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Kursteilnehmer-Bewertung und -Feedback für R-Programmierung von Johns Hopkins University

19,934 Bewertungen
4,268 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. 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!

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.

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26 - 50 von 4,155 Bewertungen für R-Programmierung

von Eric J

12. 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!

von Walter H

3. Feb. 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!

von Marta R

19. Mai 2019

Great course, however I think it is particularly hard for those who never had programmed in R before. Moreover I think the Programming Assignments were quite hard regarding the topics being discussed during the weeks.

von John C S

11. Juli 2017

Let me start by saying that I did learn some basics of R programming during this course with a lot of help from friends and fellow classmates.

I like to believe that my 20+ professional years in education and training design and delivery have left me with a pretty good understanding of adult learners, learning theory, and putting all of that into practice. With that in mind, here are a couple of points.

First, there is no prerequisite knowledge, skill, or course listed as required for this specialization. Here's what Coursera says about background knowledge, "Some programming experience (in any language) is recommended. We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required)." Great! My limited working knowledge of BASIC from the 1980s and novice ability with MS VB fit the bill. Math? No problem, got it covered with Algebra 2 30 years ago. But wait, it turns out neither of those are the case because there are pinned posts in the forum that say the author doesn't understand why an understanding of linear algebra isn't required because it would be really helpful, and you yourself make the point that, "Yes, the mathematics in Statistical Inference and Regression Models are tough for students who haven't previously studied statistics." Knowledge of statistics isn't required or even recommended for this specialization Mr. Greski. That's poor curriculum design and setting students up to fail because there is no realistic expectation set as to what they face in this.

Second, if the materials do not provide any framework or context to tie the assignments to previously taught content either in "lecture", swirl, or assignments, then the course designers and instructors did an incredibly poor job with the design. How can students, even those who have a background in statistics, be reasonably expected to know when an assignment makes use of information or learning that must be found outside of the course itself? This can easily be fixed by including a statement or section with each assignment that says something like, "This assignment covers material found in Lessons, x, y, and z. You will also need information found in sources outside this course such as, etc." The italicized sentence can be the same in every assignment. The paragraph could even say that the assignment is not connected to the current lesson in any way as the intent is for the student to make use of outside resources (or whatever the approriate intent is). Regardless, every graded assignment should have a purpose stating what the student should get out of it, and they could all benefit from a context statement.

Third, if people like me (those with a non-statistics/mathematics background) are not part of the target audience, then please define the target audience better. Currently, Coursera says this, "Beginner Specialization. No prior experience required." That makes it sound like it is appropriate for anyone with no background knowledge or experience because the course will provide all the background knowledge and skills needed along the way. I'm willing to bet that the full program for $3,310 at JHU has prerequisites other than "Beginner Specialization. No prior experience required," and "Some programming experience (in any language) is recommended. We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required)."

Finally, portions of this specialization that I have completed so far are out of date. There was one quiz question that involves a specific package in R that is not compatible with the latest version of R. That forces the student to guess. Of course, we can take the quizzes over and over again if we are patient enough so it doesn't really matter if we are guessing on the answers or actually learning and getting the correct answers. Does it? While $49/month may not seem like a lot of money to some people, for others it could be quite a bit if they are having difficulty finding a job and working to improve their skill sets and qualifications. Even though it is "just" $49/month, we are paying for what we presume to be a quality product from a top-notch university. I don't think it's too much to ask that someone correct all the various errors, keep the materials up-to-date with the current version of R (once a year at least), and review feedback such as this (and others) in the forums and Coursera comments. Yes, this means someone has to put the time in on that type of work. I can honestly say that the current state of the materials and quality of design and delivery are well below what I expected for a JHU associated product. I'm not sure I can recommend this to someone as an introduction to data science as it currently exists. I wouldn't be surprised if JHU as an Instructional Design program that could use something like this as a capstone project or similar effort.

I sincerely appreciate the time that the JHU staff, the folks volunteering as mentors, my fellow classmates, and my neighbor give to help me and other students understand the concepts and skills in this course. Our expressed frustration about not seeing a connection between assignments and lectures is not an expressed desire to have our hands held and be spoon fed. It's a frustration at wanting to understand the materials, how they fit together, and how we can use them, which I believe is the intent of education and training in general. Hopefully, Dr. Peng or someone from JHU will see this feedback and be interested in making improvements to the curriculum.

von Julio G D

5. Feb. 2017

Honestly, I'm very disappointed with this course. The content taught in this course is not in accordance with the assignments. It is like someone taught to be a builder and asked to build the Brooklyn Bridge ... Not fair at all.

