Extremely comprehensive. Well written and filled with interesting resource links for relevant content in nearby sites. While initially not expecting the Amazon Polly-based script reading, I came to like it; i.e., the fact I could first listen to each lecture adn then follow it with a review at my own pace of the same material visually.

I withheld the fifth star only because there were some broken links or content that could stand a review for updates, and possibly the tools have changed their behavior a bit, so that the steps provided probably need some tweaking. Otherwise; thank you!

Solid starting class that highlights the fundamental software you will be working with for the Data Science Specialization. It holds your hand at the beginning, but familiarizing yourself with the software may lead to a few bumps in the road.

Some of the issues were due to unclear directions and, at one point, a needed additional package to knit PDFs from RStudio R Markdowns. While the forums are not very active, there are a few people who might be able to help you. Also, Google (along with the listed Data Science forums) is your friend when looking for answers.

The course was really constructive. However, for the students who are really new to coding, courses where creation of git hub account or coding to push/pull data from git hub is involved, i would suggest to add more videos related to step by step coding. Also, there are some irrelevant questions involved in the weekly quiz which are not very fruitful when it comes to learning this course. I would suggest these questions to be removed. Team can contact me on my email Id if they require complete feedback for such questions.

Hence I would rate this course 4/5.

I found this course a fairly easy introduction to the tools you will need for this series of courses, however I already had a rudimentary knowledge of Linux and Bash Shells. For the computer novice this may be more daunting. The one area that is worth spending some time on as an investment for the future is git and git-hub. Understanding how these work together is not transparent. It took me a while to figure out what I had to do to push committed files to a remote site. I think suggested reading could be more specific to guide people in this area.

The course was well structured and I compare him with two other beginner courses for data science. So far, the best one. I prefer following my own pace and opt for either video or script. The audio voice was a bit monotone but otherwise ok. I liked that there was some attention to data analysis background at University level but it was pretty basic. The final project was good since I had to redo it a couple of times before I submitted and those skills are now pretty permanent I guess:) Thanks for the clear (most of the times) guidelines.

The Data Scientist’s Toolbox is a great way to dip into Data Science and the methodology behind it. The course is very general, and makes an effort to cover the bigger scope of things without delving deep in any. More than anything, it's a great way to learn the components and uses of data science and set a framework for all that will be coming after.

The materials are very well laid-out and almost feel like attending college classes. The visuals and slides are a little dry, but the pace is lively enough to maintain momentum at all times.

A light introduction to the Data Science field, in many ways it can be difficult for inexperienced people with software or inexperienced with stats - in my case it was not very difficult since some of the topics were already familiar.

The course can be done in a couple of days if the topics are already familiar, in my opinion the course's contents are perfect for someone very new to this field.

I would have rated more stars if the course's content was more "objective" for people unfamiliar with the subject - other than that it's perfect.

Overall a good course and got me to understand and do some things that I have never done related to data science. As a result, I would recommend it and will be continuing with the specialization. My recommendation would be to add more descriptions and explanations to make the material clearer. For example, sometimes it wasn't clear to me in which program to run a requested task, or what it means to fork. In order to to get through the assignments, I had to look certain things up on the web, whether websites or YouTube.

I got some bugs when running git bash and I had no clue how to fix them. I kept watching videos over and over but I couldn't find the answer. Then I tried google, reading many websites and doing trial and errors until the errors were gone. I understand in real life this is what I need to do but it would be good to know some proper ways of fixing errors after I submitted the assignment. So I can learn and use in future! Apart from that, the course is really useful and prepares me for the next stage.

It is a very good introductory course for anyone who would like to learn data science, or would like to use tools in data science for their everyday work (like me). In the course you will have some general essential ideas about what is data sciences and which tools are used in the science, and the most importantly, the concept of version control and the GitHub tool for the purpose. However, there will not be any in-depth knowledge in the course, which is determined by the introductory nature of it.

Videos are interesting, but auto speaker is too fast to track, I think it should be slower between some sentences and paragraphs. Links should be added to the page which is talked in the videos. And it would be better with extra reading sources. I get to ask questions to students which they don't teach at class. It makes you think about what you've learnt and it is a learning progress I believe. But if they add extra-reading links and ask also from there, that would be better way of teaching.

