+It was good to get an overview of all the tools and and technologies involved with the industry at the beginning. There are just so many out there that it's nice to have some context for what everything is so I don't drown in industry specific lingo while I'm learning.

The structuring of the course was a little confusing. In particular the Week 1 Module broke everything down by open-source, commercial, and cloud tools, but then each of those sections used the same categories of tools(data management tools, visualization, etc). I think it would have been more effective to break it down by category, and the note which tools were open-source, commercial, etc. As it stands, it felt jumpy and hard to keep track of.

Content is ok although course spends way too much time in listing available tools and showing how tools developed. Instead, course should focus solely in showing the characteristics of the tools . Practice excercises are great though... maybe a bit too simple. It would be great to add more excercises and reduce the history and context.

Also the exams ate terrible!!! you should consult an education master to improve this, many questions were of thing that were barely mentioned in the training or that are purposely written in a confusing way. In any case exams dont realy measure how much you understood, but how good you are to take notes and retrieve the right data from the your notes

The course is ok but materials need to be updated to reflect latest changes to IBM tools, so that students don't waste time trying to find things while setting up and accessing tools. Also, could help to include more info with realistic examples or a few interview blips with real data scientists on when they use one tool over another- real context. LIke, why use Jupyter Notebooks over Zeppelin or IBM environment - the course addresses these at high level but I think perspectives from real people in the field what they're really using, and how it solves their day to day issues and workflows is a needed addition to the course to make it more immediately useful.

Two very important things:

1. Several links are not working. Several examples are currently unavailable for students. Pages don't load, or an error message is shown.

2. The final exam asks for students to do things that are not included in the course. I understand that students may need to do some research on their own in order to complete the final exam. This is great. But when showing how to do markdown cells for example, you can add a link to an IBM tutorial on Jupyter Notebooks (I found one on the internet) saying: "The final exam may include extra content not directly given during this course. Follow this link for a complete tutoria." That would be great.

There's a huge area of opportunity here to improve the content of the lessons, or most likely the way it is presented to the students. If you visit forums of week 1 and 2, you'll find a lot of discontent from the students. Even though the lessons content is good, the way it's introduced to the students is not the best. I'm only giving three stars to create awareness of this particular issue. In my personal opinion the course was ok, I understand it's just a glance of the several tools one can use for data science but if you're curious enough you can take enough advantage of the course's content. Great tools presented and a lot of self learning left to do.

There is plenty of information provided in the videos and as an absolute new person to data science I find the background information and tutorials helpful. The things I dislike are:

1 the quizzes are just finding facts / statements word for word from the videos.

2 there is little to no help on the discussion board and there is no one to assist you on the ibm watson prompts

3 the final quiz was graded our coding ability yet we never learned how to code in this class

4 most dislike the fact that our final assignment is peer reviewed by people are also in the course and not professionals. I dislike how my grade can be impacted by the mood of someone.

The flow is erratic, lot of random information not sure where to put it .. need to explain the process of what happens with data and why the apps are needed and what they do and how they help the process (work together) .. I heard about dozens of apps but have no clue when and if I would use them or why they are needed. I am following the course but mostly having to research what you are saying .. the language is high level or sometimes overly complicated. I only bring this up because this course said no prior experience needed there is not way a person having no IT experience will come away from these first 2 modules with an understanding

The Course was good but it could have been much better if it was based on Video tutorials. It was really difficult to understand the written instrustions for me initially as in to how to perform some of the codes which we had to execute just by reading the insturctions given in the course readings. I had to refer some of the videos from youtube to understant some of the instructions, so had been the course explained through video tutorials it would have been much better to understand the module. The module was good it was just that the study material was a bit tricky and time consuming to understand due to the absence of video explations.

I cannot give 5 stars because of the not updated materials. The IBM Watson Studio interface was completely different from the one explained in the videos and many of us had difficulties with it. Please, understand that for beginners like us, it was confusing and frustrating.

But, Technical Staff are very active in the forum and try to answer all our questions. Thank you for your help.

I have still a few concepts a bit unclear and I couldn't find the answers on the internet. My doubts are around the concepts: kernel, language and interpreter. Are they all synonims? What are the differences between them?

Hope my feedback is useful.

The course design is such that it covers a lot of breadth but very little depth. A plethora of tools are introduced but not much hands on time spent in the form of exercises (within the given four weeks). This was causing a lot of confusion, which was getting worse because of the many instructors with very short videos..

It would have been more effective if one person was to take the student thru all the topics and in longer video clips that these short 2 minutes to 10 minute.

Subject is interesting, tools are interesting, could have made the delivery process more interesting..

The labs were really helpful towards teaching you the skills. I feel that the videos could have been organized better, and that the unfamiliar terms could have been defined. I would recommend mid-video or end-of-video quizzes that test the content in the videos because I was unprepared for many of the questions in the practice quizzes even as an avid notetaker (similar to what is done in course 1 of the Introduction to Data Science Specialization). The end-of-course project is comprehensive and tests a lot of what is taught throughout the course.

