With the generalization of high education, the overall quality of all walks of life is steadily increasing. The team like the takeaway brother has 70,000 masters and 210,000 undergraduates.
Similarly, babysitters are no longer ordinary housekeepers in the traditional sense. There has been a group of high-quality, strong learning abilities, and skilled babysitters, all of whom have a bachelor degree or above, and their abilities are close to "housekeeping."
Image source: Sina Weibo
No, there was a hot search on Weibo. A Shanghai owner recruited female life assistants and the annual salary was between 50W-100W. As the so-called "water rises, boats rise," and wages go up, the hardware requirements are naturally high.
Just as it was rumored a while ago that a master's degree from a prestigious school happily becomes a parenting wife after returning home, it seems that the notion of "professionals without high or low" has been deeply rooted in the hearts of the people.
However, whether it is a nanny, a confinement, or a childcare worker, they are all positions with a clear skill. The education, background, and ability are just the icing on the cake.
Image source: Sina Weibo
However, some netizens pointedly pointed out that most high-end housekeeping posts prefer women, and men do not seem to have a competitive advantage in this popular field. I feel sorry for men for a few seconds...
Having said that, there are many domestic hot industries that are currently booming. Whether it is AI, or big data, cloud computing, blockchain, etc., there are not too many gender limitations. It is suitable for men with strong logic and learning abilities.
Today, we take data analysis as an example to introduce a high-paying industry suitable for both men and women: data analysis.
——Industry prospects
If a company wants to win in a highly competitive market, decision-making speed and feedback efficiency are particularly important. How to quickly transform data into a basis for decision-making is an urgent and inevitable problem for modern enterprises.
Data analysis radiates great charm in corporate decision-making and is sought after by practitioners. At the same time, the huge talent gap makes rational data analysis and new data analysis talents with practical experience in short supply.
Not only that, the introduction of data analysis is scientific, the industry is adaptable, and it can be easily mastered with zero foundation. Once you have a strong business and analytical operation ability, it is not difficult to get a high salary.
——What is data analysis
In order to extract useful information and form a final conclusion, the process of detailed research and generalization of large amounts of data is called data analysis.
In fact, to put it simply, it is complicated, messy, and many data, whether it is text, music, words, numbers, etc., through processing and analysis, it is a method of knowledge and wisdom.
With the advent of the era of big data, people with data analysis thinking are favored by people from all walks of life. At the same time, based on this thinking, a popular industry has gradually formed.
The digitalization process of major companies continues to sublimate, and the demand for data analysis is also increasing. The market orientation of short supply has made them a new industry, and the wages of practitioners are relatively high.
-Give you an example
If you are the manager of a well-run Taobao clothing store, you should have a lot of data in time, such as: how many items are sold a day, how much money you earn, which brand sells more, which brand sells less, which product needs replenishment, Which styles and colors are popular, etc., so that you can make strategic adjustments to maintain healthy growth.
This is to understand the situation.
When the data accumulates to a certain level, you will start to discover patterns, such as: certain people like to buy dark round neck clothing, while another type of people like loose light-colored clothing. People who buy brand A will buy brand B shorts again. Browse Customers on page C will be interested in goods D.
This is data mining.
After you get the information, you will try to sell round-neck dark clothing to a certain type of people, and loose light-colored clothing to another type of people, add the B brand shorts sales link to the A brand product page, and add the D product Promotional offers are added to the C page, which greatly increases the sales of goods.
This is the law of discovery.
After a period of time, you find that E brand is viewed 2-3 times and one piece can be sold, so you will find ways to increase the number of clicks of E brand. Through the trend of page views, you can roughly predict the changes in sales in the future.
This is to predict the future.
——What capabilities do data analysis have?
So, after talking about so much data analysis, what aspects should we learn and optimize for entering the data analysis industry and becoming a leader in this field? The editor lists several aspects here for your reference only!
①Basic knowledge
On the basis of mathematical knowledge, data analysis also introduces statistics, including but not limited to mathematics, linear algebra, statistics, etc. These are the cornerstones that determine the height of the career development of data analysis. I hope everyone can master it.
Junior data analysts only need to learn to describe the content and formulas related to statistics, but if they want to go further, they must master statistical algorithms, even machine learning algorithms, and more knowledge. Algorithm-related work requires in-depth learning of high numbers. .
②Analysis tools
Excel is the easiest to get started and the most widely used data analysis tool, so please be steady with its functions, pivot tables and formulas. In addition, it is better to have professional statistical analysis skills such as SPSS.
In addition, as long as you are dealing with data, you have to touch the database, so you have to learn the basic methods of querying, modifying, adding, and deleting SQL.
As the data grows, you may need skills such as Python or R to process data more efficiently. Some industries will also need SAS or other tools, and you need to choose according to the actual situation.
③Business /industry/business knowledge
From various operations, we can see that pure data analysis out of business is meaningless. If you want to be an excellent data analyst, you must first understand the business.
After getting familiar with the business, get the required data, conduct business analysis on the data, and work out the corresponding plan. This is the real fragrance.
④ Inter-departmental communication and coordination ability
The company is composed of various departments, and data analysis is naturally inseparable from communication with many departments, such as the business department, technical department, etc. To show the resulting report to others and persuade others to accept it, data analysts must have good coordination Communication skills.
⑤Continuous and rapid learning ability
Learning ability is one of the most important qualities. Whether in data analysis or other positions, one needs to have the ability to continuously and quickly learn, and learn a variety of new knowledge and new skills.