Es un muy buen curso teórico práctico sobre la introducción a la programación en R. Sin embargo algo que me desmotiva mucho es que las constancias no proporciones ningún crédito. Éste curso tiene una duración de 4 semanas y no me parece justo que no se tome en cuenta si se acreditó correctamente no tenga ningún valor. es el segundo curso que tomo en coursera y me pasa lo mismo, dos universidades renombradas que no proporcionan ningún crédito. ¿Cuál es el objetivo de ofertarlo entonces?

This course is a pretty good introduction to data science, although I'm not sure how useful will these tools be in the future. Also many tutorials were unclear and I had a lot of problems to complete some lessons, I had to solve those problems by myself (difficult task sometimes considering I know nothing about data science). Anyway, I learnt a lot of stuff, in the end I recommend this course for an introduction, but check the content first to see if it suits your learning interests.

On enrolling in this course I knew nothing of the data science world and always wondered how all that "jumble" of data was organized. After this short course I am beginning to have a glimmer of how this is done. I know there is more to learn and I am curious to know how. I must say that I struggled quit bit towards the end of the course with the assignment, but I believe as I continue with the other courses I would become more proficient using RStudio and GitHub, etc.

Important note: Opt for this course only if you plan to do the entire specialisation. It is developed solely for the specialisation and not as a standalone course. You will not learn much concepts or theories or practice any R programming here.

In general this course was basic and good enough to get someone started for specialisation. The video lectures for setting up R, RStudio and Github account were helpful and very basic ( maybe coz I already had an github account).

A lot easier than I expected. In particular, compared with courses where one final project took longer than the rest put together, the project is just to put together your R/Rstudio/Github environment. There's a lot of value in that; plenty of courses you learn the concepts but don't add anything to your repertoire; if a year from now I'm putting a little project on my new Github site now and then, that will have more actual impact than a lot of harder ones.

Course was very interesting, first 2 weeks were fantastic, but it will be better if they will give more information about Git Hub and its functions, because in some parts it is hard to get tasks done and some parts of Version Control lesson are not very clear. And i would say that using A.I and make courses is almost brilliant idea, but the voice is sometimes terrible and it gets hard to understand, also usually people getting bored because of monotonic voice

I wish this course would spend a little more time upfront saying at a very basic level what data science is and gave some real life examples of data science in action. Most of the course is configuring software that you will be using down the road, but it would help to know why you'd even want to use the programs and in general, what their capabilities are before you get into setting them up. I imagine that latter course will do this (at least I am hoping).

I would have preferred more hands-on examples or projects for each week's lessons. As an entry-level practioner, this course felt thin. There is a lot to absorb and while my interest and curiosity is peaked, I've found I've been able to grasp a better understanding of the material outside the classroom rather than inside. This is ultimately a good thing but I would have liked some more "hand-holding" from the course to feel more comfortable moving forward

I liked the course the link is great between R and Git, it was extremely important. The explanation is gradual in some chapters, but in others it advances and does not show how it would be when making some modifications. I think it would be nice to be as specific as possible, so we don't waste time with simple questions/actions. There were also some test questions that don't appear in the videos or written material, it would be nice to highlight them!

Great intro course. Got me in the mood, established the perspective I should have going in. I felt that some of the lessons like R Markdown were a bit rushed and at times I felt like I was just following along without understanding the commands and ideas. Perhaps that is simply a lack of emphasis or a lenience in tone (like don't worry, we'll look at this later), but in that case I suppose it did not translate through the synthesized speech.

Great course and content overall. I understand why Amazon Polly was used for the course. However, it is difficult for me to focus on learning from a robot voice. I prefer a course lecture to be done by a real person. Again, just a personal preference, and it didn't take away too much from the learning experience since it's an introductory course. I am excited that the next course in the specialization does not use Polly though.

Although the modules were supposed to teach us how to setup RStudio and git on our computer, I found that they ignored a lot of common errors. I use a Mac and had to spend a lot of time on the web looking for answers to how to complete the setup. Although the solutions were pretty straightforward, it took me a long time to identify and pick the right solutions, the right downloads and the right tweaks to get everything going.

It's a good course but there are some things that can be improved. Sometimes the quizzes asked things not mentioned in the lecture. This is confusing for some of the students. I get it, we are supposed to try things out in Rstudio ourselves. Nevertheless i still think that the right course of action is to make the course material more detailed and thorough. Just give us all the explanation, don't leave some things hanging.

This course taught a great deal about setting up the environment needed for the course, but glossed over fundamental aspects trivial to good data science. I think incorporating more questions around the why around data science would've been good to incorporate as an extra week in the course. Nonetheless, it taught a lot of good fundamental aspects of data science, and how to begin approaching the way we think about data.