Отзывы учащихся о курсе Набор инструментальных средств для специалистов по обработке данных от партнера Университет Джонса Хопкинса

A very big difficulty for me, French people who uses to speak english everyday for may work is that :

1) Teachers don't do any effort to speak slower and to articulate

2) As with all other courses I've attended in english, it's impossible to read a transcription, even in english, while reading slides.

Slides are not sufficient by themselves, i.e. without commentary beside. The workaround I've used is to print french translation or english transcription and read it, trying to guess which part suited with which slide ...

For a non-free course, it's not really professional ...

This was a very basic introduction to the concepts of Data Science and the software necessary for future courses. Overall it was fine, but I hated the robotic voiced over videos and ended up playing them on mute to get through them. Also, considering the explanation that doing the class content and videos this way allows the university to keep the courses up to date and correct errors faster, there was still several out of date sections of the course and a number of errors, so I really felt the automated setup as an annoyance not a benefit.

The lectures were very uninspiring for the most part and I felt that my practical understanding was poor. The forums were also not particularly helpful as I saw Moderators mocking students for asking valid questions based on their inexperience with Data Science. I spent most of my time finding better tutorials on YouTube, so really.... I have a certificate but don't owe much of that to the course in question. It only becomes worse as the complexity increases with the rest of the Courses in this Specialization.

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.

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 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.

You will learn some command line commands, git, Github, and what you will need for eventually using R.

I was a bit annoyed that this course is a requirement for the Data Science certification (which is what I am after). I already knew everything taught in this course and was a bit annoyed I had to pay for this in order to complete the track. I think 'courses' that are fully setup like this should be opt-in not required.

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.

C​ourse 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.

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.