Nice course with a gentle introduction to the tools used in data science. The videos need to be upgraded since the IBM tools introduced during the course have been upgraded. It took a while to figure out how to complete the last assignment because of this mismatch between the video and the IBM tool.

For a beginner, I think it does the job at introducing me to the languages of programming (for data science), open source and commercial tools available. Some improvements would be to update the instructions for the hands on labs. But overall I think it is goooooood introduction. Thank You Teachers

I learned a lot in this course and it was definitely worthwhile overall. A heads-up though: the explanation videos for using GitHub are not good. It is better to research how to do that on your own. And once again the quizzes are a bit of a waste of time and the course would be better without them.

The content is very good, a lot of ground is covered in a short space of time. However, a few technical issues prevent 5 stars. The audio quality in some of the videos is awful, as if they were recorded underwater. Also, some of the video notations are missing completely or have no punctuation.

Course is well structured and give a easier way to understand it. The reason why I don't give it a 5 star is that, the content in video is not up to date (like IBM' Cloud). Its platform has changed name to Watson Platform and UI has some differences from video. Hope it can be fixed in the future.

The course structure is good. But please fix IBM Watson Studios' issues. It always doesn't load correctly and you always have to waste hours checking forums on how to fix it. The fixes don't usually work correctly. Even if you clear cache, cookies, use incognito, it still doesn't load correctly.

it was a LOT of information

I wonder if there could be like a printout of background information, or like an index of things that are useful to know before/during the course.

also, if can, more labs to DO and USE the tools a bit more to get a good handle on it from experience

thanks for the course!

Learning a little in a lot of various areas of data science, but lack of updates in some sections are the cause for labs and assignments taking much longer than necessary. Overall, it seems to be good introductory material in the first course for the IBM Data Science Professional Certification.

Comprehensive but student-friendly introduction to common and popular open source tools for DS. Although tools are the most popular ones, the course relies too much on the IBM platforms, which kind of bind the students to the IBM products. Lectures in the first two weeks are a little bit dry.

Great course for getting knowledge of available open source tools for data science. Only thing is, it is sometimes hard to follow the videos since the IBM Watson Studio is updated since the course videos have been recorded. There can be update information after the videos for clarification.

I think this course should be forward. Now that I'm doing the 3rd one (DS Methodology), it is more useful to understand what data science means and the process that involves. It gives you the big picture while the Open Source Tools course it more specific and more technical, in my opinion.

Nice to give a headstart before furter exploring but some tools are never used throughout the course. For example apache spark. Besides if you are including the introduction here I think there is no need to put extra readings about how to obtain an IBM Watson account over and over again.

IBM Studio's layout has changed and the videos in Week 3 explain using the previous layout. This led to much confusion in the peer graded assignment. It would really help future participants of the course if the videos in Week 3 can be re-shot using the updated IBM studio site. Thanks!

i think it is better to have assignments which could give insight of using the technology. current assignments are barely touching the surface. Secondly, moving from AI experience to Watson studio has some visual options different which created an issue in finding some of the options.

I enjoyed the course and overall it satisfied my expectations. There were confusing instructions that were duplicated and sometimes not clear which disrupted how the learner should scaffold their knowledge. Otherwise, the content covered sufficed to fulfill the learning objectives.

It's a good introductory course, but the content needs to be updated esp. setting up IBM Watson Studio, though it is not so much of problem, you just need to explore a bit and you'll get it done in no time. I think that's the essence of online e-learning platforms, explore and learn!

Good material and organization. The scheduled times are useless because, generally, it takes longer to complete the tasks. Note that some organizations do not allow for the display of the software in the their computers. Hence, there is a need to use two computers simultaneously.

I think its has too much information, i would prefer a good introduction and then center on only a few tools. Also, i think it would be beter if the course leans towards learning and understanding than besides memorising. On the other hand, the labs were great, i really enjoy them.

While this course is a very good introduction and generates and overview for the tools i would like to have some more fundamental information about for example what is spark and where is used (examples of real life scenarios etc etc.)

Maybe it will be covered in the next courses.

I fully satisfied this course, but I had a problem with IBM Cloud which closed me an access to my project, because I had to stop each session and, as it turned out, it was explained at the IBM website (not at the course instructions). In this case, I had to miss a whole week.

not recommended for a fresh data science, because many thing that's really odd for a new on this focus course, but i still recommended it because it still usefull. we can learn the jupyter notebook's work and github too, and i think it's really a good point in this course.

Perhaps the course creators can instead of us havnig to screenshot each picture of the assignment, just let us share the link and leave it at that. It's a bit redundant to screenshot 13 times for a cell that takes 2 seconds to write. Half my time is just saving pictures.

There need to be more standardization with naming via IBM Watson (Data Science Experience vs Watson Studio...videos should reflect the current version). It is also unclear how after finishing the labs, whether or not the completion is then noted within the course itself.

There is not a lot of time spent discussing the why for these tools, just an introduction and some code we could copy and paste. As this is a new language for many I would like to know more about most utilized tools and an intro to what codes etc are most foundational.

Very insightful course, the only downfall was that for some of the instructions for the GitHub usage was extremely confusing. This could be due to versioning or the interface used. I referred to GitHub for a more informative instructional information to configure SSH.