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Data visualization plays a crucial role in helping analysts and decision-makers interpret large amounts of data by visually representing patterns, trends, and relationships. It transforms complex datasets into graphical formats such as bar charts, histograms, scatter plots, and heat maps, making it easier to understand correlations, outliers, and distributions. The key purpose of visualization is to provide insight and make data more accessible and interpretable, especially when dealing with large or complex datasets. Visualization tools like Matplotlib, Seaborn, Power BI, and Tableau allow for deeper understanding and better decision-making. Why Other Options Are Incorrect: • A: Data visualization goes beyond just creating graphs for reports. It’s about understanding data patterns, which can guide further analysis. • C: While bar charts and pie charts are part of data visualization, modern tools allow for more advanced visualizations like heat maps, scatter plots, and interactive dashboards. • D: Visualization does not replace statistical analysis; it complements it by helping to identify trends and outliers that guide statistical analysis. • E: Creating reports is a different task, and while data visualization can be part of reports, its role is to aid understanding and analysis, not to focus on text-based descriptions.
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