotion manager. I spend more than half of the time on the subway, I am very tired and feel that time is wasted. One day in November 2019, when I was bored and swiping Zhihu, I suddenly discovered the industry of 'data analysis'. I vaguely felt that it was related to mathematics. When I checked online, Beijing, Shanghai, Guangzhou and Guangzhou were recruiting a lot of companies in the data analysis industry. , and the requirement is to major in mathematics or statistics. In an instant, I feel that God has opened a window for me!
Three: start learning:
On February 25, 2020, I resigned from the position of pharmaceutical investment promotion, rented a house in Beijing, and decided to start learning data analysis. In fact, I don't know where to start at all. I browsed Zhihu and saw that many great gods recommended a bunch of books and bought them all. I think it's not wrong to read books. After a week of reading, I can't understand them stupidly. Python learning, I must learn python, so I bought a set of Python courses online and started learning basic courses. I learned a lot from the first set of Python basic courses, and I learned very quickly. Finished the class. Then I don't know what to do, what to learn, and then read "Data Analysis with Python", I still can't understand the first and second chapters!
Four: hoarding materials to learn:
There are too many courses on the Internet. I collected 200G of data on my mobile phone just from the Internet, a lot of videos, e-books, notes, etc. It took me a week to sift through these materials, and finally sorted out a more effective learning materials. If you are also learning Python, I can share the information I have compiled with you, and leave a message in the comment area if you need it.
During the process of sorting, I found a few problems:
1. Python version and IDE problems, useful 2, useful 3, this is terrible for a beginner to type according to the code, because you are typing along the screen code, different courses use different tools, learn to learn I was stunned.
2. Too much information, I don't know what to learn! There is too much information, the surface is spread too large, and it is easy to go astray. My original purpose was to go towards data analysis, but because of the induction of many courses, I accidentally turned to python web, python full stack. Why does this happen? Because I learned about the training institutions on the market and found that they have learned a lot, I thought that data analysis must use these things.
3. There is still too much information, it is easy to learn heavy. Something was learned in one video course and then covered for structural integrity in another video course. But you have learned that time is money for us. Skip, skip! But I can't understand the next video! why? Because we didn't see some of the little points he mentioned earlier, we don't understand it for beginners! So we have to learn the previous ones again. If we are so impetuous, it will be difficult to continue learning.
4. The problem of the teacher's teaching style. For many courses, many problems prevent you from learning. I have encountered two points: ①The teacher's accent is a problem, and he speaks nervously! ②The code of some teachers is directly copied and pasted, which is inexplicable to me, and I can't react. After all, I have no basic knowledge.
Based on the above points, I organized the 200G data and divided it into five folders. If you are ready to learn or are learning Python, this resource will definitely help you.
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① Python learning roadmap in all directions, clear what to learn in each direction
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② More than 100 Python course videos, covering essential basics, crawler and data analysis
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③ More than 100 Python practical cases, learning is no longer just theory
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④ Huawei produces an exclusive Python comic tutorial, which can also be learned on mobile phones
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⑤ There are many Python e-books, both mainstream and classic
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⑥ It is very convenient to review the real questions of Python interviews of Internet companies over the years
. I have uploaded the information of this system to CSDN. Friends who need this set of information shared by bloggers can scan the official QR code of CSDN below and add it to get it.
Five: Start looking for a job
After two months of study pain, on April 22, 2020, I started to submit my resume online and started the interview journey. It is really difficult for people who change careers. First of all, you have no working experience and do not understand the company's business, so You don’t understand a lot of things. In the data analysis industry, it is important to say that technology is important, but business knowledge is also very important. In order to have a better interview, I started to learn business knowledge and learn to write data analysis reports. After going back and forth for 3 weeks, I finally received my first job as a data analyst in a media company. Of course, the salary is only 5500, although it is much less than the original medicine, but the real learning is still in the process of work. For us, the most important thing is to find a job in a short time, because learning on the job is the fastest!
Now I am working as an analyst and model development engineer in a company in the economic investigation industry, with a monthly salary of 27k before tax, and I have continued to learn a lot of things, such as databases, as well as some machine learning models and natural language processing. Of course, the models in economic investigation are all statistical models, mainly based on sql. Now let me optimize the sql model and add some Python models as appropriate, but because of industry problems, machine learning is not very useful. Change jobs to be a real data analysis engineer, but now the general environment is not good, let's wait and see.
The field of data analysis is an entry point. Let’s look at the biggest pit that has been stepped on. It may be focusing on the use of tools and ignoring business knowledge, so let me give you a suggestion, that is, choose a good direction for the first job.
I hope everyone can find a job that they are satisfied with.