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Overview
Context
Missing data is part of any real-world data analysis. It can crop up in unexpected places, making analyses challenging to understand.
In this Dealing With Missing Data in R course offered by Data Camp, you will learn how to use tidyverse tools and the naniar R package to visualize missing values. You'll tidy missing values so they can be used in analysis and explore missing values to find bias in the data.
Lastly, you'll reveal other underlying patterns of missingness. You will also learn how to "fill in the blanks" of missing values with imputation models, and how to visualize, assess, and make decisions based on these imputed datasets.
Programme Structure
Chapters
- Why care about missing data
- Wrangling and tidying up missing values
- Testing missing relationships
- Connecting the dots (Imputation)
Key information
Duration
- Part-time
- 1 days
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Campus Location
- New York City, United States
Disciplines
Data Science & Big Data View 468 other Short Courses in Data Science & Big Data in United StatesWhat students do after studying
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Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
PREREQUISITES:
- Introduction to R
- Introduction to the Tidyverse
Tuition Fees
Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
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International Applies to you
Applies to youNon-residentsFree - Out-of-StateFree
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Domestic
Applies to youIn-StateFree
Additional Details
This course can be accessed for free with the Data Camp Premium or Teams subscriptions
Funding
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