Statistical analysis method of big data actual combat data
Data analysis is the process of extracting valuable information from data. During the process, data needs to be processed and classified in various ways. Only by mastering the correct data classification methods and data processing modes can you achieve multiplier effects with half the effort. The following is the data analysis. 9 data analysis mindsets that every employee must have :
1. Classification
Classification is a basic data analysis method. According to its characteristics, data objects can be divided into different parts and types, and further analysis can further explore the essence of things.
2. Return
Regression is a widely used statistical analysis method, which can determine the causal relationship between variables by specifying 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 be very good. If it fits the measured data well, if it fits well, it can make further predictions based on the independent variables.
3. Clustering
Clustering is a classification method that divides data into some aggregation classes according to the inherent nature of the data. The elements in each aggregation class have the same characteristics as possible, and the characteristics between different aggregation classes are as different as possible. Differently, the classified class is unknown, therefore, cluster analysis is also known as unsupervised or unsupervised learning.
Data clustering is a technique for static data analysis and is widely used in many fields, including machine learning, data mining, pattern recognition, image analysis, and bioinformatics.
4. Similarity matching
Similarity matching is to calculate the degree of similarity of two data through a certain method, and the degree of similarity is usually measured by a percentage. Similarity matching algorithms are used in many different computing scenarios, such as data cleaning, user input error correction, recommendation statistics, plagiarism detection systems, automatic scoring systems, web search, and DNA sequence matching.
5. Frequent itemsets
Frequent itemsets refer to the collection of frequently appearing items in cases, such as beer and diapers. Apriori algorithm is a frequent itemset algorithm for mining association rules. Its core idea is to generate candidate sets and detect downward closure of plots. Two-stage mining of frequent itemsets has been widely used in business, network security and other fields.
6. Statistical description
Statistical description is based on the characteristics of the data, using certain statistical indicators and indicator systems to indicate the information fed back by the data, which is the basic processing work for data analysis. The main methods include: calculation of average indicators and variation indicators, and data distribution patterns performance etc.
7. Link prediction
Link prediction is a method of predicting the relationship that should exist between data. Link prediction can be divided into prediction based on node attributes and prediction based on network structure. Link prediction based on attributes between nodes includes analyzing node qualifications. Attributes and the relationship between the attributes of the nodes and other information, using the node information knowledge set and node similarity and other methods to obtain the hidden relationship between the nodes. Compared with link prediction based on node attributes, network structure data is easier to obtain. A major point of view in the field of complex networks is that the characteristics of individuals in a network are less important than the relationships between individuals. Therefore, link prediction based on network structure has received more and more attention.
8. Data compression
Data compression refers to reducing the amount of data to reduce storage space and improve its transmission, storage and processing efficiency without losing useful information, or to reorganize data according to certain algorithms to reduce data redundancy and storage space. a technical method. Data compression is divided into lossy compression and lossless compression.
9. Causal Analysis
The causal analysis method is a method of forecasting by using the causal relationship of the development and changes of things. The causal analysis method is used for market forecasting, mainly using the regression analysis method. In addition, methods such as computational economic models and input-output analysis are also relatively Commonly used.
The above are 9 data analysis thinking methods that data analysts should be proficient in . Data analysts should use different methods reasonably according to the actual situation to be able to quickly and accurately mine valuable information! The above methods are reflected in the Old Boys Education Big Data Development Course. If you want to study in depth, you can apply for the Old Boys Education Big Data Training Course!