What are the basic methods of data analysis

The basic methods commonly used in data analysis are the list method and the graph method. The list method is to express the data in a list according to a certain rule, which is the most commonly used method for recording and processing data. The drawing method can clearly express the change relationship between various physical quantities.

Here's how:

1. Descriptive statistics: Descriptive statistics is a method of sorting out and analyzing data through charts or mathematical methods, and estimating and describing the distribution status of data, numerical characteristics and the relationship between random variables. Descriptive statistics can be divided into three parts: centralized trend analysis, neutral trend analysis and correlation analysis.

2. Hypothesis test: Parameter test is a test of some main parameters under the condition of known population distribution.

3. Reliability analysis: reliability refers to the degree of consistency of the results obtained when the same method is used to repeatedly measure the same object.

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Data analysis. Three common methods:

1.Data trend analysis.

Generally speaking, trend analysis is suitable for long-term tracking of core product metrics, such as click-through rate, GMV, number of active users, etc. Making a simple data trend chart is not a trend analysis, but more about clarifying the changes in the data and analyzing the reasons for the changes.

For trend analysis, the best output is the ratio. There are a few concepts that need to be clarified when analyzing trends: chain comparison.

Year-on-year, fixed base ratio. Month-on-month refers to the comparison of the current period with the previous period, for example, February 2019 with January 2019. In order to eliminate seasonal differences, the concept of year-on-year comparisons was created, such as February 2019 and February 2018.

The fixed basis ratio is better understood by comparing it with a certain basis point, such as January 2018 as the basis point, and the fixed basis ratio is compared with February 2019 and January 2018.

20%。Another core purpose of trend analysis is to explain the trend, for trend lines.

is a significant inflection point.

Give a reasonable explanation for what happened, whether it was external or internal.

2.Comparative analysis of data.

For example, if a company's earnings growth is 10%, we can't judge the quality of the company, if the other companies in the industry in which the company is located are generally negative, then 5% is a lot, and if the growth of other companies in the industry is 50% on average, this is a very poor data.

Comparative analysis is to give a reasonable reference frame for isolated data.

Generally speaking, the data compared is the fundamentals of the data.

For example, the situation of the industry, the situation of the whole site, etc. Sometimes, in order to increase the convincing power of product iteration testing, the benchmark of comparison will be artificially set. That is, a b test.

The most critical thing in the comparative trial was that the two groups A and B kept only a single variable, and other conditions were the same. For example, to test the effect of the homepage revision, it is necessary to keep the quality of the two groups of users in A and B the same, the online time to be the same, and the channels to be the same. Only in this way can we get more convincing data.

3.Data segmentation analysis.

When some preliminary conclusions are obtained, it is necessary to further dismantle them, because in the process of using some comprehensive indicators, some key data details will be erased, and the changes in the indicators themselves also need to analyze the reasons for the changes. The subdivision here must be dismantled in multiple dimensions. Common splitting methods include:

Time-sharing: Whether there is a change in the data at different time shortages.

By user: whether there is a difference between a newly registered user and an old user, and whether there is a difference between a high-level user and a low-level user.

By Region: Whether the data varies in different regions.

Segmentation analysis is a very important means, asking more why, is the key to getting a conclusion, and step by step splitting is the process of constantly asking why.

The basic methods of data analysis are as follows:

1. Trend analysis. It is usually used to track core indicators for a long time, make a simple data trend chart, and see the trend changes in the data, whether it is periodic, or there is an inflection point and the reasons behind the analysis, or internal or external. The best output of trend analysis is ratios, which are month-on-month, year-on-year, and fixed-base ratios.

2. Comparative analysis. The most common data metric is that it needs to be compared to the target value to understand whether the target is being achieved; Find out how the month-on-month growth is compared to the previous month. Data can only be meaningful by comparison.

3. Quadrant analysis. Based on different data, each comparison object is divided into 4 quadrants, which can be divided into two dimensions and four quadrants. With quadrant analysis, it is possible to compare and analyze times to obtain very intuitive and fast results.

4. Cross-analysis. Data is cross-displayed from multiple dimensions and combinatorial analysis is performed from multiple perspectives. The main feature is to segment data from multiple dimensions and find the most relevant dimensions to the reason for the data change.

If you want to know more about the basic methods of data analysis, you can consult the CDA certification center. The CDA industry standard is jointly formulated by industry experts, scholars and well-known enterprises in the field of data on an international scale and revised and updated every year, ensuring that the standard is public, authoritative and cutting-edge. Those who pass the CDA certification exam can obtain the CDA certification certificate in both Chinese and English.

There is big data analysis, and there is also comprehensive data analysis.

According to the methods of analysis, there are many kinds of methods, such as induction, and then there are some abstractions that are his methods.

There are many basic methods of data analysis, first you need to observe the data comprehensively, and then classify the data and analyze it according to various types.

The sorting method of data analysis can be drawn and organized.

The basic methods of data analysis, there are some basic analysis methods in Zhejiang society, which should be able to read the data directly, which should still be relatively rich, and it should still be very good.

What are the basic methods of not analyzing, in fact, this is also a solution for the problem.

The key to data aggregation is precision.

There are four main methods of big data analysis, namely: visual analysis, data mining algorithms, advanced analysis capabilities, data quality and data management.

What exactly are my mathematically analytic stuff? What are the specific methods? You can study statistics, there should be a value of a fraction on it, right?

