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This article mainly explains "what are the basics of Python data analysis". Interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Next, let the editor take you to learn what are the basics of Python data analysis.

1. Definition of data analysis

Data analysis refers to a large number of ordered or disordered data for centralized information integration, operation extraction, display and other operations, through these operations to find out the inherent rules of the research object. Therefore, the purpose of data analysis is to reveal the law of the movement and development of things, improve the operation efficiency of the system, optimize the operation flow of the system, and predict the future development trend.

First of all, there must be some data, which may be in the past, may be present, or may be in the future. The volume of data is so large that no rules can be seen with the naked eye. At this time, we will use some means and methods to analyze and process these data, show them, and then draw some conclusions, which can help us to make some decisions. For example, some treasure website has a large number of commodity data, these data are disorganized, data analysts organize and process these data, through some relevant analysis models, dig out potential products and determine sales strategies. in order to better optimize products or services.

2. The core idea of data analysis.

In order to achieve the purpose and significance of data analysis, beginners should understand and follow the three core ideas of data analysis, which is also a necessary basic knowledge for learning data analysis. The past: for historical data that has happened in the past, it has taken place and cannot be changed. However, the historical data is still very precious, through the summary and analysis of the historical data, we can find some relevant deficiencies or can be optimized, so as to establish a model, optimize and improve the relevant system; now: the data that is being generated is very important to us, through the current data, we can understand the specific market in time, deal with the abnormal situation in time, and ensure the normal operation of the system. The future: through the historical data analysis and modeling, through the current data real-time optimization adjustment, so as to predict the relevant development trends in the future, and then prepare in advance.

3. The application field of data analysis

Data analysis is used in a wide range of fields, so let me give you a few examples to give you a more intuitive feeling. For example, urban traffic analysis, statistics in the second quarter of 2020, 100 cities ranked TOP 10 traffic congestion, through the analysis of urban traffic data, we can know which cities are more smooth traffic, convenient for us to choose livable cities. There is also housing rental analysis, through the treatment of Shenzhen housing rental situation, and visual display, so that we can clearly see the distribution of housing rental in Shenzhen. Generally speaking, data analysis has long penetrated into all industries, especially the Internet, e-commerce and finance. It is also widely used in manufacturing, biomedical, transportation and logistics, catering takeout, energy, urban management, sports and entertainment and other fields. Therefore, our clothing, food, housing and transportation really enjoy the convenience brought by data analysis!

4. Data analysis and development process

As a data analyst, whether it is to complete temporary small tasks or strategic large projects, they should follow the data analysis and development process when doing data analysis. You may not plan to be a data analyst in the future, but in order to achieve data analysis, it is also necessary to master and be familiar with the data analysis development process.

The following are the basic knowledge necessary for getting started with data analysis, which you can keep in mind:

Data analysis and development process is generally divided into the following five stages, including: data acquisition, data processing, data modeling, data analysis, data visualization. If you want to do data analysis, you must first have data, so the first step is to obtain data, and the process of obtaining data is called data acquisition; the acquired data often has some interference data in it, so it is generally necessary to deal with the data accordingly, that is, data processing; different businesses have different requirements for data, and after data processing is completed, modeling will be carried out according to relevant business or strategic requirements. After the model is built, the data will be analyzed according to the indicators to be analyzed or calculated in the model, and the target analysis results will be obtained. the final analysis results should be understood not only by data analysts, but more importantly by decision-makers. therefore, it is necessary to display the data through data visualization.

At this point, I believe you have a deeper understanding of "what are the basic knowledge of Python data analysis?" you might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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