Good Course overall focus in basic tools. the course could be shorter as someone who know which language who want to use and familir with the tools already shouldn't learn all the course materials

The course is overwhelming for a beginner with no experiecne of programming. The examples given in the class seem difficult and should have been of a lower difficulty level to keep the hopes high.

Great course, I would really encourage everyone to go through, however videos about Jupyter Notebook or other tools were so fast I wasn't able to remember all the information. Anyway great course.

The overall course is great to expect the position week 2 which in my opinion is very poorly delivered. It would great if you can re-do that portion. It feels as though nobody did quality control.

Good content, excellent delivery. Week1 had too much of information at once. I was left with little motivation after week-1. All the other weeks were good. Exercises were engaging and very useful.

There was a problem with the connection to R lab, never fixed. Also, some tutorials are outdated. These are the negative parts and why I give four stars. Other than that I like the course so far.

It's a great course for someone who interested in learning about Data Science, all the fundamentals are explained very well although some of the course material might be a bit hard to understand.

The skill share network has a problem that it does not work at all there must be a tutorial course for using it before the actual course start

but aside from that ... the course is perfect for me

Excellent introduction to a variety of open source tools available and commonly used by Data Science Professionals. Some of the video material is slightly outdated due to updates to the platform.

Good introductory course to the tools. Hands-on labs are fascinating. Sometimes I got a little lost with too much terminology, but at the end of the lessons the most important points were clear.

It's a good introductory course to know all the open-source tools available for data science. However, it will not teach you how to code for these tools apart from introducing a few basic lines.

Everything was good, except the stuff with Zeppelin notebooks. That was just confusing and it felt like there were large chunks of explanations missing for people with no programming background

This course is for understanding the tools for data science. You don't really have a time to spend on those tools yet, but it is a great course for understanding what to learn for your future.

It was a useful course to understand the different tools. I think we can add more practice instead of introducing new tools. I personally prefer to do actual practice than memorize new terms.

Great beginning course to get more comfortable with the open source tools. Some of the materials are a little out of date though, so you have to just click around to find the intended buttons.

It was a lot of content with some very brief explanations. I guess some topics will be detailed later, but It was a little bit overwhelming specially about topics involving Machine Learning.

I would focus more on some practical use cases. Too many tools are shown for us to getting familiarized with all of them (I won't even remember half of them by the end of the specialization)

This course introduced me to some great virtual environments to train my skills in data science. I'm now more familiar with these tools and ready to use them in more advanced applications.

The content was interesting. But, several times it was not clear if one was supposed to go along the instruction in the video or if self-action would be required later. That was confusing.

Remove the IBM Watson Studio (Optional). It is useless. Also, I can not create free account on given URL. Overall it was good until learning basics of R-Studio and using markdown language.

This is such a great course to introduce you to the tools used for Data Science; I learned a lot about git/github; what I did not like about the course is the way some tools are presented.

Overall good introductory course into the main tools data scientist use. Some videos should be more explanatory and not assume the viewer has programming experience or is a Data Scientist.

This is a fast paced course. Where all open source tools required for data science are introduced.

The learner has to take up self initiative to gain deeper knowledge regarding this tools.

overall, the course is great, but since the IBM environment changed, videos do not correspond to exercises. It would be good to add reading materials clarifying differences in more detail.

I think it could have done a better job covering toolsets not from IBM also, but I have a reasonable introduction to IBM Watson Studio's toolkit along with Jupyter Notebooks and RStudio.