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Handling Missing Data

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Overview

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Missing data is a common challenge in real-world datasets that can affect analysis accuracy. This course teaches how to identify and handle various types of missing data using Python. You’ll learn methods like deletion, linear interpolation, and multiple imputation, ensuring your data is clean and analysis-ready.

Syllabus

  • Introduction to Missing Data: Gain an understanding of what missing data is, how it occurs, and why it's important to address.
    • Article: Introduction to Handling Missing Data
    • Article: Types of Missing Data
  • Deletion: Explore how and when to use pairwise and listwise deletion as strategies for handling missing data.
    • Article: Handling Missing Data with Deletion
  • Imputation: Explore imputation techniques including single imputation, linear interpolation, and multiple imputation to handle missing data.
    • Article: Single Imputation
    • Article: Linear Interpolation
    • Article: Multiple Imputation
  • Off-Platform project: Tackle missing data with deletion and imputation to explore trends in Stack Overflow developer survey data.
    • Article: Off-Platform Project: Stack Overflow Survey Trends

Taught by

Kenny Lin

Reviews

4.3 rating at Codecademy based on 249 ratings

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