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3 cards from this section. Quick, good the night before a test.
Define what is meant by 'removing duplicates' in a dataset.
Think about how multiple entries of the same data could impact your results.
What is imputation in data wrangling?
Remember the different methods of handling missing values that were discussed.
What is the purpose of removing duplicates in data wrangling?
Think about how duplicated rows can mislead results.
True or False: Imputation can only be done by removing rows with missing data.
What are the various options available for handling missing values?
You have a dataset with the following columns: Name, Age, Weight, Height, and some rows with missing values for Age. Describe the steps you would take to prepare this dataset for modeling.
Think through the procedures in order from the information we learned.
In a dataset of test scores, an outlier stands out—one score is 25% higher than the next closest score. Discuss how you would evaluate and treat this outlier.
Recall the techniques for outlier treatment discussed.
4 more questions in this section. Enroll free to practise them all.
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