I noticed that w/in the course video's there were numerous cases of misspelled words and even some incorrect information. Regardless, it didn't prevent me from learning the material, however I would expect more precision from Johns Hopkins. Additionally, the narrator (at times) seemed as if he was getting exhausted/running out of fuel towards the end of each lesson. Some of the instruction isn't exactly clear, i.e. the instructions for installing R, RStudio & Git. Perhaps you all are attempting to make the student engage in some heuristic thinking? When it comes to a class like this, precise and clear instructions are a necessity, especially to novices. Regardless, I look forward to continuing to learn. :-)

From the basic layout of the course you would assume it's for beginners since it covers step-by-step instructions to install software and run command on command line window.

But on the other hand, many advanced concepts are slipped in this course without even basic introduction. I remember in one class, "data dredging" is discussed for about 2-3 minutes. But the instructor did not give a brief description about what it is, instead it just goes on about when you do not have clear question in your mind, you would run the risk of data dredging.

I think the course could be organized in a better way. But I do appreciate the instructors' hard work of putting up such a 10-course specialization.

The content is good, but there are numerous technical problems in the course. Frequently there are references to "copy the code" which is only present in a video frame, so you can't copy it. There are also references to "follow the link" and there is no link to click on. Sometimes you can type it in from the slide, but other times there are no links given. Some of the questions in the self-assessment do not have correct answers. I can verify this by taking the self-assessment multiple times and choosing a different answer each time and never getting it right. Some of the questions are also miscategorized in the wrong lessons self-assessment.

Candid but respectful comments......

Some of the lecture material seems outdated. What we are seeing is often different than what is being presented. Is your content up to date? Lectures are quite good. When we have a serious technical issue, who can we reach out to for quick assistance? In my case, for some reason when I attempted to download and open R Studio, download was fine, the file executed as expected, but the application was nowhere to be found on my computer. This is not good because I can't begin R pranking without it. I have reached out vendor and am waiting for response. Concerned. Other than that am liking what I see

This course didn't teach me much about Data Science or the different areas to pursue after this introductory course. I probably could have got as much out of it as following the tutorials on GitHub and the new desktop tool. Following the tutorial which was made for windows was also a bit annoying at times. Finally I had to wait weeks to have my assignment marked and there is still conflicting information on the course page - in 'grades' it shows I didn't pass the week 4 task. On other pages it shows I did and earned 41 out of 41 points. However I can't see a confirmation of course completion.

I don't agree with the order at which this course is introduced to learners. Why do we need to learn CLI and Git at the very beginning? Besides, everything in this course was sort of detached and sketchy. For instance, the intro to the types of analysis (e.g. descriptive, exploratory, inferential, and so on) is not covered well. In my opinion it would be much better to have an intro about the underlying theories and concepts in much more detail rather than learn Git command or learn how to melt and cast data (from Hadley's presentation).

The lectures are sometimes needlessly long with a lot of superfluous talking. The course would be better with more concrete examples and THE OUTPUT OF EACH INCLUDED. The course explains very simple queries and then asks you to do complex ones in the quizzes when the examples were poorly explained. Khan academy is a much superior course to this one, yet it does not offer a course certification. If this course actually wants to teach people efficiently it should emulate the real-time learning and coding in browser that Khan Academy has.

This course sets the stage for the rest of the Data Science specialisation.

You get a lot of textbooks for free and they cover a lot of material.

The quizzes are a little bit underwhelming, especially the first week. Too few questions, testing some questionable knowledge (eg, what other courses there are in the specialisation -- hardly a required tool in the data scientist's box).

Overall, it's a good preparation for what is to come. It managed to whet my appetite for more , however I'm not sure the course is very useful on its own.

I have to say the text to voice translation of the text to video lacks good rythm, sometimes it is not easy to follow all the detial espatially there never is natural pause after some touch ideas or steps to me. As a class of introdution level to layman like me I think it could be better introducing more practical examples to practice, or I would like to see some links to external study materilal, that would make the class experience even better to avoiding frequently searching for troubleshooting.

The initial lectures were good . The Git & Github part got me little confusing , a little detailed explanation with live examples would have really helped. The last conceptual part was interesting. Some reading material just to drill down on certain Data science jar-guns would really help though it is understood that best place to find answer to question is google / forums / stack overflow...Still some reading material would really be very helpful to maintain the interest in the course.

