Practice Common Data Wrangling Steps - 2.1.3 | 2. Data Wrangling and Feature Engineering | Data Science Advance
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2.1.3 - Common Data Wrangling Steps

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define what is meant by 'removing duplicates' in a dataset.

💡 Hint: Think about how multiple entries of the same data could impact your results.

Question 2

Easy

What is imputation in data wrangling?

💡 Hint: Remember the different methods of handling missing values that were discussed.

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 purpose of removing duplicates in data wrangling?

  • To save space
  • To ensure accuracy
  • To create more data

💡 Hint: Think about how duplicated rows can mislead results.

Question 2

True or False: Imputation can only be done by removing rows with missing data.

  • True
  • False

💡 Hint: What are the various options available for handling missing values?

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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.

💡 Hint: Think through the procedures in order from the information we learned.

Question 2

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.

💡 Hint: Recall the techniques for outlier treatment discussed.

Challenge and get performance evaluation