1.The interface was often "frozen", don't know it's the problem of the course or coursera.

2. The clips are too long-winded, explaining every single steps of creating accounts, projects, etc., which can be better done if we just explore them by ourselves. And the webs are already changed and operate differently compared to the clips, which makes the clips sort of misleading.

In really enjoying learning .However, I thing that the lectures must be updated to help to complete successfully our projects. Something valuable I learned from this course was click , delete .search make you feel comfortable with the new tools I learned. Hopefully , in the next courses we be able to get more clarification, especially iif we are new in data science.

I feel I just got to try a bunch of tools out there but didn't have a clue how they could be used in the real world. I mean I certainly could look into those tutorials in the community but registering for a course is to save me some time browsing through random topics. I think the course can be a bit more structured and provide more meaningful practice opportunities.

This course is well organized and rich in content; yet at times, it felt like instructors were just zooming though to deliver the content and not necessarily teach. Obviously they are not professional or trained teachers, but mostly it seems, they were just reading from the script, and that makes the experience and learning more challenging. Overall, great content.

A great course to get insights about various Data Science tools at our disposal to analyze and visualize data to make effective decisions. I thank the professors to make this course interesting and worth it.

Just one thing though, some more insights about these Data Science packages would have enhanced our familarity with these packages, thus helping us immensely.

Not good. Bugs. Discrepancies between material and tools. Probably needs to be updated. I might drop this track and go to another set of courses because this course is fundamental to the other 7 in this track, and IBM tools are not widely enough used to warrant spending a lot of time learning a track that is IBM tool specific.

And my latest submission is empty???

Some good material on Jupyter and Github but way too much on IBM cloud/Watson.Also, most of the IBM materials were out of date with poor explanations requiring you to figure out how to do things in the cloud on your own, with trial and error. They also tested the IBM material through irrelevant minutiae making the quizzes harder than they needed to be.

The outcomes expected from people in this course are vague. The course presents some more advanced programs within the modules and its not tole to us whether we need to understand the code or we are just supposed to run it as is without knowing what is happening inside. I mean the content is interesting but it is more advanced to beginner level.

A good course if you are interested to go beyond merely knowing what Data Science is and try yourself on the open source tools available.

The instruction in the course, however, can be a bit confusing, particularly if you do not have computing background. That being said, with a little bit of extra effort, a newbie can still follow this course.

Unfortunately, I did not have the best experience with this course. The IBM Watson Studio course content is not up to date. I spent a lot of time trying to figure out where to find information. Thankfully, the discussion forum helped me understand that the course content was a little outdated with regards to screenshots and nomenclature.

A great course to learn about different tools and to try writing code for the first time. But week 2 was not so great: some of the video's explained step by step what to do, but not why or what happened exactly. Some of the video's were the same as the readings and sometimes the order in which the metarial was presented puzzled me a bit.

A few instructions need to work on their communication skills and videos from week 2 have don't have a linear information flow. Plus, week 2 the instructors tend to jump into the middle of things which leaves the student confused.

All the other instructors and amazing and this course is a really good intro to many of IBM Watson tools :)

Almost i got Headache ! , Tools for Data Science is complete theory about tools used in data science . I found This course is completely little bit out dated subject about IBM Watson studio. Watson was pretty good cloud platform for data scientists, But this course material not reached up to the mark for delivery a quality subject.

This one is basically IBM peddling its wares! But I guess if you don't already know how to use Jupyter notebooks, this can be a good place to start. I won't recommend paying exclusively for this course! But if you are taking this to get the IBM Professional certificate, then just go through it quickly with the videos at 2x speed.

The videos are not updated. I know the people in the texts say so but it was very difficult to me to follow one of them. Due to this, I realize how bad is Coursera's help and support and forum management since I had to find my way out to finish the course. This is a complaint on the platform, not on the course. The course is OK.

The instructions on IBM Watson studio aren't very good, given that the tool has changed. Spent over an hour trying to create a new Jupyter Notebook. During the storage selection, after clicking 'Add', you need to pick existing and not Create New. Then select the storage, go back and click 'Refresh' on the Notebook creation page.

Rather uneven. Some parts are explained step-by-step, some are vaguely glossed over. Several times, course material is mentioned, either in the labs or (worse) in the quizzes, that hasn't been seen yet. Some technical problems with Watson Studio have yet to be solved. Overall, the tools are presented without useful context.

About half of the lessons are useless, as the lecturer jumps into topics that never came up before and starts coding stuff, without any explanation. He seems to be a good data scientist, but lecturing people is not his thing. There are hundreds of complaints in the forum about these lessons, but nothing was updated so far.

The overall course was pretty good. It felt a little bloated. Final Exam instructions were unclear. The pop out instructions that said "Detailed Assignment Instructions" Did not list what had to be in it. Which meant the first time around I didn't have enough because there was no clear guidance until you read the rubric.

Good overview but doesn't dive into anything specific. It might be more useful to have a limited number of tools in the overview (like the ones most commonly used or what would be used when starting) and go into slightly more detail on their uses and applications. Nice to finally work with GitHuh and Jupyter Notebooks.

Links and screenshots are badly in need of being updated. I had to find my owns way to sign up for IBM Watson, and it still took over 24 hrs for me to get my login verified. Then when you are finally signed up, the video tutorial isn't relevant because it's not the latest version of IBM Watson Studio being discussed.

Die ganzen IBM speziellen Tools halte ich für unnötig. Es hat ein bisschen den Eindruck der Werbung für die IBM Produkte. Wer wirklich Interesse an den Produkten hat, der wird sich von selbst darüber informieren. In diesem Kurs sind die Videos uninteressant.

Der erste Teil des Kurses mit Python und R ist gut gemacht.

Awesome content, but I (and many other students) had problems with IBM's Watson license, which is an essencial part of this course and all the rest of this specialization. I had to create a second account with another e-mail and now they're asking me to provide my credit card info and use a pay-as-you-go license.

The course was good although I felt that some elements which were difficult to grasp could have been elaborated more. Also, the videos are I assume old, so the actual site which is now IBM Watson has completely changed, so it became difficult initially to get to it. But, it got great when the actual work started.

Es un buen curso de introducción para conocer herramientas y lenguajes de programación relacionados con la ciencia de datos. Considero este curso como esencial para poder identificar que herramientas están a nuestro alcance y con cuales es mas factible trabajar como principiantes, en esta fascinante disciplina.