foreword
I have always said that data analysis is not a fixed position, but a general ability in the era of big data and artificial intelligence.
Just ask, which industry can leave data now? With data, who can do without analysis? It is more intuitive to look at the data below:
Data analysis is actually divided into business direction and technical direction:
For people with zero foundation, what does this picture mean?
In fact, any industry is divided into three stages as you develop further: primary, intermediate, and advanced. This is like playing a game to break through a level, crawling online level by level. Therefore, if you enter this industry with a zero foundation, you should also plan for these 3 stages, and you will be seated 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
Let me show you the recruitment requirements and feel intuitively:
2. Intermediate data analyst:
Work content:
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. The main job is to extract data and write analysis reports. Responsible for and support related reports of various departments, monitor data fluctuations and abnormalities, identify problems, and output special analysis reports.
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
The recruitment requirements for this level are as follows:
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
Come to a recruitment position to experience:
For the career development of senior data analysts, if you like the business direction, you can develop towards the management side. Common job titles include: data product manager, data operation manager.
If you like to specialize in technology, you can develop towards technical experts. Common job titles include: data mining engineer, data development engineer, and big data engineer.
4. What is the future job-hopping outlet?
If you develop towards the business side, you can eventually become a senior business expert, general manager, or CEO. One of my seniors, after several years of development, has just graduated from a data analyst and is now the general manager of a company. His advantage is that he understands both data and business. He also rented a house in Beijing just after graduation, and he has already achieved financial freedom.
It takes years of industry accumulation to become an expert in this industry.
If you are developing towards the technical side, if you have outstanding engineering skills, you can be the boss of the company's data science department. The common job title is data scientist. If your theoretical ability is very strong and you can write papers, then you can be the leader of the research institute. Because of my strong scientific research ability, my supervisor is now the head of the data department of a scientific research unit.
3. How to choose the position that suits you?
1. Learn the knowledge that suits your current ability
Find out what your foundation is, which position is less difficult to learn to switch to, and which position you are more suitable for. It is wrong for many people to start gnawing at machine learning without any foundation. Because you have no statistical probability or mathematical foundation, many technical terms in it are simply incomprehensible.
Data science is an interdisciplinary subject. In addition to computer-related knowledge, it also requires statistics, mathematics, and certain business knowledge. Therefore, it can be used as a lifelong career development goal, learn a little every day, and slowly accumulate progress.
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". The so-called "best field" refers to the three overlapping fields of what you are passionate about, what you are good at, and what society needs.
2. How do newcomers grow?
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.
3. Become a person who does not give up at critical moments
I have observed the people around me, whether they are classmates, colleagues, or business partners, and found that most people are more likely to give up when they are critical.
However, those who finally persevered, all succeeded in the end. All growth stems from a little persistence at that critical moment. Most people are passionate at the beginning and find a lot of information, but they don't want to solve the difficulties and give up at the moment before they make progress, so they never feel the thrill of success.
The above is the career development track of data analysis and data mining for those who want to enter this industry with zero foundation. May you become a person who does not give up at critical moments in this industry.
Finally, I would like to thank everyone who has read my article carefully. Reciprocity is always necessary. Although it is not a very valuable thing, you can take it away if you need it:
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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