How to do big data reporting, where is the tutorial?

Big data credit refers to the credit assessment and risk assessment of the loans or installment consumption behaviors applied for by individuals on the Internet to see if there is a risk of default, and once it exists, it will leave a bad record. A score based on a comprehensive evaluation of an individual's multiple pieces of information.

Usually big data credit investigation examines an individual's multiple applications, risk behavior detection, overdue, blacklist, court dishonesty, etc., if you want to see the personal big data credit score, you can go to WeChat: mention check, check it. The higher the big data credit score, the worse the personal credit, and the lower the usual loan applications.

Now the easiest way to check credit, you can print a credit report, or to test personal big data credit, the former can go to the credit information center of the People's Bank of China to print, the latter, go to WeChat: letter friends pigeon analysis, detect big data credit status. Including the individual's overdue status, blacklist situation, credit score, etc.

Too many inquiries will affect personal credit, and inquiries must choose a larger and more reliable platform.

You can decide whether to apply for a mortgage, credit card, or car loan in the near future according to your personal credit.

1. Clear thinking

Defining the purpose and idea of data analysis is the first condition to ensure that the data analysis process is carried out effectively. Its role is to provide clear guidance for the collection, processing and analysis of data. It can be said that the idea is the starting point of the entire analysis process.

First of all, an unclear purpose can lead to a misdirection. When the purpose is clarified, it is necessary to build an analytical framework and decompose the analysis purpose into several different analysis points, that is, how to carry out data analysis, from which angles to analyze, and which analysis indicators to use.

2. Collect data

Data collection is the process of collecting relevant data according to a defined data analysis framework, which provides the material and basis for data analysis. The data mentioned here includes first-hand data and second-hand data, the first-hand data mainly refers to the data that can be obtained directly, and the second-hand data mainly refers to the data obtained after processing and sorting.

3. Processing data

Data processing refers to the processing and sorting of the collected data to form a style suitable for data analysis, which is an indispensable stage before data analysis. The basic purpose of data processing is to extract and derive valuable and meaningful data from a large amount of disorganized, incomprehensible data. Data processing mainly includes data cleaning, data transformation, data extraction, data calculation and other processing methods.

4. Analyze data

Data analysis refers to the process of analyzing the processed data, extracting valuable information, and forming effective conclusions with appropriate analysis methods and tools. Since data analysis is mostly done through software, data analysts are required not only to master various data analysis methods, but also to be familiar with the operation of data analysis software. Data mining is actually an advanced data analysis method, which is to dig out useful information from a large amount of data, which is based on the specific requirements of users, and find out the required information from the vast amount of data to meet the specific needs of users.

5. Present data

In general, data is presented in the form of ** and graphs, and we often say that this is what it means to speak in charts. Commonly used data charts include pie charts, column charts, bar charts, line charts, scatter charts, radar charts, etc., of course, these charts can be further sorted out and processed to make them into the graphics we need.

6. Write a report

The data analysis report is actually a summary and presentation of the entire data analysis process. Through the report, the causes, processes, results and suggestions of data analysis are presented in a complete way for decision-makers to reference. A good data analysis report first needs to have a good analytical framework, and it should be clear and clear to the reader.

In addition, the data analysis report needs to have a clear conclusion, and an analysis without a clear conclusion is not an analysis, and it also loses the meaning of the report, because we are originally analyzing to find or verify a conclusion, so we must not give up the basics. Finally, a good analysis report must have recommendations or solutions.

Big data through 1Integration 2Management 3Analyze to carry out the work.

1.Integrating big data first requires bringing together data from different applications and applications, and traditional data integration mechanisms such as extract, transform, and load (ETL) are often not enough for this task. In other words, we need new strategies and techniques to analyze terabytes and even petabytes of large datasets.

As part of the integration process, you import and process data, perform formatting operations, and organize the data in a form that meets the requirements of a business analyst.

2.Managing big data requires proper storage. Storage solutions can be deployed on-premises or in the cloud.

Second, you can store the data in any form, set the processing requirements for the dataset as needed, and introduce the necessary processing engines. Today, many customers are forced to choose a storage solution based on where their data currently resides. Cloud solutions not only meet customers' current computing needs, but also enable users to quickly access all data on demand.

