Overview
Context of the Dealing With Missing Data in R course at Data Camp
Missing data is part of any real world data analysis. It can crop up in unexpected places, making analyses challenging to understand.
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
Disciplines
Information Technology (IT) Data Science & Big Data View 592 other Short Courses in Data Science & Big Data in United StatesAcademic 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 Fee
To always see correct tuition fees
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International
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 1 days. -
National
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 1 days.
Basic Access: Free; Premium (for individuals): $12.42 per month billed annually; Teams: $25 per month billed annually; Enterprise: Contact sales for pricing
Funding
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