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The instructions on this course are unclear at times. It is very odd to me that there isn't an explanation that the first video in the lab sections is just to be watched and is not the actual lab. This is my first coursera class and I was following along with the video at first. It was also unclear when I was supposed to do what was shown in the video and when I was supposed to also complete the tasks on my own.

Week 4 was a complete nightmare of mis-matched versions between the course materials and every tools and website. Considering these were all IBM tools and this is an IBM course this seems particularly problematic. It did not endear me to wanting to choose IBM products from the many similar options. I spent, without exaggerations, 4x as much time trying to navigate where the tools were now located within the tabs and figuring out what was required because things were missing in the instructions. The peer reviewed project page was particularly terrible. It needs to be QC'd asap because there is just plain missing information.

I could not get a photo to embed within the Jupyter notebook no matter which method I tried. I do not know if this is because I did something wrong or if there is something I need to do specifically within Watson Studio, like add the photo as an asset, to make the photo display instead of just posting a link. In the assignments I reviewed they also only had links so I suspect it isn't possible to display an image. I lost at least 30 min to troubleshooting this. I know I could have just done another of the listed tasks, which I did, but I wanted to learn how to display a photo, and I couldn't. If it isn't possible to do something with the tools provided then don't list it as a task to perform.

For a newbie course this had a few assignments that had incorrect or incomplete instructions. In week three the assignment was to add a type component to the SPSS Modeler, the instructions in step 11 referred me to the box and mentioned the arrow, not the ellipsis on the right side of the box. I spent a great deal of time reviewing the instructions (like an hour or two) until I accidently clicked on it and found the options. In week four I again found myself spending a great deal of time on the final assignment, not because I did anything wrong, but because when I posted the link to submit the assignment, no one else could see my assignment, eventually I discovered the submission link does not clear out when I put a new one in it, so while I would post the link in the browser and could view my notebook, nobody else could see my work because the link was corrupted with multiple links. I spent a few days trying to figure that one out. Hopefully this feedback is helpful, I am learning a lot about Data Science tools.

It gives a comprehensive overview of tools for data science, with practical hands-on exercises to ensure you can apply what you have learnt from the course. It saves you a lot of efforts to search and gather so much information. I really appreciate the hands-on exercises that helps apply the head knowledge thus retain it.

However, some info is unnecessarily detailed and not very helpful for the learner, and some questions checking about such details is not a good design. If the learner doesn't need to use such commercial tools, they don't need to know that much about them, and they can't remember such details for long either, without actual usage of them.

Another drawback is that the contents are somewhat biased towards promoting IBM products, and then I was sometimes wondering how biased the other contents might be, when bombarded with a lot of commercial logos of the tools they introduced.

The only problem with this course is the "peer-review" section. It's mostly because students have no other moral obligation to properly follow the rubric when grading. I think it would be wise to make a mandatory "comment section" when grading other than the maximum number of points. This is because while something might be correct, the grader might not consider so and should explain why the rubric is not being followed. For example, something as simple as "in this part, you missed this" "should've included this" or something similar. That way we can ensure that the students KNOW what to modify next. TL;DR Good course, but improve the "peer-review" system so it's properly graded. The course is quite complete and helps you out on the basics of installing programs which in my opinion is not intuitive at all and it does help with a lot of practice.

The first week of the course was a fire hose of information regarding all the open source and commercial tools avaliable to data scientists. The second week was very helpful in helping me learn more about how to use Juypter notebooks, git, and git hub. However, I do have to say that the education on R/R Studio was rather skimpy. I learned more about R/R Studio from watching a 2 hour tutorial on YouTube than I did from this course. The week 3 material got me excited to learn more about how to use IBM Watson and other tools associated with it to perform analysis. The capstone project was perfect for this course. I think I learned the most from the hands on project. This is a good course but it could be made better in regards to its approach to R/R Studio and not overwhelming the student with all the tools avaliable in the first week of the course.

