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.

The robot voice is a bother. If it is something experimental, the course should be free: students are human beings, not guinea pigs. There are better books and tutorials for installing and exploring the software.

This module should not be kept as a separate piece...The steps mentioned in the lectures does not properly cover what is asked in the assignments.The lecture content needs to improve to cover the assignment

I found this informative but you do not get much in terms of service. One of the quiz has a technical issue and I made a complaint weeks ago, but I still haven't gotten a response. Nor has it been fixed.

It is too easy as a "university" course. It is much more like a pratictioner course. The final exam is ridiculous, really too easy.

It can be thought as a week course instead of a four week course.

Very difficult to learn R from brief lectures and no interactivity. The Swirl exercises are helpful, but certainly not enough to bring the non-programmer up to speed for exercises in the quiz.

Presentation great. Quizzes are idiotic. You take the quiz, it tells you that you got an answer wrong but does not tell you why most of the time -- so no chance to learn from your mistakes.

This was really basic information. It's not really a 4 week course. It takes several hours. The info is necessary, but it's sufficient as a single week of content for a larger course.

The robot voice makes it extremely difficult to fully understand the information presented. I found myself having to listen more than once to ensure I did not miss any information.