This should be at most a 1 week course, that is free. Half the course is installing Rstudio and signing up for github. The other half of the course is simply learning what each course down the pipeline is about. Those lectures could just be tacked onto the description of each course and you would get the same thing out of it.

I think this course would be improved by more instruction on what git is and how to use it and maybe going over some fundamental statistical topics.

This specialization is highly rated, but that was the past. The course is decent but the Amazon Polly robotic voice is horrible. It needs a lot of work before being able to give the right type of learning experience as a human lecturer. Shame.

Also, specifically this course - I haven't seen a single things that is so "new" that the team could not keep up with normal videos. Could have used the old videos or update every few years instead of this ...thing. Very sad

I've learned many interesting things concerning github, but the concepts taught in videos were kind of inadequate in some cases. I had to google some things in order to understand exactly the concept. Finally, I am not pleased with the peer graded assignment because even though I had it all right (Correct), someone gave me low mark resulting in stuck in the final stage. I had to re-submit THE SAME assigment to mark me again, and finally recieve the certificate.

This is definitely not a beginner friendly course. Only definitions were covered rather than actually showing how they work. Github part was confusing. Markdown part was not explained at all. You need to be good at searching for your query on google if you want to take up this course but that's something you need to do for every course out there. No single course is complete but still, in this R course you need to be really good at doing google search.

The course was exceptionally planned and executed. One big problem that I had with the course were the automated videos; I can't say for others, but I wasn't at all comfortable with an AI voice teaching me the course content. I don't think I can ever get used to that. I would rather take a course that's 10 years old, 80-90% valid but has a human teaching me the course content. Other than that, I enjoyed the course. Thank you!

This course accomplishes what it says it will, but boy is it boring. If you are not already experienced in data science, it also requires a fair amount of imagination to picture what all these tools are actually used for. I would have preferred to have these tutorials integrated with the more substantial courses in the specialization. That way I could see how these tools fit in to an actual project.

The course dealt with basic software issues on getting you ready for Data Science, and discussed briefly more conceptual topics. The contents of this course by no means would take 4 weeks to complete from anyone, so I think the time span to take this course is exaggarated. Still, it is very important course to get you started, if you are complete stranger to R, RStudio, GitHub and R Markdown.

It has a lot of potential but it would really benefit from slowing down and explaining steps a little more slowly such as integrating R Studio and Git, and how to use Git and push from R studio to Git. For an online course, there's no need to rush through this content and judging from the comments in the forums, many people have problems with their instructions, or lack of.

There is a pretty big jump from the content to actually completing the assignment. The assignments are not well aligned with the swirl learning or the videos. There is no logical process taught about how to move forward if you get stuck. It often means a student is forced to search the internet and hope the answer they find is appropriate so they can write their own code.

I liked the course, but I'm still quite uncertain on many aspects; feel like I have a lot of grey areas. I think adding a small project video, and how the different tools (RStudio, GitHub, GitBash etc.) come together on a project would have been powerful to clarify how this comes together during a project. Not for us to learn or emulate, but to understand the big picture.

Establishes an overview of what Data Science is and introduces some necessary vocabulary. The installation instructions and github setup will bore IT-professionals to death, but my be useful to other students. The final test should really be scored by a bot instead of other students of this course -- as it is, it needlessly wasted my time with clickwork.

I think first course is only about installing, installing and installing. If they cover more introduction to the field rather than only installing that would be nice. But to show how to install and perfectly run the software is very necessary and the did that job very nicely.

looking forward for the next one hopefully that'll be also good as this. :)

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......