foreword
Let me talk about the conclusion first: [Zero foundation] is the biggest poisonous point of data analysis.
If this conclusion is not established, many people who come to find data analysis jobs will have a bad nose
So many people come to ask, how can I transfer with zero foundation, how can I transfer if I am not in the industry, and how can I transfer if I have no experience
if i say few chances
They will say, the training institution says you can transfer, do you look down on the workers, how can there be industries with zero foundation that cannot be transferred.
Of course, I believe that most people want to get a chance.
But I personally still suggest that when looking for opportunities, don’t pick the hardest wall to hit. I also want to say that everyone has the opportunity to participate in the US presidential election, and I even believe it is easier than doing data analysis.
These misunderstandings are largely due to agency propaganda that as long as they are trained, they can work.
The vast majority of those who can be trained to work are purely technical positions, and the development set cannot be brought into the data analyst. Therefore, the so-called big data analysts hired by many training institutions are not analysis posts, but many are data development and etl engineers.
Of course, I have seen a lot of training institutions, mainly because they have blurred the publicity, and the course schedule and instructors still honestly write etl.
It's just that the people who couldn't help being promoted took the initiative to block these keywords, thinking that what I studied was data analysis, and I could find a job with zero foundation.
no。
Because this position requires experience, it cannot be said that you hope that you can gain experience when you come in, but also hope that you will not have experience when you are recruited. This is not logically consistent.
So back to the topic, it is very simple to change careers with zero foundation, and what level you have learned.
Learn to the level that practitioners can do.
You can understand what practitioners do by reading my article, so I won’t go into details.
So as long as you carefully study what practitioners do, you will find that there are some things that are out of reach with zero foundation.
Even the training course is impossible for you.
What can be learned: SQL, python, tableau, analysis ideas, statistics.
These are easy to learn, and many people are eager to ask me, can I find a job with zero foundation after learning this?
My answer is generally, you try it, if it doesn’t work, I don’t guarantee it anyway.
If there is really a lot of vacancies in the market, then believe me, you can only be hired if you can work.
But these things are so easy to learn, and the salary is not low, so everyone will wait for you to transfer with zero foundation if you don’t earn the easy money?
There may be only hundreds of thousands of analysts with a foundation, but there are tens of millions of denominators with zero foundation. Everyone is not stupid.
So there's a little bit in the works: industry analysis experience and practical problem-solving.
You can't learn.
For example, training courses often teach an RFM model for user stratification.
But in actual work, this is not the case at all. The actual situation is: the department finds that growth has slowed down, user profits have declined, and more refined operations are needed, and then it is found that users have been layered and adjusted, and then they are given to the data. The analysis raises a requirement, saying that we need to look at user performance.
As for the RFM model, it is very old. Although it is a good analysis idea, if it can only answer questions (some even cannot answer what RFM stands for), it cannot be applicable to all enterprises.
What really applies to all businesses is how you think about the model in action.
For example, why do I choose profit, how to express the profit indicator best, whether to use the user's one-year payment or one-month payment, how to stratify and cluster, how to experiment with the results, and how to improve and optimize.
Training courses can't teach you, because you pay them, and they are only responsible for finishing the lecture.
In the company, the company gives you money to solve the problem, and if you can't solve it, you leave.
The demand side will tell you to do it quickly, and the actual situation will let you see that a simple set of models cannot solve the problem.
This is work experience.
People with zero foundation, or people from other departments, cannot feel this kind of experience.
Very straightforward, I advise students with zero foundation, you can transfer, you can try after learning what I said above, but whether you can find a job or not, I said it directly, the probability is not high now .
In addition, I personally suggest that you can choose other departments that have similar scores and do not require much professional knowledge. For example, operations and products, they are our demand side. If you really want to do this business, saving the country with a curve is a better solution.
Go straight to it, and you might say you finally got a job, but you never end up doing what you hoped to do in the first place.
You may have been taking data and making reports, although the company also calls you: data analyst.
1. Introduction to Python
The following content is the basic knowledge necessary for all application directions of Python. If you want to do crawlers, data analysis or artificial intelligence, you must learn them first. Anything tall is built on primitive foundations. With a solid foundation, the road ahead will be more stable.All materials are free at the end of the article！！！
Include:
Computer Basics
python basics
Python introductory video 600 episodes:
Watching the zero-based learning video is the fastest and most effective way to learn. Following the teacher's ideas in the video, it is still very easy to get started from the basics to the in-depth.
2. Python crawler
As a popular direction, reptiles are a good choice whether it is a part-time job or as an auxiliary skill to improve work efficiency.
Relevant content can be collected through crawler technology, analyzed and deleted to get the information we really need.
This information collection, analysis and integration work can be applied in a wide range of fields. Whether it is life services, travel, financial investment, product market demand of various manufacturing industries, etc., crawler technology can be used to obtain more accurate and effective information. use.
Python crawler video material
3. Data analysis
According to the report "Digital Transformation of China's Economy: Talents and Employment" released by the School of Economics and Management of Tsinghua University, the gap in data analysis talents is expected to reach 2.3 million in 2025.
With such a big talent gap, data analysis is like a vast blue ocean! A starting salary of 10K is really commonplace.
4. Database and ETL data warehouse
Enterprises need to regularly transfer cold data from the business database and store it in a warehouse dedicated to storing historical data. Each department can provide unified data services based on its own business characteristics. This warehouse is a data warehouse.
The traditional data warehouse integration processing architecture is ETL, using the capabilities of the ETL platform, E = extract data from the source database, L = clean the data (data that does not conform to the rules), transform (different dimension and different granularity of the table according to business needs) calculation of different business rules), T = load the processed tables to the data warehouse incrementally, in full, and at different times.
5. Machine Learning
Machine learning is to learn part of the computer data, and then predict and judge other data.
At its core, machine learning is "using algorithms to parse data, learn from it, and then make decisions or predictions about new data." That is to say, a computer uses the obtained data to obtain a certain model, and then uses this model to make predictions. This process is somewhat similar to the human learning process. For example, people can predict new problems after obtaining certain experience.
Machine Learning Materials:
6. Advanced Python
From basic grammatical content, to a lot of in-depth advanced knowledge points, to understand programming language design, after learning here, you basically understand all the knowledge points from python entry to advanced.
At this point, you can basically meet the employment requirements of the company. If you still don’t know where to find interview materials and resume templates, I have also compiled a copy for you. It can really be said to be a systematic learning route for nanny and .
But learning programming is not achieved overnight, but requires long-term persistence and training. In organizing this learning route, I hope to make progress together with everyone, and I can review some technical points myself. Whether you are a novice in programming or an experienced programmer who needs to be advanced, I believe that everyone can gain something from it.
It can be achieved overnight, but requires long-term persistence and training. In organizing this learning route, I hope to make progress together with everyone, and I can review some technical points myself. Whether you are a novice in programming or an experienced programmer who needs to be advanced, I believe that everyone can gain something from it.
Data collection
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