Let's sail Pei. 3.Analytics will only pay off on big data investments if you truly analyze the data and take effective action based on the data insights. You can visually analyze various data sets to gain new understanding; explore the data further to gain new insights; share your insights with others; Build data models using machine-side learning and artificial intelligence; Act now to unlock the value of your data.

The role of big data:

Now that big data has become a form of capital, the world's big tech companies work on the principles of big data, and most of the value they create comes from the data they have, and they often analyze this data to improve operational efficiency and develop new products. Big data can give you new insights, new business opportunities and business models.

From the perspective of the working principle of big data, the value mining of big data is a complete exploration process, not only data analysis, but also an exploration process.

Data processing --- backend invocation --- front-end display

The difference between the following two methods is that:

1) How big is the impact of the addition of new data (think about the difference between something that is fixed and something that changes).

2) Interactivity is also a key point of influence.

The most important thing is that you have to think about what functional modules are valuable and have something to see on the front end.

1) It can be started from multiple dimensions. For example, a total of 100w data, you can know the total, and then it is gone (this is a dimension). In the division of the date, is it possible to know the number of data for the date (it is more meaningful to look at the total number of friends alone, from a simple total to the total number of each date segment.

These are two dimensions). Then you can choose from multiple regions (depending on the situation in each region.) It's the third dimension).

You also have to use a combination of charts to highlight more obviously.

Differences: 1) The biggest difference is that they do not interfere with each other, and two people can develop a project at the same time.

2) You also have to consider the version (if you originally used it, you changed it to develop.) The version is not the same, and it may not be compatible with this steak).

3) The expansion function is also inconvenient (for integration (the front and back end are not separated), the expansion is relatively simple and convenient, the back end is the back end, and the front end is the front end).

4) Coupling (development efficiency, independence, test scope, etc.).

For example, there are Tomcat and Nginx servers.

Summary. 3.One of the ultimate application fields of big data analysis is sexual analysis, mining characteristics from big data, and through the scientific establishment of models, new data can be brought in through the model, so as to improve future data.

Good afternoon, please wait patiently for a few minutes, we are sorting it out, and we will answer it for you immediately, and please don't end the consultation.

Hello, glad to answer for you. 1.The users of visual analysis big data analysis include big data analysis experts, as well as ordinary rubber users, but the most basic requirement for big data analysis is visual analysis, because visual analysis can intuitively present the characteristics of big data, and at the same time, it can be very easy to be accepted by readers.

2.Data Mining Algorithm The core of Wang Youhe's theory of big data analysis is the data mining algorithm, and the data mining algorithms of various grinding kinds can present the characteristics of the data itself more scientifically based on different data types and formats.

3.One of the ultimate application fields of big data analysis is sexual analysis, mining characteristics from big data, through the scientific establishment of closed-bridge models, and then the round group can bring in new data through the mold car cavity, so as to lead the future data.

4.The diversification of unstructured data in the semantic engine brings new challenges to data analysis, and we need a set of tools to analyze and refine the data. The semantic faith engine needs to be designed to have enough artificial intelligence to proactively extract information from the data.

5.Data quality and data managementBig data analysis is inseparable from data quality and data management, and high-quality data and effective data management can ensure the authenticity and value of analysis results, whether in academic research or commercial applications.

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For big data analysis, you can find [Power Pivot] above Excel and click to open, and click [Manage]. Then find the action tips and import a large number of data sources.

Tools Raw Materials:

ASUS redolbook14

windows 10

excel2019

1. Open Excel**, find [Power Pivot] at the top and click Open, click [Manage]. Then find the action tips and import a large number of data sources.

2. After the import is completed, you will see the [Sales Table] and the [Product Table] to be imported to the Power Pivot backend. Then click [Relationship View] on the home page, and then pull the mouse from [Product Name] to [Product Name], indicating that these two fields are corresponding, so establish a relationship.

3. Then click [Pivot Table], [Pivot Table].

4. Then pull [Product Name], [Sales Quantity], and [Purchase Price] to the corresponding pivot table fields.