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Dealing with Missing Data in Python

Learn how to identify, analyze, remove and impute missing data in Python.

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4 Hours14 Videos46 Exercises15,459 Learners3800 XPPython Toolbox Track

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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.
  1. 1

    The Problem With Missing Data

    Free

    Get familiar with missing data and how it impacts your analysis! Learn about different null value operations in your dataset, how to find missing data and summarizing missingness in your data.

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    Why deal with missing data?
    50 xp
    Steps for treating missing values
    50 xp
    Null value operations
    100 xp
    Finding Null values
    100 xp
    Handling missing values
    50 xp
    Detecting missing values
    100 xp
    Replacing missing values
    100 xp
    Replacing hidden missing values
    100 xp
    Analyze the amount of missingness
    50 xp
    Analyzing missingness percentage
    100 xp
    Visualize missingness
    100 xp
  2. 2

    Does Missingness Have A Pattern?

    Analyzing the type of missingness in your dataset is a very important step towards treating missing values. In this chapter, you'll learn in detail how to establish patterns in your missing and non-missing data, and how to appropriately treat the missingness using simple techniques such as listwise deletion.

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  3. 4

    Advanced Imputation Techniques

    Finally, go beyond simple imputation techniques and make the most of your dataset by using advanced imputation techniques that rely on machine learning models, to be able to accurately impute and evaluate your missing data. You will be using methods such as KNN and MICE in order to get the most out of your missing data!

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In the following tracks

Python Toolbox

Collaborators

AAN94
Adel Nehme
Suraj Donthi Headshot

Suraj Donthi

Deep Learning & Computer Vision Consultant

Suraj is a Deep Learning practitioner with experience in applying deep learning and machine algorithms to solve complex problems in the domains of automotive, retail, surveillance, biomedical image processing, trading as well as analytics. He has worked with clients across the globe to provide reliable machine learning solutions.
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