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Bewertung und Feedback des Lernenden für R-Programmierung von Johns Hopkins University

21,415 Bewertungen
4,642 Bewertungen

Über den Kurs

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....


11. Aug. 2019

Very challenging, but good course. I've been programming in R for over a year, but there were still some things for me to pick up in this class. Assignments were a challenge, but satisfying to tackle.

11. Juli 2016

Excellent course! I already knew a lot about R - but this class helped me solidify what I already knew, taught me lots of new tricks, and now I have a certificate that says I know `something' about R!

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4101 - 4125 von 4,544 Bewertungen für R-Programmierung

von Rachade H

28. Sep. 2016

not the easier way to learn R

von Ron M

28. Jan. 2018

good but very time consuming

von 后峻

8. Juni 2017

in course purchase make th

von Jaivardhan D

25. Juli 2020

Quite hard for beginners

von Vivekanand R

2. Okt. 2016

Needs clear instructions

von JOSÉ A T M

6. Aug. 2020

No esta todo en español

von Nicholas E

9. Juni 2020

course is really fast

von Vraj P

1. Juli 2019

a little fast paced.

von Jiahui X

29. Apr. 2016

straight and narrow.

von Samuel Y

9. Aug. 2021

not for beginners

von Shreya S

17. Feb. 2017

nice to learn:))

von Vikramaditya M

7. Apr. 2020

homeoworks are

von Sushmit R

20. Aug. 2017

Very helpful.

von Sumit K S

22. Juni 2020

Need Update

von 柏一

22. März 2016


von Brian L

27. Feb. 2019

too fast

von Andreas H

22. Juli 2018

Too hard

von Benjamin S

25. Sep. 2017

Very cha

von Ajit K

22. Juni 2020


von kishore

8. Feb. 2016


von Rahul

15. Juli 2019


von Rahul M

11. Nov. 2017


von Jacob P H B

5. Aug. 2021

I want to begin this review by thanking Johns Hopkins and Coursera for putting this course together. In the age of "work/learn from home" and "upskilling" it is courses such as these that allow the layman who is unfamiliar with data science to learn basic programming. For that I am grateful. On the other hand, even as this course cost $50.00 (which is reasonable), I still cannot recommend it to other students. As a graduate student, I have had some exposure to R. My statistics class utilized R for HW assignments and basic regression models. Our TA taught us some intermediate coding methods via ggplot2 and dplyr. I primarily took this Coursera course as a refresher. It should be noted that the course description encourages - nay, states - that beginners should do just fine taking the class. Nothing could be further from the truth. The course instructor is clearly a brilliant man who is a leader in the field of data analytics. I'm sure he is also a very well-respected lecturer at JHU. However, I felt like this whole course he was speeding through the content. I had to use 0.75 speed during the lectures so I could hear everything that he was saying. On top of that, he used a lot of R jargon that made the content seem more exclusive to students who already have a background in data science. Finally, there were limited opportunities for students to apply the skills that we learned in each lecture in the actual R environment. He took screenshots of code, explained it in an opaque fashion, and then moved on to the next lesson. That may be fine for some students, but I personally like to 'drive' when I'm learning how to use a car. The one bright spot in this course was the interactive swirl sessions which did allow you to put some concepts to good use. It should be noted that these are optional. In my opinion, if you didn't do the swirl assignments then I can't see how you took anything away from the course at all. Still, the swirl module could be clunky at times and could use updating. In addition, some of the answers in swirl simply required you to copy and paste what you were previously shown, which isn't very challenging. My final qualm with this course is the "programming assignments." While the swirl assignments were probably too easy for the layman user, the programming assignments were insurmountable tasks. There is a HUGE gap between what we learned in the modules and what we were expected to perform in the assignments. I don't understand the logic of teaching concepts, implementing what we learned in simplistic interactive swirl sessions, and then taking on advanced assignments. How does that help the beginner student at all? The teacher encourages a "hacker mentality" but in my opinion, struggling and googling your way to get the right answers isn't learning, it's insufficient teaching. It would be one thing if this course was advertised to advanced users. If that was the case, fine, the student should probably be able to write some of this code. But that was not the case and from the comments, it seemed that many other students also struggled with the program assignments. The teacher also dove into statistical theories in week 4 without explaining them. No offense, but I don't understand how we can possibly grasp linear models and Poisson regressions in a sub ten minute video. In summary, don't take this course if you are beginner or looking for a refresher. The content is outdated, the pace is too fast, and the programming assignments are disconnected from what you learn in the videos. One would be better off watching YouTube videos or taking a Udemy course for free.

