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ジョンズ・ホプキンズ大学(Johns Hopkins University) による Rプログラミング の受講者のレビューおよびフィードバック

4.6
17,394件の評価
3,641件のレビュー

コースについて

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....

人気のレビュー

MR

May 12, 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.

WH

Feb 03, 2016

"R Programming" forces you to dive in deep.\n\nThese skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!

フィルター:

Rプログラミング: 26 - 50 / 3,539 レビュー

by Eric J

Jul 12, 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!

by Abhey K

May 27, 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.

by Faylene T G

Feb 17, 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.

by Nouran G

Feb 14, 2019

Assignments are hard compared to the content

by Sotirios T

Oct 14, 2018

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

by Rushi P

Apr 01, 2019

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

by Jeremy T

Sep 30, 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.

by Brian W

Feb 20, 2017

Damn near worthless. The course material has significant gaps of information. The programming assignments require knowledge that isn't presented in the course. I had to Google my way through this entire course. I would not recommend it.

by Carlos M

Aug 23, 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.

by JOSÉ M O

Jan 06, 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.

by Guilherme O B

Feb 03, 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

by Jacob T

Apr 03, 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.

by Gaby R

Oct 24, 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).

by Kerryn A M

Oct 08, 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.

by John C S

Jul 12, 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 datacamp.com, 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.

by Julio G D

Feb 06, 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.

by Shreya S

Feb 27, 2019

Brilliant content!! Practice exercises really help you evaluate yourself. A little knowledge of R programming before this course is helpful as that might help understand the slides better. Though it is not mandatory but one can still utilize free resources available online to build the basic knowledge. Also, if you have prior experience with any programming language, that'll be very helpful. Would highly recommend this course.

by Maximilian R

May 12, 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.

by WHAT

Apr 16, 2019

Nice!!

by Md. Z M

May 23, 2020

For the background, I am a graduate in Computer Science with 3+ years of professional experience in software development.

I found the course to be challenging enough, and equally rewarding. The swirl exercises are good and help you practice the commands in a guided setting. The programming assignments were very challenging. I ended up working on them for 7-8 hours on average. Assignments were designed to make you investigate and study on your own (this really helped in my understanding of R).

Surely, I would recommend R Programming. However, novices in programming will have to invest a lot of time and effort on their own to navigate through the content for significant gain.

by Ong W J

Oct 06, 2018

This course is tough if you have zero background in programming. I struggled alots especially in the last assignment.

by Zhu, T

May 26, 2020

Homework and programming assignments are not related with videos.

by SEEUN H

Feb 04, 2019

Nice, but limited explanation for beginners.

by Yaw O

Feb 02, 2019

Some concepts weren't clearly discussed

by Jesús A P G

May 02, 2020

In short, I learned a lot in this cocurse but I would not consider it for beginners with little programming experience. I spent at least 3-5 times more doing the assingtments. The information presented was very valuable but the pedagogical part was not well done. The chunks of information were poorly connected between themselves, making the learning process tidous and hard to achieve. The visuals were very sloppy with only white slides and a font too small. For these reason the lectures were boring and unappealing. My suggestion is to take more care of the didactic aspect of the course. Many times I had to watch YouTube videos on the same topic that I had just seen, and I can say that a colored diagram made all the difference between understanding everything and not understanding nothing at all.