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.

It really feels like they are trying to sell me their product. This course is kind of an introduction to Juypter notebooks but I don't feel someone who has no experience would complete the final assignment easily. There also seemed to be a lot of information taught that seems too specific and not necessarily relevant for a new data scientist, felt like there was a lot of jargon that was not explained well and grouped up with other jargon.

Some of the lab instructions are out of synch with the screenshots. Also some of the external sites references look and work differently then the screenshots or instructions. The section about the IBM tools is a huge dump of information that's somewhat confusing and doesn't appear very practical/usable to someone new to the field. The Git command line part looks very amateurish and is very hard to follow up by someone new to the subject.

A bit of an overload on certain information. I could see how some people, especially those who aren't as familiar or comfortable with programming jargon, would get overwhelmed. Accessing Github and using it's commands was one of the major concerns of most students when I read the discussion boards. Perhaps some editing or reorganization in that topic is required. Other than that, I'm grateful that this course introduced me to JupyterLab.

- module 7 is useless, you should tell people to NOT provide their credit card number at the beginning of the course, not somewhere in the middle. To get and use the free account happened to be very difficult, I simply abandoned this optional module, it's a waste of time. - what 's the added value of listing tons of tools we don't use? The most interesting part of the course is the Jupyter assignment, because it is something concrete.

good technical guide but lack the context. For example, there is no explanation on why i need to convert to RDD, why would i want to move the paragraph around. There is no practical references that aid the understanding of the technical steps. This needs to improve especially for someone who has very little programming background who probably only heard of SQL, Python and R at this point in time. Scala, Jupyer, Zeppelin are all new.

i did learn how to do specific things but i found that there was not enough context. i.e. when would i use Jupyter? When would i use R? SOme concrete examples and exercises would have been helpful.

What was particularly unhelpful was the fact that the videos were out of sync with the tool so it took me ages to figure out what was wrong. The videos need to be updated!! i saw that i was not the only one who found this difficult.

I think it would be better if the course focused on one online platform such as Skills New Labs rather than learning about multiple notebooks with multiple programming language with multiple work benches. It is to some extent confusing for someone with no prior experience in working with python, scala, or R. Nonetheless, this course has allowed me to understand more about available options which could be beneficial for experts.

Course provide brief overview of available tools used for Data Science. For awareness good, for getting working skills on any of them, no. At the end I get confusing feeling what to use in which situations, as if they all do the same thing. Possibly I would recommended to provide awareness bout all, but give in-depth practice, additional week, for one of the tools. It could be IBM's or Apache Zeppelin as more universal.