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Learner Reviews & Feedback for Tools for Data Science by IBM

4.5
stars
29,204 ratings

About the Course

In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their features and limitations. This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R, or Scala. Towards the end the course, you will create a final project with a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers....

Top reviews

DE

Aug 14, 2022

I love the detailing of every aspect of this course. The Labs, the free subscriptions and free trials provided by IBM Skills Network, everything has been so amazing. Thank you Coursera, thank you IBM.

MO

Apr 17, 2023

the best course for the beginner who is going to start his data science journey. This course tells you all options like tools, libraries, programming languages, etc. Highly recommended for beginners.

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251 - 275 of 4,771 Reviews for Tools for Data Science

By JAMES C

Mar 23, 2019

I enjoyed this class. It gives good exposure to Jupyter and Zeppelin notebooks, as well as IBM Watson Studio. Students can spend extra time with these tools to get more depth of knowledge (but still introductory knowledge). Also includes some R.

By Bright O

Oct 29, 2019

The learning has been broken down step by step. This has helped me gained deeper understanding about RStudio, Zeppelin Notebook, Jupyter, Python 3 and more. Now I feel more encouraged to continue the course till the very end. Thank you Cousera.

By Christopher T Y E

Jun 28, 2019

good intro to very very surface essentials of watson, zeppelin, jupyter, rstudio. though i didn't like the relatively extensive reading. video tutorials would be easier to follow cos a pic speaks a thousand words! but tqvm for this course!!!!

By Badal S

May 21, 2020

It is an amazing course that helps us understand the basic tools required to be a Data scientist. The course was indeed insightful and I highly recommend the aspirers of data science or analytics to begin this course and have happy learning.

By HVictor

Sep 18, 2019

I've only had the chance to work with Jupyter notebooks as its what I had originally started learning with. This course allowed me to see other tools that are out there. Expanding my visibility into areas I had otherwise not been aware of.

By Priscilla S

Apr 27, 2020

Great way to learn about the open source tools for data science to dive deeper. One suggestion would be to consider updating the IBM Watson Studio section videos. It appears that significant updates have been made to the website since 2018.

By Anette F

Nov 3, 2018

Great introduction into Open Source Tools and into the basic workings of these tools. I love the labs, this is so hands-on and really gives the most realistic view on data science tasks and how they are done that I have come across so far.

By Kanishk K

Aug 11, 2020

At the beginning, this course tries to overwhelm you with a lot of tools and you'd think IBM is just advertising but later in doing a simple project in this course you'd be thankful IBM provided all the tools in one place in the cloud.

By Neelabh S

Mar 28, 2020

Really nice introductions to these amazing tools such as Jupyter Noteboos, Zeppelin, IBM Watson Studio and RStudio IDE. Very easy to grasp and the final project helps practice all the basics in Jupyter notebook using some Python code.

By Jafed E G

Jul 6, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

By Jason K

May 21, 2019

Very good explanation of all tools that is available to users to enable them to work effectively. The labs also proved helpful with practicing and getting familiar in terms of navigation and getting use to the different environments.

By Mateusz K

Jan 1, 2019

Nice review of existing open source tools and free to use web services implementing those tools. Personally I would also enjoy some introduction to either how to set up those open source tools on a personal computer or private cloud.

By Mahdi G

Jun 14, 2023

Really it is Worthfull Course. Helpfull Training Method. Nice Videos & Specially The Labs Are Worthfull & Helpfull.

I extend my thanks and gratitude To Coursera & IBM To give me chance learning. i Say Best Wishes & Regards. u r great

By Harry F

Sep 21, 2021

Excellent course to begin introduction to the most useful tools for data science from data compilation to model building process.

The videos and demos are very understoodn and show too many information about the data science tools.

By 053 V N

Apr 27, 2020

this course I good enough to under stand which tools are applicable in data processing in data science . thanks Coursera for providing such a course that was very funy I enjoyed my valuable time learning with Coursera and faculty

By Suraj R G

Dec 3, 2019

Fantastic course it was. I got overview of most of the open source tools for Data Science.

The Assignment at the end of the course was also interesting as it summarizes all the things we learned.

Thank you for such awesome content.

By Daniel P

Apr 12, 2019

Eu adorei esse curso. Me ensinou muito mais do que apenas ferramentas de código livre para data science. Aprendi também sobre computação na núvem e ganhei vivência na IBM Cloud, além de aprender sobre como baixar dados públicos.

By Michael S

Apr 5, 2021

I enjoyed these weeks of Data Science introduction so far. Thanks for providing all the tools needed at Watson Studio and rare opportunity to get familiar with the most recent technologies and tools. That this course was about.

By Sakiru Y

May 1, 2020

The course is quite technical but very educational and instructive. Though I got a bit confused when I created the Watson Studio, because the platform was different from what the instructor used. But it is an interesting course

By Mehmet C

Dec 3, 2021

This course was heavier than the first one; it gives more opportunity to experiment on lab sessions which I like and it gives detailed view of a lot of tools that is currently available for people who is interested in data.

By Lokesh D

Jul 23, 2021

A comprehensive course on the different tools used in Data Science pipeline. Since it is an IBM course, most of the tools covered are from IBM, although I felt some more content on non-IBM tools could have been more helpful.

By Aman T

Apr 11, 2020

This course was good It will teach you various open source tools that are being used in data science fields like RStudio, Jupyter notebooks, Scala, Hadoop,Apache spark etc. I would definetly suggest you to take this course .

By DEVASISH A

Jan 2, 2020

Just enough to know the different types of open source tools that can be used to data science. to learn the tool completely, we need to refer to many tutorial materials within.

Good Introduction session for tool applications.

By Deleted A

Aug 16, 2019

The course is exceptionally good in order to introduce you various details about the tools that you require for Data Science Analytics.

Exceptionally well made support by IBM and Coursera is as a whole best for these courses.

By Lakshmi N R M E

Apr 25, 2022

The overview reagarding the tools for data science is very good. It has covered many insights and classification of tools that a data scientist can utilize and also make note of such tools to work effectively with the data.