If you are going to ask to build a bridge, teach me how. If you teach me how to be a builder ask me for a wall, not a bridge.

von Jeremy T

30. Sep. 2018

If I wanted to learn from outside source and "hack" my way through understanding, I would not be paying money for this course. I am paying money as I expect a smooth introduction and summary to the topic which it is not. I have taken classes in SQL and python on courser. Those were of better quality. I think this R class is one of the worst.

von Abhey K

27. Mai 2017

This was very engaging, however, the level of expectation and effort needed is much greater than course 1 - ToolBox.

This is perhaps the best course on R Programming designed for a small duration.

von Amy G

23. Mai 2020

At first glance, the lectures seemed great - they explain the chosen lecture material well and give you a good background on the basic building blocks and theory of R. The quizzes were also pretty good because they directly test you on the lectures and help reinforce the key ideas.

The problem with this course is the assignments. While the assignments are well-made in themselves and would otherwise help you learn by doing, they have little to no correlation with anything covered in the lectures for that particular week - or, if they do, it is not made sufficiently clear, even in hindsight.

This is not a fatal flaw learning-wise, because you can dig around in the forums/elsewhere on the internet for tutorials and explanations of the concepts you need to do the assignments, and through this you can still learn the intended material. I do feel like I've walked away from this course having learned what the instructor meant for me to learn about R.

But it does beg the question - if 80% of the essential material for succeeding in this course must be found by scouring outside websites and extra articles posted by former students in the forums, then what exactly am I paying for?

Furthermore, it seems I am not the only one who feels this way - more than once in this course I found a post from a mentor in the forums containing a random article from elsewhere on the web, with the caption "many students have said they are confused about [topic], so for the purposes of the assignment, consider this the 'missing lecture' from this week." For a free course/completely self-taught approach, this would be fine, but as part of a paid specialization, the extremely large gap between the given material and the assessments was deeply frustrating.

I understand that to learn programming is, to a certain extent, to learn by doing, and to learn how to take the theoretical information that was imparted in class and apply it to a new or different problem that is presented as an exercise. However, when, after taking diligent notes on all of the week's lectures and completing all 15 extra-credit swirl exercises twice just to make sure I haven't missed anything, I am still left wondering if the assignment in front of me was uploaded to the wrong course by the instructor by mistake, something has gone quite awry in the course design.

This course is not unhelpful, but it's not worth paying for.

von Faylene T G

17. Feb. 2019

A little more explanation on how to program using R constructs would certainly help. The course suddenly throws us deep into the ocean with hardly any experience in swimming.

von nouran a

14. Feb. 2019

Assignments are hard compared to the content

von Sotirios T

14. Okt. 2018

Programming assignments are very hard (and in some cases irrelevant) based on the material presented and topics discussed

von Rushi P

31. März 2019

Gave to little instruction and expected the assignments to be completed based on very little instruction

von Lorena M M

1. Dez. 2019

The lectures are not very engaging, just listening to someone talk about a code is not a good way to learn how to do it. The swirl course is a better option, but it is very basic and in most cases it consists only in copying something that's already on the screen. The gap between what you are taught in the lectures and the assignments is absolutely abysmal and incredibly frustrating. How are we supposed to know how to create complicated functions after just listening to someone say what a function is? I don't think that teaching someone the equivalent to "2+2=4" and then asking them to solve Riemann's Hypothesis in the exam is very fair.

I would not recommend this course.

von Rob E

21. Mai 2020

Roger Peng is regarded as one of the best in the business when it comes to practicing data science, but I found his instruction skills wanting. As someone new to R and new to programming, I found him somewhat hard to follow at times. Nevertheless, I powered through. Unfortunately, when I came to the quiz at the end of Week 2, I found it was drawing on background knowledge that had not been taught in the course. Honestly, unless you're starting with a background in programming, I would not recommend this course.

von Dilip A

23. Dez. 2019

The instructor appears unprepared to present the material. When presenting it appears that it's the first time he is reviewing the material.

This is my first bad experience on Coursera. The last Coursera course I took (SQL for Data Science by University of California, Davis) was great as it appeared that the instructor was prepared with what they were going to present.

