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Data wrangling, also known as data munging, is the process of cleaning, structuring, and transforming raw data into a format suitable for analysis. It includes tasks such as handling missing values, correcting data inconsistencies, and reshaping data, which are essential steps before performing any analysis. Proper data wrangling ensures that datasets are accurate, complete, and compatible with analytical methods, thereby enabling reliable results. This preparatory stage is foundational to any data-driven project, as quality data directly impacts the insights and conclusions drawn from the analysis. The other options are incorrect because: • Option 1 (Generating insights from visualizations) is part of data interpretation, not wrangling. • Option 2 (Applying machine learning) is part of modeling, which occurs after data wrangling. • Option 4 (Optimizing storage) is a data engineering task, unrelated to data wrangling. • Option 5 (Validating hypotheses) is part of analysis, not the initial data preparation process.
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