This course was a great intro to these concepts and helpful guide to getting things set up and getting used to the MOOC format, as well! A few times it seemed like the slides jumped right in while skipping over a bit of context, but was able to orient myself with some googling and asking friends some basic questions to figure things out.

Overall, wonderful course - but I would request that you change the signature name on the certificate from "John Doe" to a real professor's name. Having John Doe on the certificate makes it feel inane - I would never show this to somebody with whom I was applying for a job, for example. Other than that, loved the course. No issues.

Great course. The only gripe I have with it, is that sometimes the same question is asked during the tests after each module. Also, I got a lower grade because only 2 people graded my work and 1 made an error. I did get half of the points as they averaged, but still. This however is with Coursera I imagine. Definitely recommended!

If i could redo this course, I would have taken it simultaneously with the introduction to R course. On it's own it feels like a grab bag of information and it felt like I was delaying getting into the meat of things. That said, the information itself is very important and I found myself referring back to the lecture notes often.

Good introduction to basics. A few improvements are warranted:

Lessons could be reordered in a more logical progression, particularly when it comes to Git.

Gitbash is not easy or intuitive. A more structured approach (e.g. with cheat sheets, command glossaries, structure diagrams, debugging algorithms etc) would help assimilate it.

The course is excelent for people who don't know anything about R, Rstudio, RmarkDown, Git, GiHub and other tools. However, for people who already know a little bit of those technologies, they will find it a little repetitive. Anyhow, the classes are awesome for you to get to learn to use those tools. Congratulations to the team.

Automated lecture are made using difficult english to understand, it feels like that robot keeps speaking continously without a stop and also the presentations in the videos makes me feel sleepy, if you use facecam that would be better for the learners but not for you i understand that. This course is little far from perfection.

The content was clear and easy the first three weeks. But it was confused to me at 4h week and for the final presentation it was a lack of clear instructions, I was so sad because I had many troubles at the moment of commit, push and fork a file, I had to find external help and I thought I couldn't finish succesful the course

Excellent Course, very simple to understand and concisius. If you wish to learn data science and you dont have any idea about it this this is your course. Only the part of Git I wouldl like to be more explicit, because in one part there is not very clear how we have to create a text file with extension .md using Github. Thanks

Course was pretty good but the later lecture videos go really fast and are hard to keep up with. The main problem I had was when it covered R markdown it made no mention of having a LaTex program to create the pdf, I had to spend some time figuring out how to install and get one working in order to knit the markdown file.

What needs to be made clearer is the need to go looking around the internet for help on the Git to Github work. I can see that one taking some time for students to work thru. On the other hand once students go throw the trouble of doing the research and working with the code/commands a strange thing happens - learning!!

Expectations for simply meeting the baseline learning objectives or to outpoint it aren't exactly clear and there are two monitor strings that are far from being clear (15 minute guide on xyz).

Content and evaluations match in requirements. I learned a lot about softwares and databases in with which I can learn and work.

A high level view but very helpful for someone starting their Data Science journey. Good overall coverage of basics that helps in building a gradual understanding of the subject.

The only reason i haven't rated 5 stars is because there were lot of errors that i came across in the automated diction that need correction.

Organization of course was good. Sometimes, I felt that speed of the lecture is fast and I had to reduce the speed to 0.75% to understand important concepts. Improvements can be made in the transalation of text by robot, 'e-g' was being translated to EG instead of for example. Overall the content of the course was good

A brief introduction and overview of data science and the specialization from JHU. It provides necessary information and materials for the following courses, but itself does not cover much technique details. Won't take long to accomplish but still necessary if you don't know Git, Github or background of data science.

very basic when you have at least some science background in terms of a Bachelor + almost Master Sc. degree, but good for repetition, Git Bash and Github was completely new to me, at the moment I am not 100% sure for what Github and Git Bash are useful, but I am sure I will figure it out in the upcoming courses :)

It was good and it helped me to explore github,git,R and Rstudio. The peer assignment was quite good as it was my first peer assignment..But,only thing is that instead of this format(using AI),U can use on-person teaching which will be good and interactive..

I felt sleepy with the crampy female robotic voice

Found that the automated lecture didn’t deliver the message as well as a traditional lecture. There was awkward delivery in terms of speech and phrasing from the automated lecture and I found it distracting. But the material was great and I feel prepared to start the rest of the data science specialization

The course overall has been helpful in getting started with R and data science as a method of analysis. But the robot voice is extremely difficult to listen to. To the point where I am drifting off because it is so monotone, and sometimes not interpreting the content correctly due to a weird pronunciation.

The course is pretty interesting, but there is not much substantive knowledge here. For that you must keep going to the other courses of the Specialization such as R Programming and the others. There you are going to learn data science in practice. Nevertheless, this is a good introduction to the topic.

Good course, that brings goos insights on the basics about data science.

The lectures about Git and GitHub are not so clear - maybe this classes would better fit when the class already have a more advanced knowldge on the course's theme.

Thank you for the quality of the lessons and to make it available.

A good place to start of your entry to Data Science. You get to know what data science is, what are the tools used and get an idea of what can be done and cannot be done. The course even walks you through installation of r, rstudio, and git. It introduces version control system using Github too.

I really wish there were a few videos with real people in them. That computer voice is annoying, but the material was covered thoroughly, and I used the text option which actually was great. I also think it would benefit students if there was a book or some form of notes they could download.

A Good introduction to data analysis theory and tutorials on getting started with Rstudio and git installation and initial usage techniques. Consecutive course to compliment this would be R programming and Data cleansing and exploratory analysis as in John Hopkins Data Science Specialization