2.1 - Understanding Data Wrangling
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
What is data wrangling?
💡 Hint: Think about how you would prepare data for analysis.
Why is handling missing data important?
💡 Hint: Consider the implications of missing information.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the primary goal of data wrangling?
💡 Hint: Think about what steps are involved before analysis can happen.
True or False: Cleaning data is the only aspect of data wrangling.
💡 Hint: Consider the various processes involved in data preparation.
Get performance evaluation
Challenge Problems
Push your limits with advanced challenges
Imagine you have a dataset with 1000 entries, of which 250 have missing values. How would you approach cleaning this data to prepare for analysis?
💡 Hint: Think about the trade-offs between data loss and accuracy.
Discuss an instance where outlier values in a dataset could mislead an analysis. What steps would you take to prevent this?
💡 Hint: Consider methods you’ve learned about detecting outliers.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.