The built-in statistical analysis is too simple and has little practical value.The Excel data file itself can hold only 1.08 million rows without the aid of other tools, and it's not suitable for processing large-scale data sets.When the amount of data is large, there will be a situation of stuttering.To fully master Excel, you need to learn VBA, so the difficulty is still very high.It can help you understand the meaning of many operations before further learning other tools (such as Python and R).You can do a lot of things with Excel: modeling, visualization, reports, dynamic charts, etc.Production of charts for some business magazines and newspapers (data visualization).Combine Word and PowerPoint to create data analysis reports.Simple statistical analysis for students or teachers (such as analysis of variance, regression analysis, etc.).Data management and storage of small and medium-sized companies.Data processing work under general office requirements.Here, I will compare the four tools that are most popular with data analysts, Excel, R, Python, and BI, as the basis for getting started with data analysis.
TOOLS FOR DATA ANALYSIS USING R PROFESSIONAL
But you may not have the professional knowledge of data analysis and programming, or you have learned a lot about the theory of data analysis, but you still can't practice it. From the state, government, and enterprises to individuals, big data and data analysis have become trends that everyone is familiar with. The era of data analysis has already arrived.