How can you tell if a person is suitable for data analysis?

How to determine if the data is suitable for factor analysisGenerally, according to the KMO value and the Bartlett spherical test, the former is generally greater than the latter, and the latter should reach the significance level.

What kind of person is suitable to be a data analyst?Interested in data analysis, sensitive to numbers, with knowledge of excel, sql, statistics, etc., you can go to the big podium to experience their data analysis courses.

How can you tell if a person is suitable for data analysis? I think that no matter what kind of work interest is the most important, the most basic thing to be a data analyst is not to hate numbers, if you feel impatient with what kind of multiplication, division, addition and subtraction of that indicator is obtained, then obviously you are not suitable for data analysis; If you are sensitive to data, it is of course best to be able to detect outliers at a glance and the distribution of data. Then there is logic, you can try Einstein's classic logic problem to see if it can be solved and how long it takes; Logical thinking is especially important for data analysis, otherwise it will be entangled in the definition rules of various indicators and the connection with the business, and it will be more efficient for people with good logical thinking to write data processing scripts such as SQL.

Then there is the ability to understand the business, the simplest is to let him define what the goal is, which indicators can be used as KPIs, how the whole process of the user from entering the goal to achieve the goal is how to achieve the conversion, and whether the business process diagram can be drawn. If you are more technical, you need to understand some database structure and SQL, and if you are biased to display, you need to test your ability to control charts, when to use what charts are appropriate, and even how to match colors. The last is carefulness, patience and communication skills, doing data analysis can sometimes be very tangled, carefulness and patience are necessary, good communication skills can make data analysts better explain all kinds of problems.

These are relatively basic things, and they are also skills that are difficult to develop in a short period of time. As for some other business-related knowledge, it can be obtained through training, and it is actually a little unfair to ask a person who has not been exposed to your ** business some business knowledge, in fact, if you have the above points, once you are familiar with ** and business, you will definitely become an excellent data analyst.

1.Social**: Analyze an individual's social networking, such as Facebook, Twitter, LinkedIn, Instagram, etc., to understand an individual's interests, social circles, etc.

2.Email & SMS Cave: Analyze an individual's email and text messages to understand their actions, psychological condition, and more.

3.Financial Data: Analyze an individual's bank transaction history, credit card transaction history, etc., to understand the individual's financial situation.

4.Health Data: Analyze an individual's health data, such as medical reports, medical examination reports, health diaries, etc., to understand the individual's health status.

5.Location data: Analyze the location data of personal information, such as smartphones, GPS devices, etc., to understand the individual's travel trajectory and daily habits.

Once this data is collected, it can be analyzed using big data techniques. This may involve techniques such as machine learning, data mining, natural language processing, etc. Through these technologies, it is possible to understand the preferences of the individual, their actions, the likelihood of the decisions made by the individual, and so on.

However, it should be noted that this kind of data analysis requires strict data privacy protection measures to protect personal information. Hope.

Talk to him a lot, and the nature of man will come out.

The horoscope matches the sayings of the people around you.

Those who learn big data are generally college degree or above, have knowledge such as statistics, and have a certain foundation, otherwise even if you sign up for learning, but because the foundation is zero, it is difficult to overcome the difficulties, and it is better to think clearly and learn again if you give up automatically.

In fact, there is also a subtext to the question "who will be more likely to succeed (such as career success) by learning data analytics", depending on your interests, commitments, and opportunities. But to excel, in addition to the above three points, you also need a little talent, and the opportunities here refer to the career development platform, business environment, mentors, and colleagues you encounter. To borrow the words of management master Drucker, "management can be learned", management is not innate, and data analysis ability can also be improved.

Perhaps to be excellent, it only requires you to work harder + interest, and this process of hard work also includes the part where you look for opportunities.

Data analysts are usually divided into two categories, with different divisions of labor, but each has its own advantages.

One is engaged in data mining and analysis in a dedicated mining team. If you can learn and grow in this kind of professional team, you are lucky, but the threshold for entering this type of team is high, and you need solid data mining knowledge, experience in the application of mining tools, and programming skills. This type of analyst is more inclined to the technical line, and the future career path may take the technical route of experts.

The other type is the data analyst who sinks down to each business team or operation department and becomes a member of the business team. Their job is to support business operations, including daily business anomaly monitoring, customer and market research, participating in product development, and building data models to improve operational efficiency. This type of analyst is biased towards products and operations, and can switch to operations and products.

Depending on what you're analyzing, get the necessary expertise to do so. Otherwise, purely numerical analysis will not be able to be realistic, and the results of the analysis may not be useful. It is necessary to combine the object of analysis, and there must be enough professional knowledge, practical experience, etc., in order to analyze it concretely.