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Back to Data Analytics: Scraping Data using Hadley Wickam's Rvest package in R

Learner Reviews & Feedback for Data Analytics: Scraping Data using Hadley Wickam's Rvest package in R by Coursera Project Network

4.2
stars
39 ratings
10 reviews

About the Course

In this 1-hour long project-based course, you will learn how to (complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Random Forest model to the data using the FFTrees package in R, and examine the results using a Confusion Matrix. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....

Top reviews

VP
Jun 21, 2020

Thank you so much for this project. It was really helpful. The style of teaching is 10/10.\n\nLooking forward to the next session.

PN
Aug 22, 2020

Very useful and easy to understand project.Thank you

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1 - 10 of 10 Reviews for Data Analytics: Scraping Data using Hadley Wickam's Rvest package in R

By Vikash P

Jun 22, 2020

Thank you so much for this project. It was really helpful. The style of teaching is 10/10.

Looking forward to the next session.

By Phuong A N

Aug 23, 2020

Very useful and easy to understand project.Thank you

By Abdullah B H

Jul 15, 2020

Everything was great....new experience!

By Cherry I T

Jul 5, 2020

you must learn

By Raisa N E

Nov 18, 2020

good!

By p s

Jun 26, 2020

Good

By tale p

Jun 24, 2020

good

By Max

Sep 16, 2020

"practice looking at data distribution using R and ggplot2,

Apply a Random Forest model to the data using the FFTrees package in R, and examine the results using a Confusion Matrix."

Where is that in the course? I just saw an overview and some practice with Rvest package, but beyond that there was no RF, ggplot2 nor confusion matrix.

By Mayank A

Jun 24, 2020

It was a very quick overview of how to scrape the data. The instructor could have saved an R script on the desktop for learner's quick access.

By Paolo O

Oct 30, 2020

I found it a loss of time and bit frustrating. What it takes 2 hours here (and more to figure out what's not working if you run on a different R version) it can be better achieved with a few minutes of good reading. No concepts explained!