Отзывы учащихся о курсе Набор инструментальных средств для специалистов по обработке данных от партнера Университет Джонса Хопкинса

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.

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

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.

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

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.

Although I found the course material in general clear and well-explained, I found the lecture on 'Basic Git Commands' poorly explained and sometimes poorly audible as well. For a non-native speaker of the English language it was hard to follow.

This is a good course for someone who is not familiar with the basics of Git,Github and needs to install R,Rstudio and related packages. If you are not the kind of person described above you will be able to breeze through the course very fast.

The course is very fluent and attractive. You may run into some questions while following the course which you can easily find the answer to by googling it. As a beginner in both Data Science and programming, I'm very happy with this course.

The lectures were good.After all it's robot orienting converstaion it has lot of pace in speech I think that is not good for me.Because It was little bit hard to grasp the message.The pace is only the concerned.Overall lectures were good.

There are some typographical errors in the quizzes and the english subtitles. Not really a big deal. The Week 2 about GitBash made me a little confused because the video about loading git bash happened before the video about installing it.

I am giving the course 4 stars because it is online. should it be practical, then it would have earned 5 stars. Some of the concepts were unclear especially "Experimental Designs". Hope there will be more practical examples to work with.

I don't feel as though the 16 minute video on command lines was efficient. I spent a gross amount of time (over 3 hours) on youtube for supplemental instruction just to complete the final project. Otherwise, this course was sufficient.

A very good course for beginner to start off with. This course really helps setup the fundamental toolkit to create a efficient workflow. The git/github version control linking with R/Rstudio is the best thing I got from this caourse.

Excelent course. The schedule was basic however have approached a thematic complex and important.

The time to complete the tasks week was great.

But I felt need to learn more about git and github. I don't know if it was on follow weeks.

Easy to follow. Might be too easy for some people with experience in data analysis. However, the instructors also talk about some frameworks and insights from their experience which could be helpful for even those who have experience.