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.

The course is too hectic. As I am coming from Mechanical Engineeering background, the words used in this course related to data science(and related software) went above my head. There should be some videos regarding the basics of the terminology related to IT WORLD( with practical example) in this course. Watching that Juypter notebook and other tools were so challenging as they were difficult to understand for me.

I was really interested in the open source tools, but I feel like this would have been more easily retainable by taking a Python course first. Also the last week was all about marketing IBM Watson Studio as a superior DS tool but it's UX was super difficult to navigate. The video tutorials were outdated so I had to Google and eventually kept clicking until I found something as simple as opening up a new notebook.

Sometimes, the link in lab activity not match the illustration pictures. Furthermore, IBM Watson is not friendly to use. When that happen, I almost always lost and can not follow the instruction. I feel frustrate when this happen, and make the course more stressful.

Apart from the examination usually ask about minor detail rather than main idea, the feedback is bad too. It is not explain why the answer is wrong.

It feels superficial, and I felt lost trying to do the assignments, as if I didn't have enough information to use the notebooks. I did a lot of outside Googling. If the purpose of the course was just to give a glimpse of what Data Scientists use, it did what it intended. But the experience was difficult, because I constantly felt I was expected to be able to do things that I hadn't been given the tools to do.

On overall IBM data science professional certificate track: Pros: Content is just good enough, instructors are good. Cons: IBM watson and the platform given to practise on is awful and has terrible performance and reliability issues, most often doesnt work and had an impact on my test deliverables. I personally overcame those issues to some extent with kaggle's and google colab jupyter notebook environments.

IBM Watson was updated and changed the design, it became harder to understand how create a notebook and etc.

IBM Watson is lagging, the code (Python 3.5) runs through time.

The final assignment is described incompetently, as there are bindings to the cells. In the beginning it is said that their number is varied, and then they give a binding of the context to them, because of which the evaluation is wrong

Course gives a broad overview of tools that are available for Data Science functions. However, I think it would be better to introduce more of this along the way particularly in the following Professional Certificate courses that focus on specific parts of Data Science. Its hard to connect the dots on what Tools are available when you don't really have the foundation yet on what you would use them for.

I learned a lot in this course but much of it was a result of the helpful comments of my fellow students. Sadly much of the material, especially the videos on IBM Watson, was out of date and useless. I was happy to be able to google terms and read the helpful comments from other students and find my way through the course, but this course is inadequate on its own and in desperate need of an update!

I wouldn't say it's good introduction to open source tools for data science. It's rather IBM open tools for data science. They highly recommend you to use this cloud based IBM tools but then you will face with a lot of problems with that - Skill Network Labs notebooks is impossible to use because it will kill kernel after minute or two of idleness; it will maintenance work in critical moments, etc.

The course has a lot of good material if you are learning about Data Science with no industry background. The hurdle to a better rating though is the outdated videos. They make the learning experience unnecessarily confusing when you are trying to apply the lessons in real world systems that have changed so drastically. With a few video updates, this would be a 5 star course for a beginner.

Too much information in a commercial format. I mean i get it that the course is offered by IBM, but a whole section presenting the different tools was maybe too much. The tutorials were not very informative and their pace was too fast. Also some vocabulary was casually used all along while never introduced at any point in the course so far. Really had trouble getting to the end of the course.

The videos are outdated and do not reflect the platform currently on IBM's website. I think that it would be easy to solve, but it seens that isn't the interest from IBM. I couldn't use the IBM Networks Labs because it had been offline for many days. The support is nonexitent. Nobody sees what's going on in the forum. Despite this, the course appears to be mor informative and a basic level.

Too much information that you should remember while practicing and writing some code but authors think that you must learn it by heart and answer where each button is located. I think this course (like the previous one) could be twice shorter. But for the sake of justice I will say that practice parts with RStudio and Jupyter can be interesting for people whithout any technical background.

This felt very salesy, like I accidentally signed up for an IBM sales seminar or something and couldn't get out of it. Don't feel like I learned the tools very in depth either. It was more of "look at all these IBM products that exist! They're great!" It was just really in your face, and I think it could be more up front what this course really is, advertisement space paid for by IBM.

Overall good but there were some dead links and ibm watson looks way different now than it does in the

lessons.It was hard to follow along because of the difference.I do understand that a data scientist is a problem solver so I took an extra 2 days to figure it out and in doing so developed some confidence in my ability.

For the price I would give it a 5 but this is an honest review.

In the end of the course (the IBM DSX section) there is a difference between what is presentated in video restructions and the real IBM cloud, it's called IBM watson studio, it went well for be but took a time (an hour) untile I've succeded to creat Jupyter notebook with the new updated clould, so I recommend you to keep updating the instruction videos according to the new updates.

Warning for those whose native language is NOT English:

These IBM Data Science courses are in DESPERATE need of review by a native English speaker. If English wasn't my first language, I can only imagine how much I would have struggled. It is pretty unbelievable that they expect people to pay money for courses that have so many many grammar, syntax, and audio transcription errors.

In this course one can learn very useful tools, each of this with different targets, with good examples and material. But some material (mainly videos) are deprecated in some degree, turn learning in an uncomfortable experience, because lead to make some errors and many time must be invest in relate information of video with actual web tools.

I hope this problem will be solved soon.

The course provided an overview and very shallow hands on tutorials. appropriate for beginners. Course materials were out of date and IBM's own platform was not working as intended for the course causing frustration with this student and others as seen on the discussion board. Even so, I learned quite a bit on how to get started with a good sampling of tools.

Thank you.

Lots of material and tons to remember. It's unclear what is important and what is just "good to be aware of" until you get to the quizzes and need to remember much more in detail. The videos are way too fast for beginners inmy opinion but the exercises are well explained and easy enough to follow along. I would not recommend this for someone with no experience in this field.

This was a solid course. I learned what the tools were for data science. Some of the vocabulary was difficult though, as the instructor's seemed to expect that terms such as 'RDD' were common knowledge to the audience and did not stop to explain them. For me, it was difficult as I felt like I had to learn an entirely new language and then learn about the tools on top of that.

Please fix the Watson Studio and IBM Cloud access. Once the 30 free trial is over, it is extremely difficult and frustrating to get access again to complete the final assignment for the course. The final exam is much too difficult for a beginner course of this caliber. The information being tested was too detailed for a course teaching a tools overview for data science.

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.