Data analysis, what exactly is the data being analyzed?

When it comes to data analysis, there are a lot of data sources that can be used. According to the general classification, it can be divided into three categories: external data, internal enterprise asset data, and survey data.

1. External data.

1) Data from the National Bureau of Statistics.

The most frequently used external data is the data of the National Bureau of Statistics, which includes many aspects of China's economy and people's livelihood, and can be viewed from monthly, quarterly and annual time dimensions, which is highly authoritative. At work, I often look for sales and output data of related commodities in the data of the Statistics Bureau.

2) Index data.

One of the products of conscience. It can help you understand a certain topic and the situation that has received attention in a certain period of time, so as to have a good guiding role in trend analysis, **, and of course, some accurate analysis of crowd portraits.

3) Ali Index.

The trading index data platform based on Ali products such as ** and Tmall is a relatively authoritative commodity trading analysis tool in China, which has reference significance for industry trend insight.

4) iQIYI Index.

5) TBI index.

6) Other transaction cooperation data.

This part of the data mainly comes from business cooperation or purchase data, such as Datatang transaction data.

2. Internal enterprise asset data.

The company's product accumulation data, including the company's product operation data, financial data, and other related company's own asset data. For example, if a company mainly operates a well-known app, the app operation data is its internal asset data. With the help of internal data, the internal operation status of the enterprise can be analyzed.

3. Survey data.

Data is collected through questionnaires, usually on a business topic.

Big data has now become a very hot word, it is said that now is the era of big data, as a major in applied statistics, to a certain extent, data analysis is indeed very useful, but what kind of data should we analyze, how to analyze data? What methods of data analysis are the application expertise?

As for what data to analyze, it depends on the question you want to study! It is important to analyze the data that is relevant to the problem you are studying, and if it is valid, it is the data obtained by doing a questionnaire on the Internet or looking for information, otherwise it will be meaningless for you to analyze the data, and I will give you two examples.

First, if you want to study what issues college students are concerned about, you need to analyze the data of different grades, genders, majors, and so on. This is what I wrote when I graduated from university**, which mainly uses descriptive statistical methods and independence tests, and uses SPSS software to analyze, of course, after you collect data, you must do preprocessing, otherwise it is not easy to analyze. Second, if you want to study the consumption level of a certain city in a certain year, this requires you to investigate how much the residents of the city consume every month in this year, what things are consumed, clothing, food, housing and transportation, this survey workload is still quite large, you can ask for the data from the management of the city, they should give it to you, and then directly analyze it, don't blindly conduct data analysis.

In short, data analysis is the analysis of data based on the problem you are trying to solve.

Data analysis is mainly for all kinds of data that exist in our current life, including the consumption data of our daily life, the yield rate or finished product output rate of the products produced by the enterprise, or the probability of success when we do something, and so on, and then collect and classify the possible results and trends through the changes in the data.

Because we are currently in an era of very developed information technology, everyone has their own computer, and everyone can not do without the Internet on mobile phones. Some of the things we do every day, such as when we go to the merchant store to shop, and pay with Alipay or other means, our consumption data is generated. Merchants can then use these consumption data to infer which class of their consumer groups they belong to.

It can make a corresponding adjustment to its own goods according to the income of its own consumer group.

There is also the fact that when the enterprise produces products, the finished products produced every day and the yield rate of the products. Together, these data are combined. It can be seen whether there is a big problem with his production line.

If he produces finished products and the yield rate of finished products every day, stabilize at a stable figure. Then there's nothing too big of a problem. But because he inspects his own factory to ensure normal production and operation.

In terms of large countries, the country's annual gross domestic product, per capita income level and annual total grain production, as well as the index of people's living standards, and the amount of external income. The state's various taxes and various investments in education, medical and health care, and the military can be counted through the current big data, and then it is convenient for the management to analyze and make some corresponding adjustments for different problems.

Data analysis refers to the analysis of a large amount of collected data with appropriate statistical analysis methods, and summarizes, understands and digests them, so as to maximize the function of the data and give full play to the role of the data. Data analysis is the process of studying and summarizing data in detail in order to extract useful information and form conclusions.

The mathematical foundations of data analysis were established in the early 20th century, but it wasn't until the advent of computers that practical operations were made possible and data analysis was generalized. Data analytics is a product of the combination of mathematics and computer science.

The purpose of data analysis is to concentrate and refine the information hidden in a large number of seemingly disorganized data, so as to find out the internal laws of the object of study. In practice, data analytics can help people make judgments so that appropriate action can be taken. Data analysis is the process of collecting data in an organized and purposeful manner, analyzing it, and turning it into information.

This process is supported by the quality management system. Data analysis processes are required throughout the entire product life cycle, from market research to after-sales service and final disposal, to improve effectiveness. For example, before starting a new design, designers need to analyze the data obtained through extensive design surveys to determine the design direction, so data analysis has an extremely important position in industrial design.

As the name suggests, data analytics is all about analyzing data. However, this explanation seems to be equivalent to not saying, so what is the concept of data analysis professionalism?

Data analysis refers to the process of using appropriate statistical analysis methods to analyze the collected data, extract useful information and form conclusions, and then study and summarize the data in detail, and finally apply the conclusions obtained to solve practical problems in the industry.

In other words, data analysis is to concentrate and refine information in a batch of seemingly useless or disorganized data, and summarize the internal laws of the research object.

The purpose of data analysis is to concentrate and refine the information hidden behind a large number of seemingly disorganized data, summarize the internal laws of the research object, and help managers make effective judgments and decisions.

To put it simply, data analysis is all about analyzing data.

In more technical terms, data analysis refers to the use of appropriate statistical analysis methods to classify a large amount of data collected.

Analyze, summarize and understand and digest them in order to maximize the development of data functions and play the role of data.

Data analysis. It is a process of collecting, sorting, processing, and analyzing data for commercial purposes, and refining valuable information.

The process can be summarized as follows:

1.Clarify the purpose and framework of the analysis;

2.data collection;

3.Data processing;

4.Data analysis, 5Data presentation and reporting.

Keduoda focuses on the cultivation of big data talents.

1) Narrow perspective: Extract valuable information based on data and according to specific analysis ideas and frameworks. Data analysis in the narrow sense can be based on modeling, metric-based calculations, or data visualization reports.

2) Broad perspective: It includes data engineering and data processing, such as defining data models, data architecture, data processing, writing SQL calculation indicators, etc.

The purpose of data analysis is to concentrate and refine the information hidden behind a large number of seemingly chaotic data, summarize the internal laws of the research object, and help managers make effective judgments and decisions.

1. Measurement of total scale

The total index, also known as the absolute number of statistics, is an indicator that reflects the overall scale and total amount of a certain data. He manages the grouping and summarization of the raw data to obtain the total figures, which is a direct success in the statistical collation stage.

2. Relative measurement

The relative index is an index that illustrates the quantitative contrast between phenomena, which is obtained by comparing the values of two related indicators, and the result is a relative number, and the important feature of the relative number is to summarize the two specific values into an abstract number.

3. Measurement of concentrated trends

A concentrated trend is an indicator that reflects the general level reached by a phenomenon over a certain period of time. It is expressed in terms of average indicators. Average metrics are divided into numerical average and position average.

4. A measure of the degree of dispersion

The variation index is used to represent the variation and dispersion of the overall distribution of the index, through the degree of variation can also see the representativeness of the average index, if the dispersion degree is small, indicating that most of the data are next to each other, the average value can well reflect the general level of the overall situation, and vice versa.