IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

Question: 1 / 400

Which of the following describes the process of "imputation"?

Filling gaps in datasets with statistical values

Imputation refers to the process of filling in missing values in datasets with estimated or calculated values, often derived from statistical methods. This technique aims to maintain the integrity of the dataset by ensuring that analyses can be performed without the bias or errors that would arise from having voids in the data. Common statistical approaches for imputation include using means, medians, or modes for continuous or categorical data, respectively, or employing more sophisticated techniques such as regression or interpolation.

This choice is particularly important in data preparation for modeling tasks, as incomplete data can skew results and lead to inaccurate conclusions. Effective imputation helps in retaining the strength of the dataset while allowing for more robust analytical models.

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Analyzing the frequency of data points

Using complex algorithms to process data

Removing incomplete data entries from datasets

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