Generally, there is a specific scenario and purpose before data analysis, and sometimes the analysis method can be selected according to the analysis purpose, so as to carry out data analysis faster. For example, a set of data wants to study whether there are differences in the satisfaction of different genders with shopping malls. Methods such as variance, t-test, chi-square test, etc., may be used, but the choice of method depends on the type of data and the structure.

Before the analysis, we need to select the analysis method and perform simple processing of the data.

When it comes to "analysis methods", many people may be more distressed, they have prepared data but do not know what method to choose, such as whether their data is qualitative or quantitative, whether it meets the requirements of analysis methods, etc. First of all, let's understand what qualitative data and quantitative data are, as follows:

Analytical Method: <>

1. List method:

It is the most common method of recording and processing data to express data in a list according to certain rules. **The design requirements are clear, simple and clear, which is conducive to discovering the correlation between related quantities; In addition, it is required that the name, symbol, order of magnitude and units of each quantity be indicated in the title block.

2. Drawing method:

The graphing method can most prominently express the change relationship between various physical quantities. From the graph line, you can easily find some results required for experiments, and you can also represent some complex functional relationships graphically through certain transformations.

Purpose of data analysis:

1. 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 research object.

2. In practical applications, data analysis can help people make judgments so that appropriate actions can be taken. Data analysis is the process of collecting data in an organized and purposeful manner, analyzing it, and turning it into information.

There are two total points:

1. List method:

The most common method for recording and processing experimental data is to express the experimental data in a tabular way according to certain rules. **The design requires that the correspondence is clear, simple and clear, and is conducive to discovering the physical relationship between related quantities; In addition, it is also required to indicate the name, symbol, order of magnitude and unit of physical quantity in the title bar; You can also list the calculation columns and statistics columns other than the original data as needed. Finally, it is also required to indicate the name, the model, range and accuracy level of the main measuring instrument, and the relevant environmental parameters such as temperature and humidity.

2. Drawing method:

The graphing method can most prominently express the change relationship between physical quantities. It is also possible to easily find certain results necessary for experiments (such as the slope and intercept value of a straight line) from the graph line, and to read out the corresponding points that have not been observed (interpolation method), or to read the corresponding points outside the measurement range from the extension part of the graph line under certain conditions (extrapolation method). In addition, some complex functional relationships can also be represented by linear graphs through certain transformations.

For example, the relationship between resistance and temperature of a semiconductor thermistor is obtained by taking the logarithm, and if you use semi-logarithmic coordinate paper, with LGR as the vertical axis and 1 t as the horizontal axis, it is a straight line.

Data analysis is to process, organize and analyze the collected data to transform it into information, usually using the following methods: The old seven tools, namely the arrangement chart, cause and effect map, hierarchical method, questionnaire, walk chart, histogram, control chart; Seven new tools, namely correlation diagrams, system diagrams, matrix diagrams, KJ methods, plan review techniques, PDPC methods, and matrix data diagrams;

Regression analysis, trend extrapolation, time series decomposition.

Break-even analysis, probability analysis, sensitivity analysis, etc.

If you are doing investment data analysis, you can refer to the "Project Investment Decision Data Analysis Software".

There are many ways to analyze data, and the key is to see what the purpose is. Different purposes require different methods, modeling, and categorization, rather than simple statistics.

Comparative analysisReflecting changes in the number of things through the comparison of indicators is a common method in statistical analysis. The comparative analysis method can be used to make effective judgments and evaluations on the size, level, speed and speed of the data. Common comparisons are horizontal contrast and vertical contrast.

Group analysisGroup analysis method refers to dividing the data into different parts according to certain indicators according to the nature and characteristics of the data, and analyzing its internal structure and interrelationship, so as to understand the development law of things. According to the nature of the index, the grouping analysis method is divided into attribute index grouping and quantitative index grouping. The so-called attribute indicators represent the nature and characteristics of things, such as name, gender, education level, etc., and these indicators cannot be calculated; The data represented by the data indicators can be calculated, such as the age of the person, the salary income, etc.

Grouping analysis is generally used in conjunction with contrastive analysis.

Analytical method**The analysis method is mainly based on the current data, and the future data change trend is judged. Analysis is generally divided into two types: one is based on time series, e.g., sales in the next 3 months based on past sales performance; The other is regression, which is based on the causal relationship between indicators, for example, the goods that a user is likely to buy based on their web browsing behavior.

Funnel analysisAB test analysis

Answer first, comparative analysis, simply put, is to reflect the change relationship of the number more intuitively through the standard comparison of different data, it is a common method, which can be divided into horizontal and vertical two, the former is a fixed time comparison data, such as comparing the purchase amount of goods of different levels of users within a fixed time, the sales performance of different goods, the profit margin and so on. The latter refers to the comparison of changes in time and latitude on the same thing, such as environmental protection, year-on-year comparison, etc., no matter which analysis method, the fundamental purpose is to use analysis to obtain visual and clear conclusions.

Second, the group analysis method refers to the analysis of the characteristics according to the data, dividing the total data into different modules, and making comprehensive and effective judgments on the scale, speed, and level. For example, people can't use the name, gender, and education level of the registered user in the background to do specific analysis, but the data corresponding to these parameters has the basis and possibility of analysis, and a clear user portrait can be obtained after the analysis.

Third, the essence of data analysis is to analyze the existing data in the past and present, and to better predict the future development possibilities, troubles and problems that may be encountered in the relationship between parameters, prepare in advance, and reduce the probability and possibility of risks.

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