The actual course content was fine, but the link they used for accessing the software rarely if ever worked. It continuously showed essential software required for the course as "coming soon". Literally hundreds of students were reporting this problem in the discussion forums, but the only instructor feedback was "clear your cache and try again". Which was a not only a solution that had no bearing on the server side cloud environment features, but in many instances made the situation worse.

My only feedback is to PLEASE PLEASE make sure the software environment is working, and to PLEASE take student comments that they cannot access the environment seriously and propose real solutions. In the event that the environment is simply not usable, please suggest other methods of working through the material- IE, how to install Jupyter and Zeppelin on our local workstations.

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.

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.

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 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.

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.

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.

. 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

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.

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.

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.

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.

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.