This course stays on the surface and doesn't delve too deep, probably in order to not scare off people who are new to the subject. From what I've seen in the second course, the material becomes a lot more difficult, which I was glad to see.

Would have like to jump straight to the material. Although I understand some may need it, the command line course was pretty basic. This course on it's own doesn't give much. One can get started with Programming with R w/o missing too much.

Fairly basic course covering the fundamentals, I would suggest to most people to complete this course concurrently with the R programming course or to complete it all in one go, as I personally completed it within a couple of hours or so.

While this is a good introduction to Data Science and the tools available, it might feel a little too elementary if you have had prior exposure to the subject. The final peer review project could have been a little bit more challenging.

It did not meet my expectations. It is more an informative course than a specialization course. They thought they were going to go deeper into the use of statistical programs, they only cover details that you can discover for yourself.

I have mixed feelings. It was WAY too easy for me, but my wife did it too and it was about right in difficulty for her. But either way, I felt like it just taught you to install stuff rather than actually teaching meaningful material.

A good course as an introductory course. My career aspirations are not to become a data scientist so I don't know how much I am going to use the Github. Time will tell. I really enjoyed the videos and how the course is given. Cheers

Too easy if you already know anything about github or Rstudio, although I do understand the need for an introductory course. The "Experimental Design" video was the only really useful part and contained quite entertaining examples.

This is a very basic course. If you are already a software engineer you could zoom through this entire course in an evening. If you are not a software engineer and don't know what things like github are, this class could be useful.

it is not the best way to start your data science career for me as a student because it has a lot of reading and theoretical ways I don't think its good for a beginning start and I was hopeful for more practice I would love to do

Beginning course in Data Science spent too much time on tools and not enough time on concepts, possible solutions and application. Seems like I spent a lot of time in the weeds of application installation and repositories. humm

I believe that in some of the assingments the student is asked to do a few things that werent taught in the course. And also during one of the quizzes the platform wasn't working propperly, and I had to answere it many times.

I don't find the contents about R, git, and GitHub very helpful. They are way too brief and perhaps work better in those specific courses where we have a change to use them. Solely learning some commands is not very effective.

A pesar de que el curso lo menciona, es demasiado introductorio, no profundiza nada y en mi opinión se pierde mucho tiempo viendo lo de GitHub. Aprendí muy poco de data science, lo demás lo sabía de los cursos de estadística

Learned a bit but overall it is literally not worth the price of admission. I think that this course could be offered for free and maybe the others are worth paying for. Hope that the rest of the specialization is worthwhile

Although the course is a good one to get you all set up for the upcoming courses from the Data Science specialization, the content of the course is very less to be considered as a separate course and charged money for.

Though the course itself is introductory in nature, i wish there was a lecture on what and how exactly are these going to be useful with the help of a real life example and also an increase in the depth of the content.

It is useful to get to know the software that staticians use and some review about them but it doesn't teach you how to use them. A good introductory course for the specialization track but useless as a unique course.

The overall outline are great. However, the contents and requirements of this course are too simple to be integrated as a whole course. I think 1 lecture should be enough. I am looking forward to deeper introduction.

This course should probably not be it's own course as it can be completed in one afternoon. It might be better suited as the first week of the R-Programming course (to make room maybe move the graphics talk to eda?)

The course was fine, but I think it should be offered for free even if someone is doing the data science specialisation track. It is really just teaching you what data science is, and how to install a few programs.

There has to be a more engaging way of introducing course material. For example, by showing someone actually using these commands in the videos vs. just putting the Git, R commands in a power point bullet format.

It's a good introductory course, but it's very basic and I feel that I paid a lot for a very basic experience. I do understand it is the first step in a full specialization but I think it can be more challenging.

Its always good to have a real teacher's voice rather than an automated. There were also a lot of instances where number of questions were asked even though they weren't taught in the class.

Overall, it was good

It is a very basic course to give you high level of what the course is going to cover and the tools that will be used throughout the course. If you have much programming background this course will be a breeze.