Learner Reviews & Feedback for Tools for Data Science by IBM

4.5

stars

29,112 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.

Filter by:

1076 - 1100 of 4,751 Reviews for Tools for Data Science

By John H

•

Jul 12, 2019

Good introduction of a few open source data analytics platforms.

By Pitchaiah U

•

Jun 4, 2019

Nice videos with hands on availability using Skills network labs

By Dr. S P

•

May 26, 2023

overall very grate experience with this course.

very informative

By Anthony D

•

May 7, 2023

Excellent course. I've learned a lot about through the journey.

By Carol O

•

Apr 2, 2023

Informative course and kept me motivated throughout the course.

By Bucyeye S M

•

Aug 28, 2021

I learnt about many tools I can use in my Data science projects

By Pali M

•

Jan 25, 2021

The course is very detailed with interesting practical examples

By Deleted A

•

Jul 18, 2020

Great intro to IBM Watson and some core tools for Data Science!

By Luis l L L

•

Apr 15, 2020

EXCELENT, MORE TIME ABOUT THE TOOLS AND NOT ABOUT CONFIGURATION

By Utkarsh S

•

Jan 18, 2020

Nice explanation of tools that are almost used in data science.

By Mazen G A

•

Oct 26, 2019

this course is a great introduction to theses open source tools

By Chirag P

•

Jul 5, 2019

Very useful intro to data science tools + lots of free stuff :)

By longmen

•

May 16, 2019

it's a great course. Thanks for allowing me to take the course.

By Abhishek M

•

Nov 15, 2018

Watson exp is awesome, I am happy to see IBM SPSS Modeler there

By MD K A

•

Oct 14, 2018

Great course to know about open source tools for Data Science!!

By Juan C G M

•

Jul 10, 2024

Curso muy completo sobre herramientas para la ciencia de datos

By Mateo F

•

Aug 21, 2023

Me pareció mu didactica la propuesta. Lo recomiendo totalmente

By Ekaterina K

•

May 30, 2023

Great introduction. lots of additional information to explore.

By Bryan E

•

Jan 9, 2023

Great course! Thank you for all the knowledge and information!

By olujimi d

•

Aug 21, 2022

learnt alot from this course

the experience was a wonderful one

By Soumya G

•

Aug 11, 2021

Thanks for this learning looking ahead for more opportunities.

By 管正国

•

Jul 17, 2021

good! i think AutoAI is good and i want to learn how to use it

By Sourabh G

•

Jul 14, 2021

Gives an insights into tooling as one may require at beginning

By Sandra T

•

Jun 23, 2021

Extremely well organized modules and I found it really useful.

By Anuj G

•

May 5, 2021

amazing course to know what tools are required in data science