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3 cards from this section. Quick, good the night before a test.
What is data wrangling?
Think about how you would prepare data for analysis.
Why is handling missing data important?
Consider the implications of missing information.
What is the primary goal of data wrangling?
Think about what steps are involved before analysis can happen.
True or False: Cleaning data is the only aspect of data wrangling.
Consider the various processes involved in data preparation.
Imagine you have a dataset with 1000 entries, of which 250 have missing values. How would you approach cleaning this data to prepare for analysis?
Think about the trade-offs between data loss and accuracy.
Discuss an instance where outlier values in a dataset could mislead an analysis. What steps would you take to prevent this?
Consider methods you’ve learned about detecting outliers.
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