Dealing With Missing Data

Learn tools and build intuition you need to decide how to handle missing values in your data set.


  • Learn techniques for omitting missing values from calculations
  • Understand how different approaches to handling missing values affect analysis.
  • Build intiution to help you decide how to approach analyses when data are missing.

Mission Outline

1. Exploring Academic Success and Demographics by Borough
2. Defining "Missing Data"
3. Contagious Missing Values
4. Dropping Rows With Missing Values for one Variable
5. Complete Cases: Dropping All Rows With Missing Data
6. Using Complete Cases: When to Avoid
7. Understanding Effects of Different Techniques for Handling Missing Data
8. Imputing to Replace Missing Data
9. Next Steps
10. Takeaways


Course Info:

Data Cleaning in R


The median completion time for this course is 8.05 hours

This course is free, and includes four missions and one guided project. It is the fourth course in the Data Analyst in R path.


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