foreword
With the order of double reduction, the teaching and training teacher was laid off...
Big factories have started laying off employees over the age of 35...
Due to the sudden outbreak of the epidemic, many physical stores have closed down...
In the past two years, the environment has deteriorated, which is obvious to all
Let's look at a set of data first:
Didi users dropped from 42 million to 15 million, and the number of taxis dropped by 3/4 ;
Monster charging dropped from 30 million to 1.2 million, and the number of shoppers decreased by 2/3 ;
The number of Ctrip users dropped from 25 million to 5 million, and the number of tourists decreased by 3/4 ...
The only software with a growth trend this year is Boss Zhipin, with more than 100 million monthly active users and 10 million new users in more than 50 days.
This shows what?
Unemployment is surging, and countless people are desperately trying to survive.
Many people ask me, what should I do to ensure that I will not drown when the big waves hit?
My answer is: In this era where you never know which direction the black swan is coming from. We must firmly cultivate our own transplantability.
There are so many news about unemployment every year, smart people should have realized that only with multiple skills + multiple identities can withstand the repeated beatings of society these days!
Don't be complacent about the status quo, and learn a transplantable skill as soon as possible, otherwise you may become that "victim".
How do you keep yourself from being "that victim"?
According to Accenture's consulting report, these three types of people will become essential resources for enterprises:
Middle and senior management talents who can use data to make decisions for the company, they use data to capture market opportunities and find the development trend of the company, such as CTO, CEO, etc.
Professionals who can use data to build a data network for the company, they use big data, use data connotation and value for projects, such as: data engineers, data managers, etc.
Application talents who use data to empower business, they use data information and data tools to empower business growth, such as: marketing, human resource management, operations, etc.
The era of big data is booming, and emerging industries such as the Internet are skyrocketing. Traditional industries such as real estate, automobiles, and medical care are all facing digital transformation, and the supply of data talents is in short supply.
Therefore, it is foreseeable that data analysis ability will become an essential skill just like how you can use a computer in the Internet era.
Relying on data to speak and attaching importance to data analysis has become the consensus of many people.
As companies attach importance to the value of data, data analysts are also getting more and more attention. At present, more than 90% of the world's top 500 companies have established special data analysis departments.
Data analysts are undoubtedly a position that has received special attention in the era of big data, especially big data analysis talents with professional skills and industry experience, which are undoubtedly the "sweet pastry" that companies are vying for.
The data analysis industry is actually a bit like the various developments at the beginning. In the initial stage, there was a large influx of interdisciplinary, self-study, and short-term training job seekers. Of course, this is not to say that there is any opinion on non-professionals, but to state the facts.
So how do you get started in the field of data analysis? *
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Today, the editor will organize for you the knowledge you must know to become a data analyst!
▶ 01 Comprehensive understanding of data analysis
I believe that six or seven out of ten people come here for concepts such as "big data" and "artificial intelligence", but they don't know what data analysis is for? What's included?
Therefore, don't follow blindly, and use actions to understand the concepts and content of big data and data analysis is the last word.
For example, you can find relevant books to study, you don’t have to understand all of them, but at least understand the process of data analysis, application scenarios, and some data analysis tools mentioned in the book.
▶ 02 Learn statistics knowledge
Statistical knowledge does not need to be studied in depth, but it must be understood. You have to know that with the increase of work content in the future, there will only be more statistics to be learned.
To understand commonly used mathematical statistical models (descriptive statistical indicators, clustering, decision trees, Bayesian classification, regression, etc.).
The focus is on the working principle, input content and output content of the learning model. **As for the specific mathematical derivation, if you can’t learn it, you can put it aside for a while, and look back when you need it.
▶ 03 Familiar with Excel
For non-technical data analysts, only one primary tool is recommended: Excel.
The key learning object at this stage is the use of intermediate functions of Excel (pivot tables, functions, applicable scenarios of various charts and how to make them), if you have spare capacity, you can learn VBA.
▶ 04 Improve PPT display capabilities
As a data analyst, the ability to make PPT is extremely important.
Therefore, it takes a little time to understand and master how to make a PPT with prominent points and clear information, and how to insert various charts into the PPT to facilitate data update.
▶ 05 Understand the programming language of the database
This phase has two goals:
Learn basic database and programming knowledge to improve your future work efficiency;
Test which advanced data analysis tool is suitable for you to learn.
For the former, it is recommended to learn MySQL for the database (although Hadoop is very useful, but you are not a technical position, it will not be used in the initial stage). It is good to learn the joint query of the database, performance optimization, and backup content that is not needed; Python is as much as you can learn.
▶ 06 Advanced learning advanced tools SPSS or R
Although Excel can solve more than 70% of the problems, the remaining 30% still require advanced tools (such as clustering).
There are two options for advanced analysis tools: SPSS and R.
Although R has various benefits, my suggestion is to determine which tool to learn based on your learning experience in the previous step. If learning a programming language is painful, learn SPSS; Study R.
No matter which tool you use, you have to run through the key models you learned when you studied statistics until you learn to build models and slightly optimize models.
▶ 07 Understand the industry and position
The ability that data analysts most need to continuously improve is industry and business knowledge, not one of them.
Which industry and which position you want to invest in in the future, you have to learn relevant knowledge.
For example, if you want to do website operation, you need to understand Internet background knowledge, website operation index system, user operation knowledge, etc.
▶ 08 Modeling analysis report
You have studied so much, but you still can't find a good job if you go out now.
All recruiters will ask you one thing: what practical projects have you worked on? If you have relevant project experience or internship experience, of course you can use it, but if not, what should you do?
The answer is simple, make a report for them to see.
Tell the recruiter: I already have the ability to analyze data for entry-level (or even advanced) positions.
In order to help students accumulate project experience and improve project actual combat ability. Linktech will prepare corresponding project experience and interview-level works according to the target industry and company of the students.
▶ 09 Instructions for job hunting positions
When submitting your resume, don't vote casually!
Those with the keyword data have different positions:
There are positions that need to organize and analyze data, and there are also more advanced positions such as developers (data analysts, data mining engineers, etc.) that need to write code. Please be sure to read the job description before submitting your resume.
Finally, I will introduce a complete python learning route, from entry to advanced, including mind maps, classic books, and supporting videos, to help those who want to learn python and data analysis!
How to use python, SQL and other tools are the tricks that need to be used in the data analysis process. And the necessary hard skills! !
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 based on primitive foundations. With a solid foundation, the road ahead will be more stable.
Include:
Computer Basics
python basics
Python introductory video 600 episodes:
Watching zero-based learning videos is the quickest and most effective way to learn. Following the teacher's ideas in the video, it is 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
This full version of the full set of Python learning materials has been uploaded to the official CSDN. If you need it, you can click the CSDN official certification WeChat card below to get it for free ↓↓↓ [Guaranteed 100% free]
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Understand the prospect of python: https://blog.csdn.net/SpringJavaMyBatis/article/details/127194835
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