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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.