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course

Dealing with Missing Data in Python

Intermediate

4.2+

Updated 12/2024

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

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PythonData Manipulation4 hours14 videos46 exercises3,800 XP23,394Statement 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 Matplotlib Supervised Learning with scikit-learn

1

The Problem With Missing Data

Why deal with missing data?

Steps for treating missing values

Null value operations

Finding Null values

Handling missing values

Detecting missing values

Replacing missing values

Replacing hidden missing values

Analyze the amount of missingness

Analyzing missingness percentage

Visualize missingness

2

Does Missingness Have A Pattern?

Is the data missing at random?

Guess the missingness type

Deduce MNAR

Finding patterns in missing data

Finding correlations in your data

Identify the missingness type

Visualizing missingness across a variable

Fill dummy values

Generate scatter plot with missingness

When and how to delete missing data

Delete MCAR

Will you delete?

3

Imputation Techniques

Mean, median & mode imputations

Mean & median imputation

Mode and constant imputation

Visualize imputations

Imputing time-series data

Filling missing time-series data

Impute with interpolate method

Visualizing time-series imputations

Visualize forward fill imputation

Visualize backward fill imputation

Plot interpolations

4

Advanced Imputation Techniques

Imputing using fancyimpute

KNN imputation

MICE imputation

Imputing categorical values

Ordinal encoding of a categorical column

Ordinal encoding of a DataFrame

KNN imputation of categorical values

Evaluation of different imputation techniques

Analyze the summary of linear model

Comparing and choosing the best adjusted R-squared

Comparing density plots

Dealing with Missing Data in Python

Course
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Included withPremium or Teams

Don’t just take our word for it

*4.2

from 12 reviews

67%

17%

0%

8%

8%

Highest to Lowest
Lowest to Highest
Most recent
Top reviews

Alessandro M.

10 days

top

Ankush B.

about 1 year

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.

"top"

Alessandro M.

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

Ankush B.

"Useful"

Juan Z.

FAQs

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