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Course

Dealing with Missing Data in Python

IntermediateSkill Level

4.7+

Updated 08/2023

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

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PythonData Manipulation4 hours14 videos46 Exercises3,800 XP24,174Statement 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

*4.7

from 43 reviews

79%

19%

2%

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0%

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Most recent
Top reviews

Kanchanapalli

4 days

ABHISHEK

4 days

Guillaume

12 days

The instructor was very intuitive. This course is very usefull for me because it will help me to deal with missing data in my future job and also provides me some tools to get the "Python Data Associate" certification

Omer

5 days

Kevin

13 days

Med

8 days

Kanchanapalli

ABHISHEK

"The instructor was very intuitive. This course is very usefull for me because it will help me to deal with missing data in my future job and also provides me some tools to get the "Python Data Associate" certification"

Guillaume

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