Overall the course was informative. The issue is with the legitimacy of how the final project is peer graded. My first submission showed that it had failed. When I went through the comments/ criteria that someone graded me on I could tell that they just randomely clicked points. I re-submitted the same exact assignment and got a 100%. This shows me that there is no controls in place to catch people who do not take this seriously and just click boxes to grade someone quickly without actually looking at what that perosn wrote. That is disturbing.

The course introduced some interesting things especially Zeppelin notebooks which I wish I had encountered earlier. There were two main negatives to the course: 1) The videos and readings for IBM Watson Studio need some serious updating - once the account was created it was very trial and error to create a notebook. 2) Some terminology may be foreign to a beginner level student. As an example, the term "Spark" would be confusing to someone new to data science as there was little to no introduction to what it meant.

It felt like it was too soon to be going over so many programs and tools, when we haven't even learned how to do any part of the data science process yet in the certificate. It almost felt like a giant marketing ad for IBM. The labs were somewhat interesting. The week 4 instructions for how to do the final project were infuriating. It was so unclear what needed to be done, and you split it up into 4 sections when they all were essentially saying the same thing, yet also contradicting each other. Very frustrating.

Definitely not one of my best experiences in Coursera. Most of the transcripts need serious rework. The IBM badge is promised, but not granted; I will have to contact support. Some of the lecturers are hard to enjoy due to strong accent. There are several inconsistencies in the labs. 3 ungraded external tool sections are labelled with "This is new content that will be released in coming weeks and is not part of the current curriculum". In video "Data Refinery" the audio is partially very bad.

As a complete beginner, I felt at times, that the lecturer was speaking to a more experienced audience. They often referenced to the various languages, however this is not covered yet and would only sound familiar to people with previous coding experience.

I had a hard time finding the access to the tools I needed, in particular to create a project in Watson Studio.

Some of the videos are of older versions of the tools, therefore it took a bit of time to navigate the new layouts of the tools.

This course is definitely packed with tons of information, and I learned a lot, but it really felt like it was like trying to learn to drive by reading the car's owners manual. They introduce the expansive languages and tools available to data scientists, but it lacks any practical context or too much hands on experience actually using the massive amount of functionalities that are presented. With that said, it is more of a pure rote memory type course rather than a practical, hands-on one.

The course wasn't that bad. The only problem was that it was quite dry. Even with the practical labs, I didn't really have THAT much fun. As for what I learned, the course is basically there to give you an idea of the different Data Science tools that are used throughout the world today, and I like that just because this course is offered by IBM, they still didn't only tell us about tools created by them, but also open source tools and even commercial tools made by other companies.

Very light overview of the tools. Does not go beyond merely showing what the tools are, very little in-depth discussion of what the tools actually do, or what the differences are between them. The entire Waston website and such has changed since this course was first prepared, and it's a bit confusing to work around. As very light intro to the topic and tools, the course was ok, but probably not worth it for someone looking to really learn the tools and be challenged.

A lot of talking. The second lesson (about open-source tools) was very informative. I appreciate the small history lessons, and the lists of terms used in Data Science. It gave me a lot of vocabulary terms to look out for. The third lesson was insufferable. I understand IBM wants to sell their product... but I didn't sign up to the course to use Watson. I want to use Python. Why they couldn't make it as basic as their "lesson" (i.e., vocab and history) is beyond me.

This course gave a good background on the types of software and tools available for data science. My issue with it is a lack of organization and clear outcomes being stated. The majority of learning has to come from the videos with respect to content and the grades rely heavily on if you remember facts about the many different pieces. The strength of the course is in the practical applications with the labs which were very basic and next to no coding was required.

Since I am new to data science, I enjoyed learning about the many tools available but this course takes a very long-winded approach to do so. It feels like an endless descriptive list of tools with few practical exercises actually using them and seeing them in practise. I also dislike the quizzes; they tend to test your memorisation of earlier content rather your understanding of the topic - this is not a useful way to learn and could be dramatically improved.

. For the final assignment, Instructions in videos didn't match what seen on screen and had to watch youtube videos and websites in order to complete my assignment.

. some of the graded quizzes used multiple-choice questions which you had to pick from a combination of answers to get it right. This is effectively a to add more questions into one. If the quiz says 5 questions, it shouldn't be extended by adding sub-questions shown as "choices".

Thanks

Nice overview, though the course materials have not been updated for more detailed navigation within IBM Cloud Pak for Data, in addition, in would be much more convenient if in all of the videos there there would be possibility to save shorter specific notes at particular timestamps, there problems with the IBM Cloud Pak Lite subscription which has limited usage and one has to wait until the counter is updated in the beginning of the next month.

Gives a good high-level overview of tools used in data science and introduces some on-the-ground very beginning experience with GitHub and Jupyter notebooks. My main complaint is that often the quiz/test questions are not written well, cover content that is not mentioned in the videos, or are not necessarily measuring whether you are understanding key concepts but rather whether you can memorize what different IBM Watson Studio tools are called.