Course
Handling Missing Data
Learn how and when to tackle missing data with deletion, single imputation, linear interpolation, and multiple imputation techniques.
Skill level
BeginnerTime to complete
Average based on combined completion rates — individual pacing in lessons, projects, and quizzes may vary1 hourCertificate of completion
Included with paid plansPrerequisites
3 coursesWe suggest you complete the following courses before you get started with Handling Missing Data:- Data Wrangling and Tidying
- Data Wrangling and Tidying
- Learn Data Analysis with Pandas
About this course
Nearly every dataset you’ll come across has missing data. So what are you going to do about it? This course will help you identify different types of missing data and how to address each using techniques in Python.
Syllabus
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Frequently asked questions about Handling Missing Data
Nothing is perfect, and computers are no exception. Sometimes the data we collect is missing values for a given variable, which can skew analysis and results if not properly addressed.
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Related resources
- Article
Introduction to Data Wrangling and Tidying
A brief overview of the data wrangling process and tidy data - Article
Intro to Data Acquisition
Exploring and defining the methods of obtaining data - Article
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Learn what data structures are, why they are useful, and how you can use them effectively.
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Practice Projects
Guided projects that help you solidify the skills and concepts you're learning.Assessments
Auto-graded quizzes and immediate feedback help you reinforce your skills as you learn.Certificate of Completion
Earn a document to prove you've completed a course or path that you can share with your network.