Which processes are typically involved in data analysis?

The processes typically involved in data analysis include inspecting, cleaning, and modeling.

Inspecting refers to examining the data to understand its structure, identifying any patterns, and recognizing any anomalies or outliers that may require further attention. This step ensures that analysts have a clear picture of what the data contains before proceeding.

Cleaning is a critical stage in which the data is prepared for analysis by addressing issues such as missing values, inconsistencies, and errors. This ensures that the analysis will yield accurate and reliable results, as high-quality data is essential for statistical modeling and inference.

Finally, modeling involves applying various statistical or machine learning techniques to the cleaned data to derive insights and make predictions. This step is where the actual analysis happens, allowing analysts to extract meaningful information and improve decision-making processes based on the data.

The other options presented, while they involve some elements related to data, do not accurately represent the core processes typically associated with data analysis.