A one stop destination to gain know-how of the tools at your disposal if you aspire to be a Data Scientist. The course perfectly summarises all the tools used by Data Scientists today along with their pros and cons. It even goes into some detail for JupterLab, R Studio IDE and IBM Watson. A few videos can be made better though, sound quality makes it a bit tougher to understand what the speaker is saying. A good course overall with lots of videos and labs. Liked it!

The final assignment peers review had questions that the possible answers was not that precise, they where a little bit 8/80, for example, the first question asked "if a Jupyter Notebook photo did include every questions in it", but the options where something like "Yes, it does include everything that the exercise asks" or "There's no photo of the notebook". It didn't include a mid-term like "There's a photo, but the others requisites are not accomplished".

The Course offers some pretty valuable information for begginners. However, it is a little overwhelming at the beginning because it throws a lot of technical program names at you since the beginning. Although all these programs are useful as toold for data science, the vas majority are not even discussed after that initial mention. would be very helpful if these are reduced/removed so it makes the actual programs one is focusing easier to rememeber.

The open source part was great; wish it had more! It feels a little vendor lockin-ny but that was to be expected. The exercises are great. I think adding a few more screenshots, a bit of proofreading, and ensuring the OS being used by the instructor is mentioned in the Git section will make it a little less challenging for people not familiar with either*NIX platforms. That being said, it was a GREAT introduction to the tools used for data science.

The material was very good. However, the video showed DSX which is quite different in look-and-feel from Watson Studio. It took me one week of intermittent trials to navigate the issues with creating a project, but then only an hour to finish the course project. Suggestions: tell the user to delete resources if they have a lite account; search for Watson Studio and go to that, not to the main menu. Overall, an informative course. Thank you Polong.

I could know many skills and features of two different Data Scientist platforms: Skills Network Labs (learning platform) and IBM Watson Studio (Enterprise platform known as Data Science Experience).

I could also create and play with Jupyter, Zepelling and R Notebooks. My only recommendation is keeping all videos and course material updated (current platforms versions) in another case it is quite difficult to follow properly. Thank you very much!

I'm very satisfied with the substantive material covered in the course. I'm not giving it five stars because the presentation in week 3, specifically the instructions for setting up the notebooks in IBM Cloud were confusing. I understand that you are transitioning the branding but it was confusing. Students were on their own to sort out how to make it work. Overall, though, I'm happy with the sequence and will continue on to the third course.

This course provides a good introduction to tools that can be used for data science (e.g. Jupyter notebooks, IBM Watson Studio). Packed with information and good introductory lab exercises to help students familiarise with the tools. On the other hand, I hope the parts mentioning IBM Data Science Experience are updated to mention IBM Watson Studio, because it did get a bit confusing in certain parts of the course. Aside from that, great course!

3 and a half. Tedious, kind of confusing. 1 teacher more refined and professional and organized in the lesson plan than the other and you can tell which is which. A lot of information, not always a lot of explanation. A lot of things that can go wrong and unless you wanna wait days for responses, pretty good chance you're gonna have to figure it out yourself.

I learned a lot but it was harder than it needed to be.

Appropriate and concise overview on the discussed tool environments given in this course. It may be useful not to enforce unnecessary external account subscriptions in order to allow for exercises or earning grade points, e.g. either Skills Network Labs or Watson Studio. Working with the latter was a bit of a challenge due to different appearance of the current platforms as compared with the course resources.

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.