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Learner Reviews & Feedback for R Programming by Johns Hopkins University

4.5
stars
22,186 ratings

About the Course

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples....

Top reviews

WH

Feb 2, 2016

"R Programming" forces you to dive in deep.

These skills serve as a strong basis for the rest of the data science specialization.

Material is in depth, but presented clearly. Highly recommended!

MR

May 11, 2020

Really interesting course. The interactive coding sessions with swirl are especially useful. Would be great, if you provided sample solutions for the programming assignments, in particular for week 4.

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3401 - 3425 of 4,725 Reviews for R Programming

By Rafael P V

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Jan 18, 2016

Very interesting, sometimes there's things that you have to figure out by yourself to go on.

By Muthukumaran P

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Jan 29, 2019

This course helped me to learn the basics of R and to apply it in many problems of Science.

By Stacy M

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Jan 6, 2018

More advice/demonstrations/exercises on when to use what methods would make it even better.

By Ludwid R

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Oct 27, 2017

The lectures were informative; however, the homework assignments had a huge learning curve.

By Rodrigo P G

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Apr 18, 2016

Nice course and interesting material but the other students evaluation is quite subjective.

By Dushan Y

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Mar 30, 2016

Good fundamental overview of the programming language. I also liked the pace of the course.

By Shaurya S

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Sep 7, 2020

can do with more practical real life examples/ case study.

Swirl was a great tool to learn.

By Rishabh T

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Mar 31, 2019

Good content, videos are very useful. Also the swirl() exercises are best way to practice.

By Himanshu S

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Feb 24, 2019

excellent course for the beginner. best part is that swirl is also included in the course.

By Angel M

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Nov 6, 2020

The Swirl exercise are the best and more recomended part of the course! I learn a lot :D.

By KANTARIA M H

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May 28, 2020

Sometimes I felt little bit hard assignments in comparison of contents covered in videos.

By Laurent R

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Mar 31, 2019

This course is great, I loved the topics. However, the last assignement is a bit to hard.

By Himanshu C

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Apr 5, 2018

Very Good Coursera. Would Recommend Anyone who is pursuing acareer in R and Data Science.

By Ian S

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Jun 29, 2016

Good exercises and challenging course, but very hard if you do not know any R beforehand.

By Julio C R N

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Jul 16, 2020

great explanations. A bit tiresome but because it goes into great details on the basics.

By Chih-Wei P

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Aug 26, 2016

It would be nice if you could combine the lecture notes into a single file for download.

By Camila R M

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Apr 10, 2021

Programming Assignments were not at the same level as the material taught in the course

By KSHITIJ S C

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Sep 2, 2020

Great course! Would definitely recommend to those who want to start data analysis in R!

By Jack J Z

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May 10, 2019

Homeworks and assignments are sometimes harder than what can be found in the textbooks.

By Gourav B

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Jul 4, 2018

Course is very good for beginners and will definitely enhance your R programming style.

By John K

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Jun 27, 2017

Great material and lectures, but a little advanced programming exercises for beginners.

By Jeyalakshmi S

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May 14, 2016

Very much helpful to understand the basic about the R programming. Very well structured

By Martin S R

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May 8, 2020

Teaching-wise it was great, but the materials for the assignments were too complicated

By Heather G

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Mar 20, 2016

I'm a fairly experienced R programmer and still learned a few things from this course!

By Sushmita G

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Mar 2, 2016

Great start to get into data analysis. A little difficult for 4 weeks, but manageable.