Cleaning Data In R with Tidyverse and Data.table

Online Course

Udemy
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Cleaning Data In R with Tidyverse and Data.table
  1. What is the course about?
  2. Prerequisites & Facts
  3. Cost, Certificate & Degree
  4. Reviews & Experiences

What is the course about?

Cleaning Data In R with Tidyverse and Data.table
The course Cleaning Data In R with Tidyverse and Data.table is an online class provided by Udemy. It may be possible to receive a verified certification or use the course to prepare for a degree.

Get your data ready for analysis with R packages tidyverse, dplyr, data.table, tidyr and more

Course description
  • Cleaning Data In R with Tidyverse and Data.table
  • Convert raw and dirty data into clean data
  • Understand how clean data looks and how to achieve it
  • Use the R Tidyverse packages to clean data
  • Handle missing values in R
  • Detect outliers
  • Just basic R skills are required for this course
  • R and RStudio
  • 4 hours on-demand video
  • 5 articles
  • 3 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
  • R-Tutorials Training
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Prerequisites & Facts

Cleaning Data In R with Tidyverse and Data.table

Course Topic

Personal Success, Teacher Training, Teaching & Academics

University, College, Institution

Udemy

Course Skill Level

see course summary

Course Language

English

Place of class

Online, self-paced (see curriculum for more information)

Degree

Certificate

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Degree & Cost

Cleaning Data In R with Tidyverse and Data.table

To obtain a verified certificate from Udemy you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.
Udemy
Cleaning Data In R with Tidyverse and Data.table
provided by Udemy
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Reviews

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School: Udemy
Topic: Business, Data Analytics, Data Cleaning

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