The course is well structured and provides a good introduction, however, I expected a bit more from a course that costs 20 pounds than just a few clips on how to install and set-up software/create a github account etc. This information is available for free online on the websites of the respective software.

I understand that such an introduction is needed for the course, but then do not offer it as a separate 20 pound module. Instead include it for free with any other 'specialization' you can buy.

This is a beginner level course that covers only setting up an R development environment, and does not include any statistical programming with R. The fact that it was beginner level was not stated on the course, and it wasn't clear to me when selecting this course that it was intended as the introduction for a 10 week specialisation. I was introduced to R Markdown and to the existence of IDEs that automatically sync with git repositories, so I still gained something from this course.

It shouldn't have been a stand alone course. Just a series of "how-to" instructions on installing RStudio, linking it with and using Github, which one can find easily with more detailed examples on the internet. This one can be combined with the next course (R Programming) in the specialization.

That monotonous bot voice is a serious drawback. Having an instructor is very important for a course especially when you are charging for the course.

I'm sorry for leaving such a negative review, but the use of voice-to-text rather than recorded lectures makes the videos absolutely awful to listen to. There is a video at the beginning of the course which tries to rationalize why they made this change, but in my opinion they do not provide a reasonable excuse for why they did this. The monotone, robotic voice might be an acceptable substitute for some people, but it isn't for me.

I can see that the lecturer's intention was giving an overview by mentioning all different topics, however, it just got me confused. I wish it was more into a solo topic. It would be great if it was given only git/github lectures, so we, the students, can be more comfortable when it comes to uploading files in the following courses. I see in the discussion forums that github is a problem for most of the students in the forums.

The course is extremely introductory and even though it may lead you to references you may use to self-teach yourself, it is not worth taking a four weeks course for a one week content. At the end of the week you will have about three new programs installed in your computer and no idea on how to use them for practical situations. Although it's intended as introductory, it surpasses that barrier to mere spectacle.

In my opinion, the content of this course is too basic and little bit of topic for the data scientist specialization. Of course it is useful to be able to use git, shell etc. but I believe that most of the people already know those and the rest of the students can be redirected to relevant study material. All in all, there was about 90 minutes of relevant study in this course.

Very very introductory. I didn't find the tooling aspects of this course particularly helpful, but I'm not in the target audience. It's length was misleading -- I completed the work in two days easily, but I am a technologist, and already had the tools installed. If you use GitHub and RStudio, the meat of the course is only about an hour of the lecture.

Not very engaging videos. Superficial introduction to the mechanics of some tools without providing much context. Final submission did not work per video explanation. Need a more engaging presenter. Not quite at the level of Prof. Ng's Machine Learning course which I realy enjoyed and learned a lot from.

This should not be its own course, as it would be pretty useless if you were doing it on its own without doing any of the other courses. The end of course project literally being just to make a Github account and download R-studio could be quickly covered in the first week of the other courses.

showing - doing things live in the video would have been great .. it seemed like explaining a PPT by an instructor. You could have taken one data set and have done all shorts of things and then in the midst thrown some quizzes to student instead of going though all the discussion in one go

Not bad, but certainly not good. I cannot believe there is a style of teaching where you never get to see the best way to do something. I can slog thru the programming, but I doubt it's the best way to do something, but I never get to see how something should have been done.

Very basic course. Poorly motivated, material presented without an effort to demonstrate why. This is not entirely out of place in this intro course, but it permeates the entire program. Difficulty poorly controlled, projects and quizzes much more advanced than lectures.

I personally think that this course should be better interwoven into the other modules of the data science specialization. It's a quick primer, but aside from that, not very valuable in terms of the information that it's providing. I wouldn't pay $49 to take this course.

It is generally good course, but I feel it is just a combination of very little tools, skills and ideas. I believe it is quite complicate for me to understand, esspecial git and github. I think it is better to combine this course in other courses where you need it.

Would like to have seen more material using git and git bash commands. More repetition here would be helpful. Seems like a lot of this information will be lost or forgotten when it comes time to use it, especially as it relates to the Data Science Specialization.

Good content, but course conducted by a machine is way worse then course conducted by a human being. Unfortunately it looses a lot of quality. I can understand that it is easier and more convince to update and change content but it is worse for a student.

Course content is good. But the way of teaching is worst. It is because of the computer generated voice i.e text is converted to audio and we listen to that. It is extremely horrible and feeling bad without any interest. Natural way of teaching is best.

Dry videos. Most classes are someone showing how they do something their way, rather than guiding or teaching with the intent to have the student absorb and re-apply principles.

Very difficult for a student to absorb material from this video series

Hard to follow with a lot of technical intructions right off the bat with inadequate explanation, a lot of "read more about this at <insert URL>." Videos were boring and the instructor was invisible just reading off the slides. Not very engaging.

This course should not be the first module of the specialization. Also, it lacks exercises and the content's explanation is not that deep and interactive, making the first course of the specialization boring and not caught student attention.

I definitely did not like the robot voice even though the course mentioned that it could be annoying. I wish there was more instruction involved, especially for beginners like myself. It was still confusing to complete some of the tasks.

Video voice is terrible! I understand the concept behind automated videos but the voice itself is just horrible! In times of A.I. I'm pretty sure you can make less robot, more human, and less boring and making yours eyes-shut voice.

Very disappointed in the quality of the videos. The voice-over is computer generated and takes too much focus. Luckily you can choose just to read it yourself. But this is not what I was expecting from Johns Hopkins and Coursera.

The course needs to spend more time on how to init and push an item from RStudio to Git. I ended up having to watch a youtube video on how to do it. And all the comments show that this course did not prepare students how to do it.