Chevron Left
Back to Tools for Data Science

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

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.

Filter by:

426 - 450 of 4,789 Reviews for Tools for Data Science

By Olaoluwa I

Jul 30, 2022

This course touches everything and more. It has all you need to know about the tools you'd be working with as a Data Scientist. Great course indeed.

By Anwar E E E

Dec 18, 2021

Very interesting for beginners and it expose the learner to streams of tools that can use in the areas of DS, AI and ML. I had enjoyed it very much.

By Julien D

Jun 28, 2020

I really liked this course. I had no problems at all. I also liked Romeo Keinzler. But, I think this should be labeled an Intermediate level course.

By Anar A

May 11, 2020

Very interesting because most of the course based on practical. We started to use 4 different data science platform free and practice. Thanks a lot.

By Abigail B

Jan 6, 2020

This provides a succinct and brief overview of some of the biggest open source software out there, including Python, RStudio, and Jupyter Notebooks.

By Agnieszka K

Nov 5, 2019

Very cool course introducing you to open source environment where you can analyse your data sets, learn and practice, and deliver as data scientist.

By Etta Q

May 4, 2019

A great material to get familiar with Watson studio, which combine many kinds of languages. IBM cloud is also a good place to hands-on your project.

By Conor C

Feb 20, 2024

Very useful, layout could be more clear, i.e., which modules to begin and progress through numerically. Some modules are more advanced than others.

By Farhat K

May 28, 2023

Awesome course with best hands-on Lab experience! Awesome use of Data Science tools like development environments and GitHub repository experience!

By Sunny .

Feb 20, 2021

the course is design very well. In this short course, i learnt lot's of things and it's truly appreciable to all the content designer and teachers.

By Mehreteab Z

Mar 28, 2021

Course was well prepared. Videos very helpful. Exercise questions also help to measure level of my understanding of the subject matter.

Thank you.

By Reza A

Nov 23, 2020

This is a good course in order to be familiar with different parts of data science and relevant tools and application. For me that was amazing! :)

By Deleted A

Apr 8, 2020

Very good and interesting course , you can put your hands on real tools for data science and learn a lot about this tools to apply to your career.

By SACHIN G

Oct 11, 2019

The course is made in very simple language, anyone can learn from this course. The most amazing part of the course is Lab. Love you IBM & COURSERA

By Shaheryar

Sep 9, 2019

Happy to learn the tools that were somehow new to me. It was great to practice as well and good explanation of tools were provided by instructors.

By Dr. S U R

Nov 7, 2021

Amazing Learning Experience, Coursera provides the best environment to learn at your own pace & time in the comfort and privacy of your own home.

By TAN W

Sep 5, 2019

The IBM platform seems was updated and not the same as the description in the course video, if there is an updating it would be perfect. Thanks.

By Rahul K

Jun 5, 2019

This Course is very important if you want to learn for data Science tools, there are the best tutorials here about the explanation of the Tools

By Costik M

Aug 15, 2021

Not so easy but totally worth it.

Gives you basic idea of Data live cycle in industry and what are the most known and used tools to handle it.

By Saad M

Jan 21, 2020

Videos are outdated, as well as its very hard to figure out that how to work with Watson studio, because there free version is full of issues,

By Sounak S

Aug 27, 2021

Had a great experience on learning about open source tools used for Data Science and working on hands on based projects on IBM Watson Studio.

By Felillop

Jun 24, 2021

Un buen curso sobre las herramientas IBM y Jupyter, aconsejo en este curso empezar a ir tomando nota para no perder detalle y quedarse atrás.

By joseph (

Sep 6, 2022

Its a tough and great course but its doable. There is a lot of new stuff you learn especially if you are not coming from an ICT background.

By Husayn Z A

May 14, 2020

I really enjoyed this course. I loved the explanation of the videos and the assignments weren't too hard either. Overall, I really loved it.

By Susan A

Apr 24, 2020

I found this course very informative... I knew very little about all these Open Source tools available to Data Scientist for analyzing data.