I like the approach of an overview of all the tools. However, I noticed that because some of the online platforms such as Watson Studio updates regularly, the placement of different pages and buttons are different from what's in the video. So that requires some exploration, which I think is good as it's usually part of the job when a data scientist is at work. That can, however, create some more confusion and time pressure for some students I imagine. I also found the Watson Studio docs page where there are additional learning that can be done on one's own by just studying and following the sample notebooks. I think that should be emphasized so that students who are motivated can go learn themselves. Same with the Watson youtube videos.

In general it is a good course for beginners, especially for the presentation of the workflow in automatic learning, deep learning and AI. I also have the same opinions about the use of Python, r, rstudio, Jupyter Notebooks and GitHub. There are aspects that should be improved, for example the sequence between the contents should be more linear, there are sections that are announced but not developed, like the use of SSH keys. In this case it was necessary for me to visit other places to have better information.As for the exams, these should be better aligned with the course content. By improving these aspects the course would be more productive

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This was a good course overall. The videos were helpful in showing how to navigate different notebooks and had links to other sites with further info if we needed. My only negative is that they haven't properly updated the course to match IBM Watson Studio and still have the video showing the old version. I had to spend hours on my own searching through the site to figure out how to create a project in this new format. I appreciated the promo which allowed me to access more of Watson Studio, but maybe offer more explanation on how to create projects within different versions. Otherwise, a really good course on the basics.

I would say this is sufficient for beginners who are new to Data Science. It's definitely worthwhile to learn from experts and I love the fact that students are given the chance to redo assignments if they do not pass the required grade. The labs such as using the Jupyter Notebook, R Studio and uploading notebooks to GitHub are also really helpful. I would have given five stars if all the tutorial videos of using the tools are the same as the current version of all the toolkits but it's really a great way to kickstart on gaining insights on Data Science. Kudos to everyone who finished the course!

The topic explanation is great, quizzes are good and the pace is perfect. The only issue is the IBM Watson Studio. Issues all over the place (timeout, cloud down for maintenance, issues with logging in, issues using the Watson service) and no response on forums to the more serious queries. I reported an issue, updated the query and no response for now 9 days and going. Had to pay for an extra month due to this and reset deadlines twice. Reported issue again today on the course page, to Coursera and to IBM. I could have done the audit version, got the same amount and saved money.

Excellent coverage of all major and semi-major data science tools, including where they fit into the ecosystem of data science, and hands-on practice with some major ones. Modules 1 and 2 gave equal time to all the most common vendors and organizations. Module 3 was much more specific to IBM tools, which is understandable because it is an IBM course. The mandatory modules were extremely high-quality and professionally built. The optional modules seemed more like casual, unscripted discussions, with pronunciations I found difficult to follow. Overall, a worthwhile course.

A good course that introduces many aspects of data science by discussing open source tools that are consistently used in the field of data science. I like the IBM Watson Studio system that allows you to remotely run projects using various hardware and software environments. A lot of the tools discussed are IBM proprietary tools but they do discuss the theory and reason for using such tools. This is valuable to understand data science at a beginners level. All in all a good introductory course that helps fill in gaps of knowledge.

Good Intel on the most common tools to get an understanding of the general idea on working within the data science field. One star away due the obvious reasons on pushing the IBM products on an IBM course while there would be easier to acces tools available online and even Coursera has their own Jupyter labs installement like in the Applied DS with Python cert. Unless recruited by IBM I don't see myself working much with the IBM cloud and Watson studio. All in all good course and was fast to push thru.

I would have given a full 5 star but for the for the fact that the quality of the audio for some of the modules is poor and the instructions for the labs are outdated (Watson Studio on IBM Cloud has changed and you need to update the course with new screenshots so that the learners can have a better user experience). The course content is just about right and it meets the objective of an Introductory course. Thank you for giving me this opportunity to provide this feedback.

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 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.