Practice What is Data Wrangling? - 2.1.1 | 2. Data Wrangling and Feature Engineering | Data Science Advance
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2.1.1 - What is Data Wrangling?

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define data wrangling in your own words.

💡 Hint: Think about how raw data needs to be prepared for further analysis.

Question 2

Easy

What is imputation?

💡 Hint: Consider methods like using averages or other statistical means.

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 data wrangling?

  • Cleaning and transforming data
  • Analyzing data
  • Storing data

💡 Hint: Focus on what 'wrangling' implies about the data process.

Question 2

Which of the following is NOT a step in data wrangling?

  • Removing duplicates
  • Predicting outcomes
  • Handling missing values

💡 Hint: Think about the primary goal of data wrangling.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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.

💡 Hint: Consider the implications of each method on your dataset size and analysis.

Question 2

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.

💡 Hint: Consistency is key; uniform data formats can save time during analysis.

Challenge and get performance evaluation