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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is data wrangling?
💡 Hint: Think about the steps needed to prepare data for analysis.
Question 2
Easy
Name a common task in data wrangling.
💡 Hint: Consider what challenges data often presents.
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 does 'data wrangling' mean?
- The process of analyzing data
- The process of cleaning and transforming data
- The process of visualizing data
💡 Hint: Remember, it's about making data usable.
Question 2
True or False: Feature engineering is only about creating new features.
- True
- False
💡 Hint: Consider the full scope of feature engineering.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with several missing values, outline a detailed strategy for handling these under different missingness scenarios (MCAR, MAR, MNAR).
💡 Hint: Think about how the nature of missing data influences your approach.
Question 2
Consider a dataset where certain features are highly correlated. Discuss feature selection approaches and their implications on model accuracy.
💡 Hint: Evaluate the importance of each feature.
Challenge and get performance evaluation