Overall, it is good. The final assignment managed to consolidate everything that we have learned. However, I think the R-Studio material definitely need a lot of improvement. I barely kept my eyes open, the instructor speaks in a non-engaging way, maybe, it becomes very 'dry' because the student cannot clearly sees what he refers to (the screen so small with white pointer)

I think there is plenty of value in this course, but it's hard not to be aware that it feels like IBM discovered "inbound marketing" and realized this platform is the best home for their style of campaigning.

I think the strongest aspect is the introduction of time-saving tools that could certainly enhance your workflow; that is, if you aren't already familiar.

Slightly IBM oriented. Mainly focuses on Jupyter Notebook and Watson Studio on the IBM cloud platform. These two tools are extensively used throughout the courses in the specialization. Since this talks about "open source tools", I was quickly able to find the source code of Jupyter Notebook. However, I was not able to find the source code for Watson Studio.

Course was great., good content!

However I was incorrectly graded in the final assignment hence weighing down my aggregate score. Though it didn't affect my much, it might have affected someone who had correctly answered but was wrongly marked by the grader whose understanding was not clear themselves.

Please have a look at the peer-graded evaluation system.

Some of the course content was not really valuable on a theoretical level. For example: just listing Python or R libraries and then asking which library does what in the test assignment tested more the memory of participants rather than actual skills or understanding. Without proper hands-on experience, this kind of theoretical knowledge is quite useless.

Good as one can expect from Coursera.

However, one comment: the tutorials are based on an old version of IBM Cloud: version 2 if I remember well, when now it is version 3.6. So the pages are slightly different, and it makes it sometimes confusing to understand where to navigate for a student who was a beginner - which was the course's targeted audience.

at first i found it hard to relate to real life examples and the applications of the tools then after the peer garded assignment and working on the exercise following the steps that were so clearly explained and reading and understanding the Markdown Cheat Sheet, things got clearer and more interesting.

Looking forward to learning more about the subject

Very informative yet easy to follow. One minor gripe I had with the course is that the tutorials for IBM Watson Studio (in week 3) were outdated, and the menus shown in the tutorial videos were absent on the actual Watson Studio website. As a result, it took some additional figuring out to perform some actions that were being explained in the video.

Well, very good course. The problem not to give 5 stars is that you show a lot of tools and no comment exercises to develop our skills. The videos are great, and the last project also's great, but only show the tools is not a great thing for the future Data scientist. So, next time, put some activities or commented examples, like the last project.

It starts from very basics so that very easy to understand and apply. However, education materials in videos are not updated so it makes difficult to find out where to an account create a notebook. For promo code, screens are totally different. I cannot find where to apply for it on the current version of IBM DSX. Hope, It will be updated asap.

It was great learning in this course.This course was introduction to tools being used by data scientist.From various discussion threads and my experience in this course, I observed that coursera should update the videos specially for Data science experience.Bcoz lots of students are facing issues on low level works like creating notebooks...

I found several concept very unclear in this course, for e.g. asking us to see a sample notebook,article from IBM community and follow it from start to end. As a beginner to coding and data science this wasn't very helpful as I couldn't understand the concepts. A beginner level tutorial or code present for that would have been very helpful.

The final project needs to be more descriptive as to what is needed so people know what to expect for peer-reviews. Example: Do we run the markup code or show it pre-running so people can tell the header level? I thought the section on GitHub was pretty helpful. It might be useful to tell people how to capture the URL for files on there.

honestly, it was well taught. but the key problem was that the videos by mr. romeo kienzler were a bit advanced for my understanding as i had no prior experience of this and i tried to replicate the the things he was doing and couldnt understand at all what im supposed to do ahead of somewhere. mostly this problem was in Github and Rstudio

This a great introduction to the three major open source use for Data Science. Don't expect too much. The individual tools discussed here is more of the high level usage and background of it and has some basic coding exercises. If you are looking for a more detailed course for one of the specific tool, you should find and invest for that.

Only issue we ran into is that there have been some URL and naming updates that aren't reflected in the training yet. Such as DSX is now call Watson Studio. The data science work bench also does not include Rstudio at the time of me writing this. I believe they are in the process of an update that is why the link to RStudio did not work.

The course is structured well to get introduced to various open source tools. A minor issue is that markdown cell commands(such as Tables) for Jupyter notebooks as explained in introductory lessons does not generate results as expected. This leads to confusion, as a learner I am not sure if have followed steps correctly or its errata.

Good overview. However, the lab instructions have not kept up with the evolution of the applications used. For example, Watson Studio has changed its UI making it hard to follow the lab instructions that are based on a previous version of Watson Studio. Also, I had trouble linking out to Jupyter Notebooks from the Coursera page.

This is course is for the beginners to the data science and have no idea of online open source notebooks like jupyter notebooks etc

It's a good course and introduces all the data science open source tools u would need to build a real time model.

Don't miss any part ,go through every part.

It was fun learning !

happy learning :)

Had difficulty with creating account on IBM Watson studio and getting the lab to work correctly. Instructors were responding to newly created questions and not to responses. I had to figure it out myself to finish the course and I am sure lot of other students are struggling as well. Please improve to make this course get 5 *.

Good course but some portions are in dire need of being updated to match the current systems. Other than that, I do feel like I got some exposure to open source tools for data science. I wish there was a mini assignment like the last one for each major tool introduced to help gain familiarity though. Overall, would recommend.

The course was really great although some parts are outdated and it's somehow confusing to learn from a video that doesn't match the tool. I also had trouble working with one of the activities because the material isn't working. Nevertheless, I still enjoyed the course and learned a lot from it so overall I'm still satisfied.

The course is good to learn free tools for using Python, R, Scala. There is one confusing thing in this course: it describes an earlier version of IBM Watson Studio and the changes between versions are significant. So you'll spend some extra time simply to register yourself and get to your tool.

The rest is fine!:)

It was lots of new information that I think it was too much for someone who is a Data Science dummy. Also, the examples doesn't describe what is the point of the evaluation, only said put this code, put that code and see the results. I like to know a little bit about the set of the data and what is the target.