Data analysis terms are explained as follows:

1. Data: data is the result of facts or observations, which can be in the form of numbers, text, images, audio or video. In data analysis, data is usually represented as numeric or text fields. Dataset: A dataset is a collection of related data, which can be a table in a database, a record in a file, or a measurement collected by a sensor.

2. Variable: a variable is a feature or attribute in a dataset, which can be numeric, textual or categorical data. Data type: Data type is the kind of data, such as integer, floating point, string, and so on. Data preprocessing: Data preprocessing is the process of cleaning, converting and formatting data before data analysis to ensure the quality and consistency of the data.

3. Data Visualization: Data visualization is the conversion of data into a visual form, such as charts, graphs, or charts, to make it easier to understand and interpret. Statistical analysis: Statistical analysis is the process of describing and making inferences about data using statistical methods to extract insights about data distributions and relationships.

4. Machine learning: Machine learning is the process of using algorithms and models to automatically extract knowledge or predict outcomes from data. Predictive modeling: Predictive models are algorithms or models used to predict future outcomes, which can be regression models, classification models, or other types of models.

5. Data Mining: Data mining is the process of automatically discovering valuable information, patterns or trends from a large amount of data, which can be applied to a variety of fields, such as healthcare, finance and so on.

6, big data: big data refers to large amounts of data that are difficult to process using traditional methods, usually involving distributed storage and processing techniques. Data Scientist: Data scientists are professionals responsible for performing data analysis, mining and interpretation, using technology and expertise to extract valuable information from data.

Role of Data Analytics

1. Providing Decision Support: Data analytics can help managers and decision makers make informed decisions based on facts and data. By analyzing large amounts of data, it is possible to identify problem areas, find solutions, and predict future trends. Discover business opportunities and optimize business: Data analytics can help companies discover new business opportunities and optimize existing business.

2. Improve operational efficiency: By analyzing operational data, bottlenecks and problems in the operational process can be identified, so that targeted improvements can be made to increase operational efficiency.

3, enhance market competitiveness: through the analysis of market data, you can understand the situation of competitors, find market gaps and opportunities, so as to develop a more targeted market strategy to enhance market competitiveness.

4, improve customer satisfaction: through the analysis of customer data, you can understand the needs and preferences of customers, so as to provide more in line with customer demand for products and services, improve customer satisfaction.