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Dealing with Missing Data in Python

4.1+
11 reviews
Intermediate

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

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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.
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  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.

    Play Chapter Now
    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.

    Play Chapter Now
    Is the data missing at random?
    50 xp
    Guess the missingness type
    100 xp
    Deduce MNAR
    100 xp
    Finding patterns in missing data
    50 xp
    Finding correlations in your data
    100 xp
    Identify the missingness type
    50 xp
    Visualizing missingness across a variable
    50 xp
    Fill dummy values
    100 xp
    Generate scatter plot with missingness
    100 xp
    When and how to delete missing data
    50 xp
    Delete MCAR
    100 xp
    Will you delete?
    100 xp
  3. 3

    Imputation Techniques

    Embark on the world of data imputation! In this chapter, you will apply basic imputation techniques to fill in missing data and visualize your imputations to be able to evaluate your imputations' performance.

    Play Chapter Now
    Mean, median & mode imputations
    50 xp
    Mean & median imputation
    100 xp
    Mode and constant imputation
    100 xp
    Visualize imputations
    100 xp
    Imputing time-series data
    50 xp
    Filling missing time-series data
    100 xp
    Impute with interpolate method
    100 xp
    Visualizing time-series imputations
    50 xp
    Visualize forward fill imputation
    100 xp
    Visualize backward fill imputation
    100 xp
    Plot interpolations
    100 xp
  4. 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!

    Play Chapter Now
    Imputing using fancyimpute
    50 xp
    KNN imputation
    100 xp
    MICE imputation
    100 xp
    Imputing categorical values
    50 xp
    Ordinal encoding of a categorical column
    100 xp
    Ordinal encoding of a DataFrame
    100 xp
    KNN imputation of categorical values
    100 xp
    Evaluation of different imputation techniques
    50 xp
    Analyze the summary of linear model
    100 xp
    Comparing and choosing the best adjusted R-squared
    100 xp
    Comparing density plots
    100 xp
    Conclusion
    50 xp
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datasets

DiabetesAir Quality

collaborators

Collaborator's avatar
Adel Nehme

prerequisites

Introduction to Data Visualization with MatplotlibSupervised Learning with scikit-learn
Suraj Donthi HeadshotSuraj Donthi

Deep Learning & Computer Vision Consultant

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Don’t just take our word for it

*4.1
from 11 reviews
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  • Ankush B.
    12 months

    Well explained course dealing with all types of imputation for missing data.

  • Juan Z.
    over 1 year

    Useful

  • William M.
    over 1 year

    The exercises are practical and require a complete review of knowledge. I loved this course!

  • Eric H.
    almost 2 years

    I think that how to deal with missing data is the most important course of action that everyone should take. This is most comprehensive course to deal with missing data.

  • Maciej G.
    almost 2 years

    i felt like nan masta

"Well explained course dealing with all types of imputation for missing data."

Ankush B.

"Useful"

Juan Z.

"The exercises are practical and require a complete review of knowledge. I loved this course!"

William M.

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