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Dealing with Missing Data in Python

Intermediate
4.6+
12 reviews
Updated 03/2025
Learn how to identify, analyze, remove and impute missing data in Python.
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PythonData Manipulation4 hours14 videos46 Exercises3,800 XP23,744Statement of Accomplishment

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Course Description

Tired of working with messy data? Did you know that most of a data scientist's time is spent in finding, cleaning and reorganizing data?! Well turns out you can clean your data in a smart way! In this course Dealing with Missing Data in Python, you'll do just that! You'll learn to address missing values for numerical, and categorical data as well as time-series data. You'll learn to see the patterns the missing data exhibits! While working with air quality and diabetes data, you'll also learn to analyze, impute and evaluate the effects of imputing the data.

Prerequisites

Introduction to Data Visualization with MatplotlibSupervised Learning with scikit-learn
1

The Problem With Missing Data

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2

Does Missingness Have A Pattern?

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3

Imputation Techniques

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4

Advanced Imputation Techniques

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Dealing with Missing Data in Python
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*4.6
from 12 reviews
67%
33%
0%
0%
0%
  • Haridel
    4 days

    I like the speaking pace of the instructor.

  • Venkata Nitish Kumar Reddy
    8 days

  • Iyinoluwa
    17 days

  • Helen
    12 days

  • Helen
    12 days

  • John
    27 days

    alot of good introduction and things to further study for more information.

"I like the speaking pace of the instructor."

Haridel

Venkata Nitish Kumar Reddy

Iyinoluwa

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