Q2 What is the primary goal of data cleaning (aka data munging)?

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BlackTom AI · Accepted Answer

The prompt presents a question about the primary goal of data cleaning (data munging) and offers several answer choices. We will evaluate each option to understand why it is or isn’t aligned with data cleaning concepts.
Option 1: 'Creating complex data schemas for databases.' This describes designing database schemas, which is more about data modeling and database design rather than the cleaning and preprocessing of existing data. It may involve schema decisions, but it is not the core aim of data cleaning, which focuses on making data usable rather than structuring databases.
Option 2: 'Transforming raw data into a more structured or usable format.' This accurately captures the essence of data cleaning. The process typically includes handling missing values, correcting inconsistencies, standardizing formats, and restructuring data so it can be effectively analyzed or fed into downstream processes. This option aligns with the standard definition of data munging.
Option 3: 'Performing data visualization to communicate insights.' Data visualization is an analytical step used after data cleaning to present results, not the cleaning process itself. While cleaned data supports visualization, visualization is a separate activity focused on presentation rather than purification.
Option 4: 'Applying machine learning models to identify trends.' This describes a modeling or analytics step that follows data preparation. While cleaned data is essential for modeling, applying models to identify trends is not the goal of data cleaning itself; it represents a later stage in the data science pipeline.
In summary, the option describing transforming raw data into a more structured or usable format directly corresponds to the purpose of data cleaning, whereas the other options refer to database design, visualization, or modeling—distinct activities in the data workflow.

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Q2 What is the primary goal of data cleaning (aka data munging)?

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Data cleaning usually focuses on fixing of data while data standardization usually focuses on fixing of data.