An amazing review of available tools for data science an data analysis. Many tools are reviewed, including tools for programing with different languages, cloud for online storage etc with some handy works to get familiar to them. The practices include the procedure of creating personal accounts, free profile in the IBM cloud. One ends up having a nice overview of many available tools for data science.

Good Course! Goes through the basic tools for Data Science. The course assignments are fairly easy, especially if you have worked on any of these tools before. The IBM resources on the cloud that are used for this course are fantastic, although they are slow to load at times or confusing to navigate. But with some practice, it is easy to master them and take advantage of these open source tools!

Good Day

I am personally thankful and grateful for this opportunity .

Thanks and Warm Regards.

ABHIJIT SENGUPTA

Portfolio URL : https://about.me/abhijitsengupta

Website : www.pactolianconsulting.com

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Kolkata , India

Skype : abhijit.sengupta357

Ph. : + 91 33 25907110

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+ 91 6290750012

I did not know about Cognitive Class Labs. I was trying to install all these different packages in order to be able to work with them and make progress but I have always had difficulties. I am super excited that I finally realised that there is a much easier way to work with all these amazing tools on line without any problem :-) Thanks a lot Coursera and IBM for this amazing course :-)

The course was well structured and I got to know the importance of all the included tools and how they work. One thing, I'd like to add is the tool limitations and errors we get usually. Well, I figured out after googling and researching, but then as they were usual errors, it'd have been great if we had a cheat sheet or something to avoid those errors time to time.

It was a very good introductory course to the Open Source tools available for the practice of Data Science. The pace is normal and the contents are enough to understand and fulfill the requirements in the final project assignment. The IBM Watson Studio presented some issues but they were solved in less than 24 hours by the IBM Engineers. Kudos to the support team!

It is very useful to know the IBM cloud platform and practical. The cloud platform is very complicate and may do many things. It looks like a forest for me.

Some videos is old interface. If there are step by step instructions, it will save me some time to quickly start. Assignment is easy, but take time to get used to the environment and to know where am I.

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!!!!

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.

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!

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.

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.

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

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.

Good introduction to free and paid programs available for practicing and understanding data science! It shows detailed UI walkthroughs and tutorials, and gets you started setting up accounts that you will most likely use many years while learning data science and programming (R, Scala, Python, etc).