What are the three main stages of data analysis?

The three main stages of data analysis focus on the systematic processes that help transform raw data into meaningful insights. Data preparation, the first stage, involves gathering and organizing data to ensure that it is suitable for analysis. This includes cleaning the data to remove inconsistencies, handling missing values, and formatting the data appropriately.

The second stage, data analysis, is where the actual analytical procedures are conducted. This might involve statistical analysis, applying different algorithms, or using various analytical tools to uncover patterns and trends within the data.

Finally, data interpretation is crucial as it involves translating the findings from the analysis into understandable insights. This step requires the analyst to communicate the results effectively, providing context and relevance to stakeholders, which can drive informed decision-making.

The other options do comprise components related to data analysis, but they do not encapsulate the three main stages as accurately as the correct choice does. For example, while data visualization is an important aspect of presenting data, it is part of the interpretation stage rather than a standalone stage. Similarly, data reporting falls under communication of results rather than being a stage in itself.