IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

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Which method is commonly used to handle missing data in datasets?

Aggregation

Interpolation

Imputation

Imputation is a widely recognized technique for addressing missing data in datasets. It involves filling in the missing values with substituted values based on other available information within the dataset. This can be achieved through various strategies, such as replacing missing entries with the mean, median, or mode of the observed values. More sophisticated techniques can also involve using predictive models that analyze existing data to forecast the missing values.

The reason imputation is often favored is that it enables the retention of all the available data rather than eliminating records with missing values, which could lead to loss of information and potentially bias the results of any analysis. By effectively addressing the gaps in the dataset, imputation allows for a more robust and comprehensive analysis, ensuring that the insights derived are as accurate and reliable as possible.

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Estimation

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