What are the methods of data analysis?

2 data analysis In order to find more problems and find out the reasons;

3 data analysis can't sit.

2. Steps: ① Investigation: Collecting, analyzing and mining data.

(2) Chart analysis: chart the results of analysis and mining.

3. Common methods: Data analysis methods commonly used in data mining mainly include classification, regression analysis, clustering, association rules, characteristics, change and deviation analysis, web page mining, etc. They mine data from different angles. ① classification. Classification is to find out the similarities and differences of a group of data objects in the database and divide them into different classes according to the classification method. Its purpose is to map the data items in the database to a given category through the classification model. It can be applied to customer classification, customer attribute and characteristic analysis, customer satisfaction analysis, customer purchase trend prediction and so on. For example, a car retailer divides customers into different categories according to their preferences for cars, so that marketers can directly mail advertising brochures for new cars to customers with such preferences, thus greatly increasing business opportunities. ② Regression analysis. Regression analysis method reflects the time characteristics of attribute values in transaction database, generates a function to map data items to real-valued predicted variables, and finds the dependency between variables or attributes. Its main research problems include the trend characteristics of data series, the prediction of data series and the correlation between data. It can be applied to all aspects of marketing, such as customer seeking, maintaining and preventing customer churn, product life cycle analysis, sales trend prediction and targeted promotion activities. ③ Clustering. Cluster analysis is to divide a group of data into several categories according to similarity and difference, and its purpose is to make the similarity between data belonging to the same category as large as possible and the similarity between data belonging to different categories as small as possible. It can be applied to customer group classification, customer background analysis, customer purchasing trend prediction, market segmentation and so on. ④ Association rules. Association rules are rules that describe the relationship between data items in a database, that is, according to the appearance of some items in a transaction, other items also appear in the same transaction, that is, the association or mutual relationship hidden between data. In customer relationship management, by mining a large number of data in enterprise customer database, we can find interesting relationships from a large number of records, find out the key factors that affect the marketing effect, and provide reference for decision support such as product positioning, customer group pricing and customization, customer seeking, segmentation and maintenance, marketing and promotion, marketing risk assessment and fraud prediction. ⑤ Characteristics. Feature analysis is to extract feature expressions about a set of data from a database, which express the overall characteristics of the data set. For example, by extracting the characteristics of customer churn factors, marketers can get a series of reasons and main characteristics that lead to customer churn, and these characteristics can effectively prevent customer churn. ⑥ Analysis of variation and deviation. Deviation includes a large class of potentially interesting knowledge, such as abnormal examples in classification, abnormal patterns, deviation between observed results and expectations, etc. Its purpose is to find the meaningful difference between the observation result and the reference quantity. In enterprise crisis management and its early warning, managers are more interested in those unexpected laws. The mining of unexpected rules can be applied to the discovery, analysis, identification, evaluation and early warning of various abnormal information. ⑦ Web mining.