It is a good start to data science, you don't need a background in programming. The course is aimed at 1) helping you set up R, RStudio,git and conect it to GitHub and understand it's basic functionality and 2) getting a basic understanding of what data science is.

The course covered most of the necessary tools in the Data Science industry. The content is clear and very easy to retrace the steps. Git version control required a deeper explanation of undoing a commit, branching and merging. The rest of the content is flawless.

The track consists of 9 courses that each last about 4 weeks which are released in batches of 3 courses each month. This course introduces the very basics of R and R studio, Git and Github and a few otherthings that will be used in the data science specialization.

A little daunting at first but the instruction is simple and the ability to search video transcripts for tidbits basically saves me the step of taking notes. Some of the navigation was difficult in the forum, but I ended up figuring it out and posting something.

Very helpful and important introduction to the world of Data Science. I do feel an overall lack of more examples and/or small optional projects/exercises to help learn, though. Maybe something like a list of exercises could be given at the end of each Module?

This course gave me a thorough introduction to the use of the tools in preparation for being a data scientist. I also had a good grasp of what task a data scientist faces on job. R, RStudio, Git and GitHub are nomore nightmare for me having taken this course.

it implies statistical concepts that, I understand because I have studied them deeply in my college, but for someone who has few or no knowledge about the subject, it can be very complicated. still its very complete and I learnt a lot about R studio program.

The course is suitable for browsing in one day. There is not much deep knowledge, only some shallow concepts. I feel that as the beginning of a group of courses, the concept introduction is not too comprehensive, and many aspects have not been introduced yet

Good starter content, data science background and overview of tools. Could provide more lecture time on the tools (RStudio, Git/Bash). The course is labeled for beginners, but I can see where someone without much experience could really get intimidated by

The course provided a good overview of Data Science and the various tools used to perform the work. Those without an IT background may find it challenging to install and configure some of the required tools. But you can find plenty of tutorials on YouTube.

Well structured and nicely organized. Content is great and lays the ground rules for start of statistics using R.

Minus one start only because there's no instructor teaching the course. I would've preferred a real human voice rather than an automated voice.

The videos might not seem a lot at first view, but they contain links to some of the most useful material out there. The mentors on discussion boards are immensely helpful as well. For the uninitiated in data sciences, this is a great module to begin with.

Good pace for the first course. A little more guidance on Git command flow would be help. However, the available Help documentation on Github did the trick. The problem was having to work primarily in the command line which provides limited feedback.

This course has good content, but the instructor's voice is all done by AI. They explained early in the course that this is because it makes it easy to make changes to the videos, but it still creates a depersonalized learning experience for the viewer.

Well done, but very basic. Only do it if you are really completely new to the subject.

The audio part is entirely done using automatic text reading. Very well done compared to other similar tools, but still the experience is not the same as with a human

Videos are already sort of "old". Having Macbook i had significant problems with pushing files to GitHub repo, nevertheless I was doing everything said in the videos.

I could do that after using some other functions that were not mentioned in the video.

It was straight forward. However, there were some difficulties installing RStudio using the latest version. I had to go the previous one .e.g. Latest was 3.5 I used 3.4 matching RTools. Other than that very straight fwd, including Github (basic) usage

I am not too thrilled about Polly delivering the lectures but I understand why they have to do it this way.. Still explained all the concepts clearly and I am confident that I will get through the entire specialization after going through this course.

A good course with clear instruction that gives you a basic review of using data and installing R and related programs. Occasionally necessary steps in R are omitted and one has to do some googling. Not a fatal flaw, but frustrating for a beginner.

It felt a bit like a RPG tutorial where your big accomplishment is learning how to run or crouch, but I guess it makes sense for it to be part of the Specialization track. I would recommend it as part of a series, more than as a standalone course.

Great introduction to the different tools that a data scientist will encounter and use, including RStudio, Git, and GitHub. I would have appreciated more practical experience linking Git and GitHub, as that is critical for version control of code.

I liked the course. I think that at times it is not clear and at others it is wordy. I gave 4 stars mainly because the course menu promises subtitles in Portuguese and in more than 60% of the videos there are only subtitles in Korean and English.

I had some issues in following all the steps that are shown in the lessons. I think the demos skip important steps. But, on the other hand, it forces to search and look for solutions to these issues on the web. Overall: good introductory course!

It's a good course, you're not going in-depth but this is just an introductory course for the Data Science master and the tools you will use. You will learn the basics of Git and get acquainted with R and is thus somewhat essential for starters.

Reviews or Test should rely more heavily on the instruction that goes into detail on a topic matter and questions that were asked on quizes. For example, the datasharing question was worth 2 points, when that topic was only cover very briefly.