Big data analysis methods, learning materials, and asking for help

Economics has a set of analytical methods characterized by quantitative analysis. The main methods are: empirical analysis, marginal analysis, equilibrium analysis, static analysis, comparative static analysis, dynamic analysis, long-term and short-term analysis, individual quantity and total analysis, etc.

1. Empirical Analysis:

The empirical method of analysis in economics comes from the philosophical positivist approach. Empirical analysis is a statement that is verified on the basis of facts, and this empirical statement can be reduced to a form that can be proved on the basis of empirical data. When empirical analysis is used to study economic problems, it is necessary to come up with theories that are used to explain the facts, and to make decisions based on them.

This is the process by which economic theories are formed.

2. Marginal analysis:

It is a method of quantitative analysis of economic behavior and economic variables using the concept of marginality. The so-called marginal means extra or additional, that is, the next unit or the last unit that is added. In economic analysis, simply put, the margin refers to every increase or decrease in the original economic aggregate.

Strictly speaking, the margin is the rate of change of the dependent variable when there is a small change in the independent variable.

1.Classify. Classification is a fundamental method of data analysis, according to its characteristics, data objects can be divided into different parts and types, and then further analysis can further explore the essence of things.

2.Regression. Regression is a widely used computational analysis method, which can determine the causal relationship between variables by specifying the dependent variables and independent variables, establish a regression model, and solve the parameters of the model according to the measured data, and then evaluate whether the regression model can fit the measured data well, and if it can fit well, it can be further based on the independent variables.

3.Clustering. Clustering is a classification method that divides data into some aggregate classes according to the connotative nature of the data, and the elements in each aggregate class have the same characteristics as much as possible, and the characteristics between different aggregate classes are as different as possible.

4.Similar matches.

Similar matching is a definite method of accounting for the similarity of two pieces of data, and the degree of similarity is usually measured by a percentage. Similar matching algorithms are used in many different accounting scenarios, such as data cleaning, user input error correction, referral calculation, plagiarism detection systems, active scoring systems, web page lookup, and DNA sequence matching.

5.Frequent itemset.

The APRIORI algorithm is a frequent itemset algorithm that discovers relevant rules, and its core idea is to discover the frequent itemset through two stages: candidate set generation and plot downward closure detection, and has been widely used in business, network security and other fields.

Funnel analysis.

Funnel analysis model is an important method in business analysis, the most common is applied to marketing analysis, because each key node in the marketing process will affect the final result, so in today's refined operation is widely used, the funnel analysis method can help us grasp the efficiency of each conversion node, so as to optimize the entire business process.

Comparative analysis.

Comparative analysis method, also known as comparative analysis method, is to compare two or more interrelated index data, analyze their changes, and understand the essential characteristics and development laws of things.

In data analysis, there are three types of data analysis: temporal comparison, spatial comparison, and standard comparison.

User profiling.

User analysis is the core of Internet operation, and commonly used analysis methods include: activity analysis, retention analysis, user segmentation, user portrait, etc. In the RARRA model just mentioned, user activity and retention are very important links, through the analysis of user behavior data, the optimization of product or web design, and the appropriate guidance of users.

Usually we will monitor the daily active user data, monthly active users and other user activity data to understand the new active user data, to understand whether the product or web page has received more people's attention, but at the same time, we also need to do retention analysis, pay attention to whether the new users are really retained to become fixed users, and the retention data is the real user growth data, in order to reflect the use of the product for a period of time, about the calculation of activity rate and retention rate.

Segmentation analysis.

In today's world where the concept of data analysis is widely valued, it is difficult to really find problems with rough data analysis, and refined data analysis has become a truly effective method, so the subdivision analysis method is more in-depth and refined in the original data analysis.

Indicator analysis.

In practice, this method is the most widely used, and it is also the method of using other methods to analyze and highlight the key points of problems, which refers to the direct use of some basic indicators in statistics to do data analysis, such as mean, mode, median, maximum, minimum, etc. When choosing which underlying indicator to use, you need to consider the orientation of the results.

Data analysis for big data is more of a practical science.

Visual analysis, data mining algorithms, advanced analysis capabilities, semantic engines, data quality and data management, data storage, data warehousing, etc. all need to be mastered.

Many people say that big data analysis requires learning various data analysis methods and data mining models. This is certainly true, but it requires an understanding of the business, and when learning, you need to focus on a lot of knowledge related to business analysis.

A good analyst must be "from the business, to the business".

However, it is very difficult to reach a professional level by self-study, so many people consider choosing the way of training. It allows you to obtain the most effective knowledge in the fastest time.

I measure the quality of the course mainly based on the course content, training mode, and employment services. I've taken a class of Lagou education before.,After watching the open class, I feel that the content is very close to the needs of large factories and there are a lot of projects to attract actual combat.,I just wanted to listen to two classes for nothing.,In the end, I paid for the class.,There is indeed something.,The database is real.,And the teacher will really take you to conquer a few projects.,There will be interview works when communicating with the teacher.。

To be honest, learning this matter is only the key to actual combat, and optical tools and theories are ultimately difficult to implement and even easier to fall into misunderstandings.

1.Actively explore data**: Enterprises can explore more valuable data by collecting and analyzing the external environment, such as policies, industry development trends, market demand, etc., as well as the internal environment, such as customer behavior, business conditions, etc.

3.Conduct data mining: Enterprises can use data mining techniques to uncover valuable information from historical data to meet their data collection needs.

4.Establish lessons learned: Companies can establish lessons learned to collect data in a systematic way and update it regularly for better analysis.

5.Use Social**: Businesses can use Social** to collect valuable data information to meet their data collection needs.

