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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What are missing values?
💡 Hint: Think about situations where data might be incomplete.
Question 2
Easy
Name one common cause of missing data.
💡 Hint: Consider how people might accidentally skip 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 a common cause of missing data?
- Human error
- Data redundancy
- Data modeling
💡 Hint: Think about processes that involve human interaction.
Question 2
True or False: Ignoring missing values can lead to biased data analysis.
- True
- False
💡 Hint: Reflect on the importance of complete datasets.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
How would you write a report on customer satisfaction if your data had 20% missing entries? What strategies would you use to fill in the missing pieces while maintaining the integrity of the analysis?
💡 Hint: Consider various data filling techniques and discuss their impact.
Question 2
You are working with a dataset that has undergone cleaning, and 10% of the rows have missing values for a significant variable (e.g., income). Discuss how this absence may influence your predictive analytics model.
💡 Hint: Think about how different datasets can affect analytical outcomes.
Challenge and get performance evaluation