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5 cards from this section. Quick, good the night before a test.
What does data cleaning entail?
Think about why we need to prepare data.
What is the purpose of handling missing data?
Consider what missing values can cause.
What does data cleaning ensure?
Consider the main goals of data cleaning.
True or False: Normalization transforms data into a range from 0 to 1.
Think about how the extremes of the data are affected.
Given a dataset with significant missing values in multiple columns, outline a strategy to address missing data efficiently while retaining the dataset’s integrity.
Think about how much missing data is acceptable and how best to preserve data utility.
You are modeling income data that has extreme outliers. Describe the steps you would take to handle these outliers before proceeding with the analysis.
Consider both numerical results and visual assessments.
6 more questions in this section. Enroll free to practise them all.
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