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Coping with Missing, Invalid, and Duplicate Data in R

Coping with Missing, Invalid, and Duplicate Data in R

1425/month
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Self paced
English
Course by
PluralsightCourses from Pluralsight
Certificate awarded
Intermediate
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Checklist

Certification

You will get a certificate on completing this course.

University

This course is not affiliated with any university.

Price

This course costs very less.

Edvicer's Rewards

You can get a cashback of ₹ 100 on buying this course.

Why should you choose this course?

Description

Learn about the most essential steps of data preparation: Missing value imputation, outlier detection, and duplicate removal.

Syllabus

Course Overview
Intro
Managing Expectations
Course Dataset
Data Import
Factors vs. Character Data
Succession of Steps in Data Pre-processing
Duplicate Data in R Base and dplyr
Summary
Intro
Understanding Missing Values
Quick and Simple Methods for Missing Values
Imputation Methods
Using visdat for NA Visualizations
MICE for Missing Values
Machine Learning for Missing Values
Working on the Carparts Dataset
Summary
Intro
Understanding Statistical Outliers
Methods for Outlier Detection
The 6 Sigma Rule
The Boxplot Method
Hypothesis Tests for Outliers
Outliers in High Dimensionality
Plausibility Checks and Replacement
Summary
Intro
Reproducibility in Pseudo Random Processes
Data Pre-processing Task Views
Course Summary

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