Practice What Is Data Wrangling? (2.1.1) - Data Wrangling and Feature Engineering

Practice: What is Data Wrangling?

Lesson 2.1.1Data Wrangling and Feature Engineering

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10 questions

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4 cards from this section. Quick, good the night before a test.
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Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
Define data wrangling in your own words.
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Think about how raw data needs to be prepared for further analysis.
Question 2.
What is imputation?
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Consider methods like using averages or other statistical means.
3Question 3.
What is data wrangling?
- Cleaning and transforming data
- Analyzing data
- Storing data
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Focus on what 'wrangling' implies about the data process.
4Question 4.
Which of the following is NOT a step in data wrangling?
- Removing duplicates
- Predicting outcomes
- Handling missing values
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Think about the primary goal of data wrangling.
Question 5.
You are provided a dataset containing various fields with missing values scattered throughout. Describe a systematic approach to address these missing values while ensuring minimal data loss.
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Consider the implications of each method on your dataset size and analysis.
Question 6.
You’ve received customer feedback data where some entries are duplicated, and others have inconsistent formatting (e.g., dates). Create a plan on how you would clean this data for a reliable analysis.
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Consistency is key; uniform data formats can save time during analysis.

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