Data analysis and visualization sometimes overlap partially.
For example, there is analysis as preparation for visualization: In order to ensure that the visualization reliably provides the information you are looking for, it may be necessary to investigate more thoroughly where the data comes from and what the numbers mean. The ideal information is specific, accurate, and representative.
On the other hand, there is visualization for analysis: Information visualization is often a part of data analysis. The story behind the numbers becomes clearer when they are visualized. This way, you can more easily compare subgroups, discover trends, see developments over time, or identify correlations.
Statistics: For data analysis, I use descriptive and explanatory statistical methods that are relevant to your data. For data analysis, I use software such as Microsoft Excel, SPSS, and R.
Of course, there are also projects where data analysis is not necessary.
Depending on your question and needs, the data analysis step may be very extensive or almost unnecessary.