# Dealing with Missing Data in Python

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

4 Hours14 Videos46 Exercises13,993 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.

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.

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.

4. 4

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!

In the following tracks

Python Toolbox

Collaborators

#### 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.