I found several concept very unclear in this course, for e.g. asking us to see a sample notebook,article from IBM community and follow it from start to end. As a beginner to coding and data science this wasn't very helpful as I couldn't understand the concepts. A beginner level tutorial or code present for that would have been very helpful.

The final project needs to be more descriptive as to what is needed so people know what to expect for peer-reviews. Example: Do we run the markup code or show it pre-running so people can tell the header level? I thought the section on GitHub was pretty helpful. It might be useful to tell people how to capture the URL for files on there.

honestly, it was well taught. but the key problem was that the videos by mr. romeo kienzler were a bit advanced for my understanding as i had no prior experience of this and i tried to replicate the the things he was doing and couldnt understand at all what im supposed to do ahead of somewhere. mostly this problem was in Github and Rstudio

This a great introduction to the three major open source use for Data Science. Don't expect too much. The individual tools discussed here is more of the high level usage and background of it and has some basic coding exercises. If you are looking for a more detailed course for one of the specific tool, you should find and invest for that.

Only issue we ran into is that there have been some URL and naming updates that aren't reflected in the training yet. Such as DSX is now call Watson Studio. The data science work bench also does not include Rstudio at the time of me writing this. I believe they are in the process of an update that is why the link to RStudio did not work.

The course is structured well to get introduced to various open source tools. A minor issue is that markdown cell commands(such as Tables) for Jupyter notebooks as explained in introductory lessons does not generate results as expected. This leads to confusion, as a learner I am not sure if have followed steps correctly or its errata.

Good overview. However, the lab instructions have not kept up with the evolution of the applications used. For example, Watson Studio has changed its UI making it hard to follow the lab instructions that are based on a previous version of Watson Studio. Also, I had trouble linking out to Jupyter Notebooks from the Coursera page.

This is course is for the beginners to the data science and have no idea of online open source notebooks like jupyter notebooks etc

It's a good course and introduces all the data science open source tools u would need to build a real time model.

Don't miss any part ,go through every part.

It was fun learning !

happy learning :)

Had difficulty with creating account on IBM Watson studio and getting the lab to work correctly. Instructors were responding to newly created questions and not to responses. I had to figure it out myself to finish the course and I am sure lot of other students are struggling as well. Please improve to make this course get 5 *.

Good course but some portions are in dire need of being updated to match the current systems. Other than that, I do feel like I got some exposure to open source tools for data science. I wish there was a mini assignment like the last one for each major tool introduced to help gain familiarity though. Overall, would recommend.

The course was really great although some parts are outdated and it's somehow confusing to learn from a video that doesn't match the tool. I also had trouble working with one of the activities because the material isn't working. Nevertheless, I still enjoyed the course and learned a lot from it so overall I'm still satisfied.

The course is good to learn free tools for using Python, R, Scala. There is one confusing thing in this course: it describes an earlier version of IBM Watson Studio and the changes between versions are significant. So you'll spend some extra time simply to register yourself and get to your tool.

The rest is fine!:)

It was lots of new information that I think it was too much for someone who is a Data Science dummy. Also, the examples doesn't describe what is the point of the evaluation, only said put this code, put that code and see the results. I like to know a little bit about the set of the data and what is the target.

I found the course to be very difficult and had to copy all the video transcripts. I decided it just wasn't for me. The final exam was the last straw for sure and very difficult. I think I had to take it 6 times and that last try today I finally passed it with a 91% missing just one question. It was torture!

Me gusta su introducción, lo que no me gusta es que no haya subtitulos y que sea innecesariamente largo, las herramientas para data sciences está bien saberlas y todo, pero no me sirve tener una tecnicidad en ellas por teoria, falta que el curso sea mas practico, que te sumerja, así uno aprende mejor y mas

Thanks for this class, I learn a lot about "Skills Network Labs" and " IBM Data Science Experience". However, the version of tools and website page used in this video should be updated cause there are many differences from the real website and the website showed in video. This might make people confused.

Too many tools introduced in this class, but for each too little information. And a lot of IBM tools are introduced in this course, I encountered several technical issues while using them. Instead of using IBM cloud platform, what not let us download some useful tools in our own computer and practise?

Nice course with a gentle introduction to the tools used in data science. The videos need to be upgraded since the IBM tools introduced during the course have been upgraded. It took a while to figure out how to complete the last assignment because of this mismatch between the video and the IBM tool.

For a beginner, I think it does the job at introducing me to the languages of programming (for data science), open source and commercial tools available. Some improvements would be to update the instructions for the hands on labs. But overall I think it is goooooood introduction. Thank You Teachers

I learned a lot in this course and it was definitely worthwhile overall. A heads-up though: the explanation videos for using GitHub are not good. It is better to research how to do that on your own. And once again the quizzes are a bit of a waste of time and the course would be better without them.

The content is very good, a lot of ground is covered in a short space of time. However, a few technical issues prevent 5 stars. The audio quality in some of the videos is awful, as if they were recorded underwater. Also, some of the video notations are missing completely or have no punctuation.

Course is well structured and give a easier way to understand it. The reason why I don't give it a 5 star is that, the content in video is not up to date (like IBM' Cloud). Its platform has changed name to Watson Platform and UI has some differences from video. Hope it can be fixed in the future.

The course structure is good. But please fix IBM Watson Studios' issues. It always doesn't load correctly and you always have to waste hours checking forums on how to fix it. The fixes don't usually work correctly. Even if you clear cache, cookies, use incognito, it still doesn't load correctly.

it was a LOT of information

I wonder if there could be like a printout of background information, or like an index of things that are useful to know before/during the course.

also, if can, more labs to DO and USE the tools a bit more to get a good handle on it from experience

thanks for the course!