I have to give it 5 star cause it was so explanatory and exciting, though the video instructions need to be upgraded cause the web interface as at April 19th, 2020 isn't the same as the Video instructions. So that needs to be upgraded!!! But you work your way round it if you pay attention!!!

Nevertheless, it's a wonderful course and very exciting!!!!

Exceptionally well-organized and providing diverse and extensive information about a wide array of tools in the most accessible and constructive manner. The additional module on Watson Studio was a wonderful surprise, offering me the opportunity to become acquainted with one of the most valuable and user-friendly tools for Data Science. Thank you!

Good Course. Will suggest the learners to do optional labs and hands on with Jupyter Labs using Kaggle notebooks and explore IBM cloud and Watson Studio features such as Data Refinery to get the maximum out of this course. This course can easily take 25-30 Hours if you are a beginner and complete all hands on Kaggle exercises including GitHub.

I felt for the first time that I can make it to this world of IT! The course puts you in the driver's seat and gives you a glimpse of what this profession is all about. Great content and practice! I make emphasis in the practice, if you make it through, this can be your new job in the future! Thanks to Coursera and IBM for such a great course!

Loved it! The most useful aspect was the cognitive class cloud web environment, which truly allows you the flexibility and mobility to practice from anywhere. There is no need to download install software, so you can access the course, literally from any machine, in a seamless manner. That's a very neat feature for someone who is working.

The videos in this course could certainly use a good update since many of the websites they ask you to use have been updated/changed/improved. Overall, this course is overflowing with very useful info and resources that you could need right away or in the near future, especially if you're new in this field (like me). I liked it a lot :)

Assignment assessment is not controlled and sometimes assessors don't assess based on the right content. I encountered this issue in several courses' assignments. I hope that the peer-graded assignment undergoes some revision to establish a more credited assessment approach.

Thank you for your consideration.

Coursera is always the best!

This is a very informative course, provide data science newbies with various of tools to learn about on their data science journey. Not only that, this course also explained to me that doing data science isn't just analyzing data by coding with R or Python, it is more than that, with preparing data, storing data and building models.

I learned so much in this course. I had no idea these tools were available in one place online. In preparation for doing the IBM Professional Certificate, I spent about a week installing programs and languages and stressing out about GUIs and IDEs. This course showed me multiple ways to get around all this and use IBM online tools.

A very good informative course! All tools of data science are discussed. A bit practical sense of few important open source tools is given nicely. You will uderstood that there are wide variety of tools for data science! Great one! This will push the beginners out of their theoretical zone to see the real information and tools.

A lot of this is outdated since the IBM Watson stuff has been completely revamped, and the Zeppelin Notebooks tutorials were difficult to follow, since they seemed to have already been run when I went in, and therefore I couldn't tell what was function and what was output. But it's great to have all these free tools available.

Overall, I found tremendous value in this course. However, I noticed that the MAX is no longer available, which led to some confusion. While the course content is largely beneficial, some of the information seems to be outdated. I would recommend an update to ensure future learners have the most current and accurate resources.

This actually helps me since I have no background regarding the tools needed for development in Data Science. Week 1 and 2 are particularly helpful since it introduces a lot of tools that one could use. Week 3 is a bit promotional and week 4 is true challenge. The course really guides you on how to start jupyter notebook.

It was a great experience to learn the various open-source tool for data science. I have gained considerable knowledge of the Jupyter Notebook and IBM Watson Studio. It will be of great help for learners if the data science experience tool videos will be modified with IBM Watson Studio navigation and notebook creation.

Loved the course! It presents integrated environments where we can perform data analysis (+all previous and post steps) using multiple languages in open-source tools. IBM Skills Network Labs is perfect for learning, IBM Watson Studios enables collaboration and scalability, for enterprises. Super convenient tools!

the course is great for people getting into the field of data science and have no clue where to start with resources for the filed. It helps build confidence and security knowing there are resources at our finger tips, for free, and with guidance from the the tutorial videos provided in this course. great stuff.

Very informative on the open source tools available. It does get tricky sometimes to understand the instructions of the notebooks as the videos display an older version and the current website would have the updated version. IBM Cloud is a huge advantage to work on Python,R and Scala with spark kernels for free.

Very beneficial, albeit somewhat painful , in getting the assignment done since the videos (at the time of this review) are not exactly correlated with current software (Watson v. Data Experience).

But hey, we are to be problem solvers, so that was just one minor hurdle to overcome and learn from in the process!

This course was intensive on tools used in Data Science. It was an overwhelming experience for I learnt to use resources on Github and understood how Jupyter notebooks are important in writing long codes. All in all , a great experience and would like to complete the full IBM data science specialization soon.

It's a good overview of all the tools that can be used for data science. If you're following along with the IBM course, it gives you a good idea of what you could be using for your capstone class. They do not detail much of tools rather introduce them to you. It'd be up to you to delve deeper if you'd like.

A very gentle and slightly challenging introduction to the some of the best tools in the Data Science fraternity. Also, the added introduction to the IBM Watson Clouds tools was seriously interesting. Who knew you could just drag and drop files of refined data and you are on your way to "Model Building"?

Astonishing course for learning the basics of the tools used for data science, open source tools and comercial tools, at the beginning it might be a bit overwhelming because of lots of terms that are unknown by most starters like me, but as the course goes on and if you are commited, it's a piece of cake

As someone with no computer science background, I am fascinated but i did struggle at times. As a layperson, I simply persevered, relearn and somehow survived until the end. So anyone (regardless of background ) with enough determination can also get through this and benefit from it. Good Learning

Nice in introducing approaches to data science; however, some parts appears unnecessary to be mentioned right in the beginning. For example: Appache Zeeplin and Zeppling for Scala are more of courses in either intermediate or advanced level. Perhaps postponing it till database would be a better option.

This is a very comprehensive presentation on the available tools for Data Science both Open Sources and that of IBM proprietary tools. As presented, you as a Data Scientist has the sole option which of these tools are fit for your data science studies. Very enlightening and full of thoughts to ponder.