Coursera needs to vet their instructors' course recordings before allowing them to put forth their material on the site.

von Carlos M

22. Aug. 2016

Difficult at times, I regularly used outside websites like stackoverflow to help with assignments, but that's how the real world works, there's no way that the lectures could solve all your problems.

Favorite: writing my own functions that searched real databases and returned means, ranks, and useful info. I felt like I took a huge step forward in my goal for data science.

Least favorite: Assignment #2, it felt completely unrelated to anything I learned, I wasted hours just to find out it was redundantly simple and in the end I didn't even find out if my code worked, the grade was peer-reviewed based on if you could correctly upload it to github and if it "looked" like it would work. (How would I know! LOL, I assumed all my peers' code was good enough)

Would 100% take this course again.

von JOSÉ M O

6. Jan. 2019

A very, very excellent course, really be very satisfied with what I learned during these four weeks. Only one thing, in my opinion, I felt that the "Programming Assignment" are a little bit bigger than what is explained in the videos (and that's really good), but some exercises that are requested in these "Programming Assignments" could still be be added to the list of 'things to learn', for us not to get so lost to these same.

von Guilherme O B

2. Feb. 2016

Excelente opportunity to learn a lot. The course is very well prepared introduce you to R programing. Dont feel bad if you dont get it at te first moment. It will be a process of leaning worth trying

von James M I

15. Apr. 2016

Great course to start getting your hands into R. While the lectures are OK and the Swirl exercises are great, the course falls a bit short in building you up to tackling the weekly programming assignments. I had to spend a lot of time researching on my own to complete the assignments. I don't mind that, but I could do that on my own without paying for the course. I'm paying because I want to learn R quickly without spending hours researching what could have easily been described in the lectures. A lot of times, the course mentors in the discussion forums provided the best guidance. I would have liked each programming exercise to build upon the last one, so that at the end of the course I would have a great R product to review later. As it is, each programming assignment can stand on its own. When learning to programming in a new language, I prefer small, quick victories that build upon one another to accomplish a great task. This is the reason for not giving full 5-stars. Otherwise, Dr. Peng does a good job at providing material. And, be sure to check out his podcast, Not So Standard Deviations, to remind you that even people who have been in this field for years still find some areas challenging. It gives us R beginners some mental support.

von Jacob T

3. Apr. 2019

This is an excellent course but be warned, the programming assignments are difficult. If you do not have any coding experience, the assignments may be a bit challenging for you. But with the help of the internet and Github, you can complete them with no problems. The course content is great. All the videos and information are for beginners and are very helpful. The best part of the course was the swirl exercises it had you complete in R. Swirl exercises acquitted you with the programming needed for success in R. Overall it is a great course. Just understand that the programming assignments can be challenging for new timers.

von Gaby R

24. Okt. 2018

The only feedback I have is that they should try to incorporate more examples (simple real world ones would be great) when explaining concepts in R, because otherwise it is quite dry. The things you learn in the individual lectures give you a very basic idea of the functionality without much insight on *why* its important or *how* it can be applied...I found most of my solutions/answers by googling and using swirl a bunch of times. However it is rewarding if you power through it and have A LOT of patience, especially if you are a beginner with no background in programming (like myself).

von Kerryn A M

8. Okt. 2018

I learnt a lot, but most assignments required me to look a lot of stuff up outside of the course. They say they have done this deliberately to encourage "hacker mentality". I agree that we need to learn how to cope on our own, but it ends up taking up hours of your time and seems like a pointless lesson. One reason for doing a course like this is so that you can have access to consolidated and trusted learning materials, which was not really provided. Having said that, the assignments are a good way to push yourself to learn R. But most of what you need to do the assignments will not be provided within the course.

von Tareq R

16. Sep. 2018

I think for a MOOC, this course could have used the power of video a lot more, listening to the videos that are basically voice over a slide , wasn't helpful at all, and if it wasn't for the book, I wouldn't learn anything.... if the videos were more illustrative and visual to explain certain concepts , that would have been much better

von Javier P

8. Dez. 2019

Very poor material. Very theoric, no interaction, interesting applications or examples, and very boring lectures.

The material is very trivial, without real applications.

The assessments were useful but in some cases they were not related with the topic.

von Kristofer R

31. Okt. 2018

R Programming assignments were much more complex than what the tutorials taught. There was a very drastic jump between the two and was very difficult/frustrating to complete, especially for someone with no coding experience.