This is a required course in the Data Science specialization. If you're planning to take that specialization, you should start with this course. If not, you shouldn't bother - there's not much here.

As a software engineer I found this course a bit useless, but I do understand other people don't have the same background. Having said that, I believe it should be optional in the specialization.

It's a great course. But the peer-graded setting of projects really delays the grant of certificate. It took me more than two sessions of waiting just to get a grade for the peer-review project.

Very basic, can be done in one day! However, we always need something simple to begin learning something new. Also, it was useful to have some guidance in the installation of the various tools.

The course is basic, and quite simple. Some might say too simple. I think it should be about 1 week long. I already knew all the things going into it so it was easy to finish it over two days.

The theoretical part of the course is good, but the quizz questions were simplistic and very hastily prepared. I would recommend this course as an optional module and not as a graded course.

It's a very basic introduction. Would be nice to have the option to skip it for more advanced students.

Also the recorded lectures are really quite, so you have to turn up the speaker a lot.

Good Introduction to the basic tool set, Can be bit more comprehensive by giving an overview of other tools available to do the same operations and also why this tool set has been chosen.

There wasn't much to learn that I didn't already know. There was a lot of talk but no real practical application (I guess except for github for people that didn't know how to use that).

This course coud have been provided as intro to R programming course or it could be just planned for 1 week. Anyway, overall it was ok though not yet extremely intense or informative.

It was a good overview. I understand that it’s really just a quick intro into upcoming specialization courses. I wouldn’t take it alone. But I’m excited for the rest of the courses!

Make it more illustrative.. The examples are very less and not explained properly for beginner. Assignments should be more practical. There must be a tutor to clarify the query.

A good attempt to give introduction on basic Data Science tools though most videos end abruptly which spoils the seamless navigation experience. Maybe the team can work on this.

Good introduction, but the professor could have been more descriptive about how to use git/github. These are new concepts for people so it may be difficult to grasp right away.

Probably a good course if you aren't terribly familiar with GIT or some form of version control. It also helps to get all the tools in place to be ready for the other courses.

Good outline but perhaps a bit slow going when a lot of people want to get into coding as quickly as possible- could some of the git instructions come further down the line?

The automated videos have seriously reduced the value for money for this course. With thousands of students, surely you can spend a bit more time making this presentable.

Good course for an absolute beginner, but much too light if you have any experience at all with data science or programming. Entire course completed in just a few hours.

It is a good intro course to get set up with everything you may need for future courses, but it's not necessary if you are already a little familiar with github and R.

The assignments were too simple to solve. Maybe 1 or 2 graded questions on Git/GitHub could have been added considering the importance of its usage in future courses.

I enjoyed the start up course and look forward to more, but am battling to figure out how to sign-up for the next step all roads seem to force me back to the Toolkit.

Nice to have a place to get all the tools setup but it's also harder to feel like it's worthwhile when there are no applications for any of the things we installed.

I think final assignment can be improved. For example, the assignment implies that you know how to generate a codebook.md and the video classes doesn't teach that.

For people that are new to Data Science this is a good intro, for people that might have some experience with R, statistics and ML in general this is way to basic.

I think this course could be integrated into the other ones of the specialization, or, if it is meant to be just a course to get an overview, be free of charge.