What are the commonly used data analysis techniques?

1. analysis visualization (visual analysis)

Data visualization is the most basic requirement for data analysis tools for both data analysis experts and ordinary users. Visualization can display data intuitively, let the data speak for itself and let the audience hear the results.

2. Data mining algorithm (data mining algorithm)

Visualization is for people, and data mining is for machines. Clustering, segmentation, outlier analysis and other algorithms allow us to dig deep into data and value. These algorithms not only have to deal with large amount of data, but also deal with large data speed.

3. Predictive analysis ability (predictive analysis ability)

Data mining allows analysts to better understand the data, while predictive analysis allows analysts to make some predictive judgments based on the results of visual analysis and data mining.

4. Semantic Engine (Semantic Engine)

As the diversity of unstructured data brings new challenges to data analysis, a series of tools are needed to analyze, extract and analyze data. The semantic engine needs to be designed to be able to derive from? Documents? Intelligent information extraction.

5. Data quality and master data management (data quality and data management)

Data quality and data management are some management best practices. Processing data through standardized processes and tools can ensure predefined high-quality analysis results.