Overall well structured course but certain advanced ML features of Watson studio were demoed in the course without explaining the basics of Neural networks or decision trees

Great overview of the myriad tools used in data analytics and data science. But it also seemed like an infomercial for IBM products and services, which is a little sketchy

A lot of the questions during the 'final check' are based on the content of the video and not on the general knowledge has about the topics that are described in the video.

It did seem like some of the videos needed to be updated. There was some struggle to follow along with, but with a little exploring I was able to find everything I needed.

This course was straight forward. The correction to the course, would be based on its latency. It really needs an update to the content connections to IBM watson studio.

good introduction to the tools that a data scientist needs . But a bit promotional too. Great if you want to get good knowledge about the tools to be used in datascience

I think it was a good introduction, although some content seem slightly out of place. It might be better to introduce the IBM tools at a later part of this certification.

In general, the content is relevant, but some videos have audio problems and some topics (Git for example), are not well explained for someone who is new to the subject.

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delivery, it's a shame one section of this course seemed outdated in reference to the materials it was pointing to (when I took it).

which made catching

This awesome course helps me to discover many data science tools. Its content fits for all attendants, including fans of open source tools, to employees of enterprises

Course Is Fantastic for beginners .But those who are expert in any one tool like jupyter notebook or any other its little confusing but course try to deliver its best

This is a very good introductory level course to know about various open source tools for data science. It gives idea about R, Python, Jupyter Notebook, Zeppelin etc

This part of the course is interesting and I did learn how to use some new tools, but for most of it, it was almost a sales pitch. Saying that, I do love IBM Watson.

Overall is good, but in the last module which require student to participate in IBM watson studio have a lot of problem such as notebook creation on watson platform

I particularly liked the chance to practice with the online tools. The only caveat was that I missed some of the unstructured "work with your own data" exploration.

Course content needs to be updated in case of IBM Watson, there's so much buzz about it. Video tutorials are absolutely inconsistent with the actual UI of the tool.

Week three videos need to be updated, Enjoying the content, but the use of the old videos made the last lesson or two harder to follow than was strictly necessary.

An interesting introduction. Some materials are slightly out of date, so know you might have to dig around in the IBM webGUI, but very well laid out none-the-less

It gives an awesom idea of what kind of tools can a Junior Data Scientist use, in order to learn about it. Nevertheless, some of the chapters are a few repetitive.

Most of the modules were very great. I would have loved to see those question prompts but there were none. It wasn't easy keeping a steady attention to all videos

It would be helpful to have some extra videos explaining to explain more in detail functions in each of the programs. Apart from that, the content is quite good!

Great intro course for the open source tools. However, would be better if there's an optional readings/tutorials for installing some of the tools directly on PC.

Some of the options in the videos are no longer available as explained in the DSX Platform, but after looking for them a little I was able to complete the tasks.

I didn't like because it was based upon previous version of the website. I think it would have been better if the course was up-to-date with the website version.

In my peer graded assignment of this course, total marks assigned is a 25 on 25 by the peer reviewer, but Coursera calculates it as 23 on 25. is there a reason?