You are tasked with analyzing sales data from multiple

Solution

Data cleaning is a critical early step in the analysis process. Without clean and accurate data, any insights derived from the analysis will be unreliable. Cleaning involves removing duplicates, handling missing values (e.g., using imputation techniques), and ensuring consistency. This step ensures the foundation of the analysis is robust.

• Option A : Building predictive models before cleaning the data can lead to biased or inaccurate results.
• Option B : Exploratory analysis comes after cleaning the data to ensure trends reflect reality.
• Option C : While partially correct, removing duplicates and averaging missing values may not always be the best method for handling these issues.
• Option E : Visualizations should be created only after cleaning and analyzing the data for accurate representation.