von Srinivas S

1. Nov. 2016

I am a very frustrated learner trying to write a constructive review here. I studied the course full time to get a certificate to put in my profile. R programming is considered nearly essential skill, if not fundamental, to data analysis. So I had high hopes going into this course.

Lecture videos: I cannot begin to tell you how many times I fell asleep watching the video lectures. This course is for you if you like listening to someone talking through pages and pages of copy/paste text from a command prompt. I, on the other hand, prefer learning by doing. Sadly, all the doing is clumped into the assignments (more on them later). Also, Dr. Peng makes gross burping/drooling noises in the video from time to time. I apologize if that sounded rude, but try listening to the lectures with headphones and you'll say the same. Video editing for pre-recorded videos is not rocket science.

Assignments: These were the biggest frustration throughout the course. Imagine you want to learn to play the piano (and you play a little guitar now) and you go to the piano class. Your piano teacher talks your ears off with music theory for several hours and then hangs you out to dry in front of an audience in a concert hall. You protest "But I have no clue of how to PLAY", but your teacher says "All the piano greats learned by fiddling around with a "hacker" mentality". You ask "Why did I pay $50 to hack on my own?"

Off-lecture help: If you take the course for the same reason I did, then thank your favorite god for the moderators in the discussion forums (mentors). 1 out of the 2 stars I give belongs solely to them and the fantastic work they do. The stickied posts in the forums offer a bit of help with the assignments (not enough, but still something). And they are also active in answering questions. The other star in my review goes to "Swirl". You will learn way more by doing the swirl exercises than watching the lectures by a long way.

In conclusion, I finished the course with nothing more than a rudimentary understanding of R despite the fine grades. Very little thought seems to have been put into the lectures. I would recommend this only if you want to show this certificate to someone. Otherwise, stay away!


17. Jan. 2016

This course is VERY abstract and I find myself rushing through the videos to get to the practice/quiz so that I can trial and error my way through the project..... hoping for the best. Neither am I excited about starting the lesson each week because there is no real world problem/or data set I'm continuously practicing from. The lessons are essentially a reference guide and not a useful approach for teaching. If I wanted a reference guide, I'd just pick up one of the various handbooks/books on the markets that list R codes and their functions. A better way to teach/present this course is by infusing actual, real world examples or cases throughout the lessons instead of just listing a function and talking through its corresponding activity/response. Ideally, the real world example would be introduced in the first lesson in Week 1 and that data set would be used throughout the course(s) to apply and practice newly introduced functions. Teaching from this perspective would likely make the concepts much easier to grasp and importantly, RETAIN. The lessons, as they are currently presented, encourage rote memorization and rob the students of actually applying the concepts/code taught.

Also, I love the idea of this specialization. However, I think the professors need to work more closely with online instructional designers to make the entire series more palatable for online learning. It very much feels as if they have adapted a traditional course on their own without the help of professionals who are skilled at designing online courses. If the aforementioned is the case, the professor(s) should be commended for their efforts, but there is definitely more work to be done to make this an engaging course I'd recommend.

PS - I am taking notes and I have some experience with STATA so this type of coding/anaylsis is not unfamiliar to me. I can only imagine how novices may take to the lessons.