A good overview of common tools used in data science, with an understandable focus on IBM Watson Studio and colleagues - it is after all an IBM course. Nevertheless, good pointers to open source tools too. In many ways a bit superficial, but I guess that's exactly the scope of the course - let the student know what tools are available. They can then decide what areas they want to look more deeply into.

The course was great, as well as the instructors. What wasn't good was the support in the forum with the Labs, I understand IBM Cloud is an independent platform from Coursera, but is IBM the one accrediting the course!, so I expect the support team in the course can help solve issues with the Labs besides the silly answers repeating the instructions of the Labs and can go further helping the students.

In my honest opinion, the beginning was a bit overwhelming. Keeping up with all the named tools which are just being introduced to me was difficult, especially when they're mentioned with the assumption that one already knows what it is. However, the rest of the course was well structured. Diving into specific tools and breaking down their main purposes and ways to utilize them was easier to follow.

The course is very informative on the subject of tools available for Data Science. However, the tools have upgraded their user interfaces since the course was made, and so there are a few visual discrepancies between what you see in the lectures and tutorials and the actual tools. This might cause some confusion, but none that will prevent you from actually getting around and completing the course.

Not as well put together as the first section. This was entirely lecture and slide based -- the first section (Intro to Data Science) had video interviews, etc., that made it much more engaging. This had lectures where the voice recordings and editing were very low quality and often different VO's on the same lecture. Not as well packaged and presented as the original section in this course.

I think it would help learners if the videos show step by step process of each software because the knowledge base is different with each student. There is also a discrepency between the current updates and when the videos were made. A copy or a handbook (e-book) to be provided will be an asset. This is to help keep the knowledge in sequence and provide aid for later review.

thanks

Asif

This course provides some high level information, but it could do with some more actionable examples. There were times when - during the videos - it would have been nice to "follow along" with one's own Watson Studio session. But because the instructors were showing data on their own accounts, this was difficult/impossible to accomplish. I look forward to moving on to "meatier" classes.

Class is a bit confusing because you're signing up for different services and using them. In the first week or two, you use one service, then in the middle weeks use a different one, but in the final assignment, you're using a service that you signed up for in the beginning. Still, pretty cool to get an idea of the open source tools available. This can could be better structured.

Gives an overall high level idea on Cognitive Lab tools, Jupyter Notebooks, Zeppelin Notebooks, R-Studio IDE and IBM Watson Studio. IBM Watson section is problem prone due to product update and the same not reflecting in course contents. Also I had to wait for peer review since I could not find other candidates who have completed the course with me...Recommended for beginner level

Overall, this is a good course for the intro to data science series. It exposes us to many useful data science tools. However, I think since this course covers a lot of different tools, some questions in the quizzes seem too overwhelming. I suppose students usually forget what they learned because the questions are not actually helping them understand a practical problem further.

I enjoyed doing this course , i get to know about basics of various open source data science tools and had a great learning experience by hands-on-experience simultaneously. But there are certain topics which are not very clearly described especially for someone who is new to data science field so i do suggest more elaborated content inclusion that will be really appreciated.

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