Solutions to problems such as difficult data collection, insufficient data analysis, and strong subjectivity.

Actively explore data**: Enterprises can explore more valuable data by collecting and analyzing the external environment, such as policies, industry development trends, market demand, etc., as well as the internal environment, such as customer behavior, business conditions, etc. 2.

Carry out data mining: Enterprises can use data mining techniques to uncover valuable information from historical data to meet the needs of enterprise data collection. 4.

Establish lessons learned: Companies can establish lessons learned to collect data in a systematic way and update it regularly for better analysis. 5.

Use Social**: Enterprises can use Social** to collect valuable data information to meet their data collection needs.

Some elderly people do not know the solution of intelligent operation.

Hello, some elderly people do not know the solution of intelligent operation 1Provide special smart devices for the elderly to simplify the operation process, such as voice-controlled smart home systems, which can be easily operated by the elderly. 2.

Provide easy-to-understand operation guides, such as providing **guidance or **examples, so that the elderly can easily understand the operation process. 3.Install a smart home system to allow the elderly to remotely control various smart imitation devices at home with their mobile phones, which is more convenient to use.

4.Establish a special technical support service, so that the elderly can get technical support at any time to solve operational problems.

In view of the problem that many elderly people are reluctant to go out due to the epidemic, and offline social activities are difficult.

Hello:1Establish an online communication platform:

You can set up an online chat room, invite the elderly to participate, and use the best conference software to allow the elderly to communicate online, exchange experiences, share feelings, and various games. 2.Launched the Learning Course for the Elderly:

Specially launched online learning courses for Lao Li Jikuren to encourage them to learn new skills, such as online shopping, online social networking, computer operation skills, etc., so that they can better adapt to the era of online social networking. 3.Carry out networking activities:

For the elderly, online calligraphy and painting competitions, painting competitions, poetry recitation competitions and other activities can be carried out, so that they can play freely, show themselves, increase their interest in participating in activities, and improve their social status. 4.Establish an online service center for the elderly:

The data analysis of big data can provide direction for business decision-makers.

1.Trend analysis

A method of analysis in which two or more indicators or ratios are compared in order to calculate the direction, amount, and magnitude of their increases and decreases.

2.Comparative analysis.

Compare two or more indicators to find patterns. Static comparison, horizontal comparison of different indicators. Dynamic comparison, longitudinal comparison of the same indicator.

3.Multidimensional decomposition method

Put a product or a market phenomenon into a spatial coordinate of more than two dimensions for analysis.

4.User segmentation

Divide according to the degree of interaction between users and products to better manage users.

5.Users scrutinize

User sampling, specifically observe the user's characteristic data on behavior and transactions, to observe whether it has significant characteristics, extrapolate macro data, and find out data rules.

6.Funnel analysis.

Divide the business process nodes, establish the conversion funnel of the entire business process, and track and analyze.

7.Retention analytics

After a user registers, track the user's activity for the next week and month.

8.AB test

The essence of the a b test is a controlled test, i.e., the optimal solution is selected by comparing several different versions.

What about data analysis being difficult? 8 major analysis methods to help you, Qingteng will share with you here. If you have a keen interest in big data engineering, hopefully this article can help you.

Big Data Analysis: Methods and Applications is a book published by Tsinghua University Press in 2013. This book introduces the theories, methods, and tools related to big data analysis in data mining, statistical learning, and pattern recognition.

What is the most important ability of big data mining and analysis, the students gave a variety of answers. Students are very interested in the analysis of "** Big Data Analysis Technology", "What is the Big Data Analysis Process", "Eighteen Tools for Big Data Analysis", and "12 Employment Directions of Big Data Analysis", but they don't know much about the methods of big data analysis. In the field of big data mining and analysis, the most important capabilities in the field of data mining and analysis are:

The four most commonly used data analysis methods are descriptive, diagnostic, alphabetical, and directive.

It is necessary to use some tools to help people better understand the importance of data analysis in mining the value of data. One of these tools is called the four-dimensional analysis method. In a nutshell, analysis can be divided into 4 key methods.

1. Descriptive Analysis: What Happened?

This is the most common method of analysis. In business, this approach provides big data analysts with important metrics and a measure of the business. For example, monthly revenue and loss bills.

Data analysts can use these bills to access large amounts of customer data. Understanding the geographic information of your customers is one of the "descriptive analytics" approaches. Visualization tools can be used to enhance the information provided by descriptive analytics.

2. Diagnostic Analysis: Why Does It Happen?

The next step in descriptive data analytics is diagnostic data analysis. By evaluating descriptive data, diagnostic analytics tools enable data analysts to drill down into the data and drill down to the core of the data. A well-designed BI Dashboard is able to integrate:

Functions such as data read, feature filtering, and drill-through data by time series are used to better analyze data.

3. Type Analysis: What Could Happen?

Type analysis is mainly used to perform. The probability of an event occurring in the future, a quantifiable value, or an estimate of the point at which something will happen can all be done with a model. Models typically use a variety of variable data to achieve this.

The diversity of data members is closely related to the results. In an uncertain environment, it can help make better decisions. Models are also an important approach that is being used in many fields.

Big data analytics methods.

4. Directive Analysis: What Needs to Be Done?

The next step in data value and complexity analysis is imperative analysis. The instruction model is based on the analysis of "what happened", "why it happened", and "what could have happened" to help the user decide what action should be taken. Typically, directive analysis is not a method to be used alone, but rather the analysis method that needs to be completed last after all the previous methods have been completed.

For example, a traffic planning analysis takes into account factors such as the distance of each route, the speed at which each route is traveled, and current traffic regulations to help choose the best route home.