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    Question

    A data analyst is tasked with improving operational

    efficiency by examining employee performance metrics and identifying bottlenecks. Which of the following skills is most essential for successfully accomplishing this task?
    A Knowledge of machine learning algorithms Correct Answer Incorrect Answer
    B Proficiency in data cleaning and transformation Correct Answer Incorrect Answer
    C Expertise in advanced SQL queries Correct Answer Incorrect Answer
    D Experience with dashboard creation in Tableau Correct Answer Incorrect Answer
    E Understanding of statistical modeling Correct Answer Incorrect Answer

    Solution

    For a data analyst tasked with improving operational efficiency by analyzing performance metrics, data cleaning and transformation are the most critical skills. Before any meaningful analysis can take place, raw data often needs to be cleaned and structured properly. This may involve handling missing values, removing outliers, converting data types, and aggregating data to ensure it is consistent and ready for analysis. This foundational step is essential because, without clean, well-organized data, even the most sophisticated analysis would be flawed. A good data analyst must ensure that data is reliable and structured before applying any complex modeling techniques. Additionally, data wrangling skills are necessary to handle unstructured data, making it usable for further analysis. Option A (Knowledge of machine learning algorithms) is incorrect because machine learning is more advanced and typically comes into play once data is clean, and a proper analysis framework is in place. Option C (Expertise in advanced SQL queries) is incorrect because although SQL queries are helpful for data extraction, they are not the most critical skill for cleaning and transforming raw data. Option D (Experience with dashboard creation in Tableau) is important for presenting insights but does not directly help in transforming or cleaning data. Option E (Understanding of statistical modeling) is incorrect because statistical modeling is useful for data analysis but not as essential for data cleaning and transformation.

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