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In a Data Science Lab setting, the first step to address the requirement is to organize the dataset efficiently. Assuming we have a dataset with students’ marks across four subjects—Mathematics, Science, English, and Social Studies—we begin by computing the required summary statistics: Average, Minimum, Maximum, and Standard Deviation for each subject. Using tools like Python with pandas, R, or a visualization platform such as Tableau or Power BI, we can generate these values quickly. For example, in Python, the `df.describe()` method provides the mean, min, max, and standard deviation for all numeric columns. Once calculated, these statistics can be formatted into a table view where each row represents a subject, and each column represents one of the statistical measures. This table provides a quick overview of overall performance, variability, and extremes in student scores, making it a key component for data-driven insights. After preparing the summary table, the next step is to design the dashboard. Dashboards are central in Data Science Labs for monitoring, reporting, and analysis, providing multiple visualizations in a single view for quick comprehension. The first visualization is a bar chart depicting subject-wise averages. Each bar represents a subject, with its height corresponding to the average score. This visualization allows easy comparison of performance across subjects and quickly identifies strong or ______ ____ ___ _______ __________ ______ _____ ____ ___ ________ _________ ____.
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