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Hi guys. I'm not sure that you are reading the feedback, but instead of saying that it's good or bad I'm going to come up with suggestion. I'm data pipeline architect with 20+ years of experience who decided to take these courses to understand a gap of knowledge that current data scientists have. I think it comes from the very course. The R is kind of out dated for current world of big data, but I think you've already heard about this. Some Data Scientists who show up in our company they are good with theory but very bad in implementation. They don't understand the Big Data, especially distributed data. R is good language to the lessons but it doesn't have any connection with real world. You better include some basic knowledge about Spark (especially Spark ML), distributed computation and finish with R. Most of data science algorithms and libraries implemented (and used by real world) are in Python. Contact with some Cloud Providers like AWS and Google to create accounts for education. You course would be more attractive and, what is most important, would be more useful for people and companies where they start to work.

If you want, you can contact with me about some volontier consultancy. My email is nick.orka@gmail.com

Generally good information, but the static powerpoint videos are a bit too vague to be useful. I ran into issues several times when the steps described in the video (sometimes providing incomplete terminal or git commands) didn't coincide with the steps described in site specific tutorial videos recommended in the course forum. So I ended up spending a lot of time 1) figuring out the full commands required, and 2) reconciling conflicting sets of instructions after receiving error messages. This mainly occurred when trying to get my local git work to correspond to GitHub. I suggest more actual demos in the video lessons (i.e., actually type in full commands, show the result, and explain how to interpret those results), or 2) explain overarching concepts and then simply list a set of existing online tutorials that should be followed.

This course lacks the inter-activeness that holds up a class. Even the material was not worth the money as it just teaches you to install certain programs and exactly what one can do with it. I feel there is enormous scope to improve this course in particular.

Things I will suggest:

1. The installation process is not shown with a depth. I feel increasing video size wont matter as reducing and removing certain important points that students should learn. I hope a better depiction and graphical representation of such an amazing subject can be done.

2. The problem with coding is that though they told how to make certain thing bold or Italics not actually it was shown as to where to put this.

I hope that everyone gain interest in such a subject.

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

The course begins with the instructors' explanation that they created the course using text-to-speech instead of recorded instruction with a real person in order to more easily keep the code and instructions updated. However, at least in my experience, some of the instructions seem to be outdated and do not work on Mac OS. This meant I had to look to other online resources to find the needed instruction, as well as go through a trial-and-error process on my own, which was time-consuming and challenging. I would recommend that the instructors' update the course text, since the content of the course is already satisfactory.

I was really exited about taking this course as a way to learn how to use R for statical analysis. I was first surprised that the course is taught by a robot and not a person. After getting over that, I realized this class is not meant for beginners. I am someone with no programming background, my husband who is an engineer has had to do the class with me for me to be able to complete the lessons. It seems like you need some programing knowledge to be able to follow the instructions. The positives are that this course teaches you how to install R and R studio. I am hoping the the following courses are easier to follow.

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.

it was little bit irritating if i may say so learning from robot. It actually made me think twice about course. Generally students find it difficult to understand or to regularly attend any online course due to a little bit of laziness or the dullness of the teaching person. Nowadays every online course and platforms are trying to make their class more easy and fun to learn. But in this course ,i know but can't understand the need of such dull method to teach in class. as i was hoping something innovative and more of a traditional way to learn from the class.

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.

This course talks about the basic of Data Science theory and makes you install R, RStudio and create a Github repository.

The theory part is really nice and well presented. However, the "practical" part is boring (tutorial of downloading installer and clicking in "Next", and the tests ask about RStudio interface details, such as "Which of the options below is NOT a way of creating a new project in RStudio?". And we install R and RStudio, but we don't use them. We see a bit of R Markdown, but nothing of programming.

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.