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Codecademy

Handling Missing Data

via Codecademy

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Overview

Learn how and when to tackle missing data with deletion, single imputation, linear interpolation, and multiple imputation techniques.

Nearly every dataset you'll come across has missing data. So what are you going to do about it? This course will help you identify different types of missing data and how to address each using techniques in Python.



### Take-Away Skills

This course will teach you how to use deletion, LOCF, NOCB, linear interpolation, and multiple imputation techniques to address Structurally Missing Data, MCAR, MAR, and MNAR data.

### Note on Prerequisites

We recommend you have some knowledge of Python, as well as the pandas library before taking this course.

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

4.2 rating at Codecademy based on 141 ratings

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