Many people know about data analysis, but I think that those who use it well are so. When faced with large amounts of data, we often either process it chaotically or don’t know where to start. This indicates a lack of theoretical knowledge support. Then, with the introduction of data analysis, this article covers commonly used methodologies and ideas in data analysis.
Before introducing the methodology and ideas of data analysis, let’s look at the data analysis process. This can be easily broken down into six steps:
1. Clarify the purpose of the analysis and ask questions.
We can only accurately place the analytical elements, ask quality questions, and provide clear guidance by understanding the purpose of the analysis.
2. Get the data.
Data sources commonly include databases, the Internet, market research, etc., to collect the original data. Using statistical tools from third-party manufacturers is also a good idea.
3. Process your data.
The processing of collected raw data mainly includes methods such as data cleaning, data grouping, data acquisition, and data extraction.
4. Explore your data.
Through exploratory analysis, the formation of hypothetical values is tested, new data features are found, a comprehensive understanding of the entire dataset is obtained, and later analysis strategies can be selected.
5. Analyze the data.
After sorting the data, you need to analyze the data and some related analysis comprehensively. It requires a good understanding of the product, business, technology, etc.; data collection algorithms such as classification and aggregation are often used. Already used. Excel is the most straightforward data analysis tool; professional data analysis tools include BI reporting tools.
