Learner Reviews & Feedback for Tools for Data Science by IBM

4.5

stars

29,295 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

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.

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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4776 - 4789 of 4,789 Reviews for Tools for Data Science

By Mohamad A T

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Apr 8, 2019

Not updated

By SATVIK V

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Jul 6, 2022

TOO HASTY.

By Jonathan Q

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Jul 27, 2022

no update

By Muhammed S U

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Dec 2, 2020

It sucks.

By Naga s T

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Feb 14, 2023

Not good

By Bethany L

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Nov 20, 2022

Terrible

By Korawan E

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Jun 4, 2020

Very bad

By Viswajith E S

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Nov 8, 2021

IBM AD

By David E P B

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Jul 25, 2020

so bad!

By lianghui t

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Jan 16, 2019

useless

By Carlos P d V

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Nov 12, 2020

Pesimo

By Pony F

•

Sep 8, 2020

Boring

By ABINAYA.S

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Jun 14, 2022

worst

By Yu T

•

Jun 18, 2021

dumb