The course structure is really good. The content is good too. I found the speed a little too fast. Plus there should have been some small exercises in between before the quiz to make the lesson more interesting and intriguing. Another point that I would like to state is that, the slides could be even better and visually appealing than they are now.

The content is fine, as an introductory course, however, the computer generated lectures are a travesty. There are enough mistakes in the text-to-speech translation that make for a distracting experience, and if you are really a novice, could be problematic. I wish that the teaching staff would go back to filmed lectures with a real professor.

Es un buen curso al principio, un poco básico. Te enseña a instalar el programa R y GitHub, siento que si no estás familiarizado con programar, puede que no te sirva mucho. Por el otro lado, si ya lo sabes hacer, puede ser que sea repetitivo.

Acabe el curso en 3 días, así que es un poco breve para el tiempo para el que está programado.

i felt this course was very disorganized. It introduces terms and concepts not explained before. There was an assignment on creating forks. This process was not in any lecture. Of course, with R and GitHub you can find anything on internet.

The lectures narrated by Amazon Polly is very boring. It also messes up some of the terms.

It's ok. After the first lesson, I should be able to provide a clear elevator pitch with a high level understanding of what I can expect to accomplish (4 or 5 steps) as a Data Scientist. Instead, there was one slide that touched on this quickly, somewhere in the middle. What are the problems, how do I solve them, give samples.

I guess this is the basis for the work to come in the program, but it should be called "installing RStudio and using Git". Not useful for anything but getting ready for real work. The videos were annoying - I just read the text instead of listening to the computer voice and having to pause and restart as I did the work.

The first course is composed in articulate way that allows a simple and schematic way of comprehension, but some single parts of the first course seem to be lacking of some information, above all to me without previous experience in informatics tools like github. Maybe I suppose to master this tools too long in advance.

literally i lossed the feeling of real time learning and it seems like just reading. i really do not like this kind of teaching style. infact direct teaching by the instructors is being a good experience rather than using this kind of technology

i reas all the course content, without listening by recorded speech

Good introductory course for the specialization.

Video' probably need an update as they're all cut in the end.

Also some more background and a little more extensive lectures would have been nice.

Anyhow, it did the deal: an introduction to R, RStudio, Github and Git.

Curious what the following courses have to offer.

The course size is pretty small compared to other courses I joined in Coursera. It took me only 3 days to complete the 3 day course. I think all setup guides should be left as assignments for students with some links where we can refer to on our own. Anyway, the course finally convinced me to start using R.

Instructor speaks very fast so that i read subtitles instead of hearing what he say, in addition to i stop video more times to understand what he say, but totally the scientific and technical contents are great also his advises for us in which how we can find the answers for our questions about data science

The part of explaining Predictive and Inferential Analysis is confusing. I think it won't hurt to give some more specific examples and methods used in each case. Both types of analyses involve sampling, so I think it is necessary to keep it clear how that sample can be used to either "infer" or "predict".

the lectures are full of the Prof misspeaking, this leads to you not knowing how to complete the task because the Prof can NOT communicate properly to us while we listening to the lectures... it seems like they completely missed the fact that NONE of us are data scientists...

Now your Audience......

I put three stars because it should be specified more how basic this course is, is almost that this is done for somebody that doesn't know almost anything from CS. So it should integrated with other kind of specialization. I hope that the following courses have some serious math and advance topics.

The course goes over the basic toolkit for data scientists. Overall it seemed too easy and maybe a bit simplistic. I was expecting more. There was a lot of optional reading made available in week 1, perhaps some optional assignments/quizzes related to that reading could be added to the course.

concepts were very good but teaching method/material must improve...some of the materials and methods used are too unstable to be useful for professional use...more work should be done by instructors to separate the 'reliable' concepts/info from 'interesting to know but not ready for mass use'

Very basic material, but a good introduction and a necessary step to ensure a baseline of knowledge for future courses in the data science specialization. I would only take this course if you are interested in the specialization otherwise save your money and google the info you need.

Insufficient available project available for review and thus unable to pass course due to technicality. This is a major problem. The course should still be passable even in the absence of sufficient other projects to review, which is a problem that no student has any control over.

I was a little put off by having to grade my peers and it felt like the final task required a few bits of information that hadn't been previously covered. I felt some more examples could be useful in getting people adjusted to GIT. Not a bad first course but not what I expected.

The materials are good, but it felt like this class should have a been a 1-week introductory lesson to Data Science. It is definitely now a 4-week class, maybe a a 2-week one if you take very easy. You end up with a basic setup for the next class. That I found very useful.

Very light & not really consistent with the heavy workload of subsequent courses. Felt it could have been much much stronger explaining GitHub- which shows up as a problem in latter courses strongly suggesting that toolbox does not effectively cover GitHub for newbies

One thing to note, I am using a work computer, so our IT support had to add the software required. This was inconvenient for them because I had to put in several support requests as I progressed through the course even though I installed as much as I was allowed to.

I think the course was too superficial and didn't cover enough topics to be a standalone course. It could be part of a greater course. My feeling is that this wasn't worth $30 dollars, and that such a small course was put together just to charge for one more module.

As an intro, this course is probably pretty good. I, however, already had experience with R (although the refresher was useful). However, if you've taking a data science or machine learning course recently, I'd give this one a pass and head on to the next course.

Thank you for the fantastic effort. Here's some constructive feedback on the course.

It's a very basic course, could have included more material. Also, the audio quality is not that great. To make it better, I'd Include more walkthroughs for Git and GitHub.