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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does data quality refer to?
💡 Hint: Think about the characteristics that make data usable.
Question 2
Easy
Name one technique to handle missing data.
💡 Hint: Think about common approaches you've learned.
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
Which of the following is a common issue in data quality?
- A. Consistency
- B. Duplication
- C. Completeness
💡 Hint: Consider what happens to data when there are repeated entries.
Question 2
True or False: Normalization is used to scale data to a specific range.
- True
- False
💡 Hint: What range are we typically targeting with normalization?
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with missing values, outline a strategy to handle these issues while preserving the dataset's integrity.
💡 Hint: Think about the context and significance of each data field.
Question 2
How would you write a Python function that detects and removes duplicates based on specific columns?
💡 Hint: Understand how to apply `drop_duplicates()` within a function context.
Challenge and get performance evaluation