Basically if you take this course you are paying money to create an account on a website and download some software (both of which you can do for free). The rest of it is a preview of the other courses in the series. The quiz questions don't correspond to the information on the slides. I successfully passed the course, but I didn't really learn anything. Now I am debating on whether or not to continue to the R programming course after reading through the reviews of that course.

This is a good, general introduction. A motivated student can run through it very, very quickly. As the first of ten courses, I understand that it is a very general introduction. Still, I think it could be ramped up a bit. Week 2 of the next course - R programming - is kind of a kick in the head if you're not a programmer. I feel like some of the content from R programming could have be included in the toolbox course in order to take advantage of the relatively light load.

This should be at most a 1 week course, that is free. Half the course is installing Rstudio and signing up for github. The other half of the course is simply learning what each course down the pipeline is about. Those lectures could just be tacked onto the description of each course and you would get the same thing out of it.

I think this course would be improved by more instruction on what git is and how to use it and maybe going over some fundamental statistical topics.

The course was exceptionally planned and executed. One big problem that I had with the course were the automated videos; I can't say for others, but I wasn't at all comfortable with an AI voice teaching me the course content. I don't think I can ever get used to that. I would rather take a course that's 10 years old, 80-90% valid but has a human teaching me the course content. Other than that, I enjoyed the course. Thank you!

This course accomplishes what it says it will, but boy is it boring. If you are not already experienced in data science, it also requires a fair amount of imagination to picture what all these tools are actually used for. I would have preferred to have these tutorials integrated with the more substantial courses in the specialization. That way I could see how these tools fit in to an actual project.

The course dealt with basic software issues on getting you ready for Data Science, and discussed briefly more conceptual topics. The contents of this course by no means would take 4 weeks to complete from anyone, so I think the time span to take this course is exaggarated. Still, it is very important course to get you started, if you are complete stranger to R, RStudio, GitHub and R Markdown.

There is a pretty big jump from the content to actually completing the assignment. The assignments are not well aligned with the swirl learning or the videos. There is no logical process taught about how to move forward if you get stuck. It often means a student is forced to search the internet and hope the answer they find is appropriate so they can write their own code.

I liked the course, but I'm still quite uncertain on many aspects; feel like I have a lot of grey areas. I think adding a small project video, and how the different tools (RStudio, GitHub, GitBash etc.) come together on a project would have been powerful to clarify how this comes together during a project. Not for us to learn or emulate, but to understand the big picture.

Establishes an overview of what Data Science is and introduces some necessary vocabulary. The installation instructions and github setup will bore IT-professionals to death, but my be useful to other students. The final test should really be scored by a bot instead of other students of this course -- as it is, it needlessly wasted my time with clickwork.

I think first course is only about installing, installing and installing. If they cover more introduction to the field rather than only installing that would be nice. But to show how to install and perfectly run the software is very necessary and the did that job very nicely.

looking forward for the next one hopefully that'll be also good as this. :)

The course structure is really good. The content is good too. I found the speed a little too fast. Plus there should have been some small exercises in between before the quiz to make the lesson more interesting and intriguing. Another point that I would like to state is that, the slides could be even better and visually appealing than they are now.

The content is fine, as an introductory course, however, the computer generated lectures are a travesty. There are enough mistakes in the text-to-speech translation that make for a distracting experience, and if you are really a novice, could be problematic. I wish that the teaching staff would go back to filmed lectures with a real professor.

Es un buen curso al principio, un poco básico. Te enseña a instalar el programa R y GitHub, siento que si no estás familiarizado con programar, puede que no te sirva mucho. Por el otro lado, si ya lo sabes hacer, puede ser que sea repetitivo.

Acabe el curso en 3 días, así que es un poco breve para el tiempo para el que está programado.

i felt this course was very disorganized. It introduces terms and concepts not explained before. There was an assignment on creating forks. This process was not in any lecture. Of course, with R and GitHub you can find anything on internet.

The lectures narrated by Amazon Polly is very boring. It also messes up some of the terms.

It's ok. After the first lesson, I should be able to provide a clear elevator pitch with a high level understanding of what I can expect to accomplish (4 or 5 steps) as a Data Scientist. Instead, there was one slide that touched on this quickly, somewhere in the middle. What are the problems, how do I solve them, give samples.