Handling Missing Datapro-logo

Why Learn Handling Missing Data?

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.

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  1. 1
    Gain an understanding of what missing data is, how it occurs, and why it’s important to address.
  2. 2
    Explore how and when to use pairwise and listwise deletion as strategies for handling missing data.
  3. 3
    Explore imputation techniques including single imputation, linear interpolation, and multiple imputation to handle missing data.
  4. 4
    Tackle missing data with deletion and imputation to explore trends in Stack Overflow developer survey data.
testimonial

— Madelyn, Pinterest

I know from first-hand experience that you can go in knowing zero, nothing, and just get a grasp on everything as you go and start building right away.

Course Description

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

Details

Earn a certificate of completion
1 hour to complete in total
Beginner

Gain an understanding of what missing data is, how it occurs, and why it’s important to address.

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3 articles

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