Practice Understanding Data Wrangling - 2.1 | 2. Data Wrangling and Feature Engineering | Data Science Advance
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is data wrangling?

πŸ’‘ Hint: Think about how you would prepare data for analysis.

Question 2

Easy

Why is handling missing data important?

πŸ’‘ Hint: Consider the implications of missing information.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the primary goal of data wrangling?

  • To analyze data quickly
  • To clean and transform data
  • To visualize data

πŸ’‘ Hint: Think about what steps are involved before analysis can happen.

Question 2

True or False: Cleaning data is the only aspect of data wrangling.

  • True
  • False

πŸ’‘ Hint: Consider the various processes involved in data preparation.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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?

πŸ’‘ Hint: Think about the trade-offs between data loss and accuracy.

Question 2

Discuss an instance where outlier values in a dataset could mislead an analysis. What steps would you take to prevent this?

πŸ’‘ Hint: Consider methods you’ve learned about detecting outliers.

Challenge and get performance evaluation