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Learner Reviews & Feedback for Tools for Data Science by IBM

4.5
stars
28,175 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

ED

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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376 - 400 of 4,596 Reviews for Tools for Data Science

By JUDIKA R S T

Jun 28, 2020

This course help me as beginner to understand about tools for data science such as rstudio, IBM cloud to create notebook and how to create simple code using phyton

By Vivekanand P

Sep 11, 2019

Nicely documented learning. Only suggestion is to update the content of lab where instructions are per the old DSX tool and are not exactly same for Watson studio.

By Ramon M

Apr 19, 2020

Great course! I learned about so many open source tools for data analysis. I also learned how to use these open source tools at a basic level. FOUNDATION SECURED!

By Yasmin

Apr 19, 2020

Clear and easy to follow course. very rich in resources and self-learning materials to sharpen your knowledge even if totally new to programming and data science

By SIU C C

May 9, 2022

This course in terms of the contents is good. However, when come to topics regarding line commands for GitHub, it is too fast and cannot learn anything indeed.

By Keiandra K

Mar 12, 2023

This course is easy to understand and as a beginner you can learn the tools pretty effecively. It provides detailed information and the opportunity to explore.

By Senthil K T

Aug 4, 2022

Given sufficient information about the tools were using in datascience. The video explanations are too fast we have to steady pause to get what are instructed.

By Abdulkadir G

Mar 6, 2022

The course helped me tremendously and enhanced my skill set. I did not know my Keras from my Pyspark before this course but I am now using Pyspark comfortably.

By Min T A

Feb 14, 2021

I gained a lot of valuable information with regard to the tools and scope of data science. With that, I now have more confidence in my data science profession.

By Mina W

Jun 23, 2020

I enjoyed this level a lot and am astonished by the capabilities of the Watson Studio. I am really glad that I can finally find my way on these powerful tools.

By Andrew K J

Jul 12, 2019

I found this course very useful. I especially enjoyed practice with new markdown tools in Jupyter which were very useful for creating well formatted notebooks.

By Ramiro B

Sep 22, 2019

As elementary as it could be, it's a great introduction to Jupyter Notebooks indeed. But starting from this, I could see farther the reaching of Data Science.

By Patricia P

Jan 17, 2019

São muitos recursos e um mundo de ferramentas. O curso passa pela mais relevantes e propõe atividades práticas em cada uma delas. Muito bom ter este panorama.

By Lydia

Jan 31, 2021

Informative course: the course introduces Jupiter Notebook and R Studio. Good start to learn more about programming languages that are used in data science!

By VINESH P

May 16, 2020

A great course especially for starting with Jupyter Notebook, Zapellin or R studio. IBM Watson Studio is definitively the most crucial aspect of this course

By Azhan A

Sep 5, 2019

Awesome course. Lots of Learning. These free sources adds lots of ease to your work, its like everything tool you can think of is present in a single place.

By Onkar S

Aug 23, 2019

Really helpful in terms of getting a knowledge about the main core technologies used in Data Science.I would recommend this course to the absolute beginner.

By Girish P B

Jun 26, 2019

Course content was good, but IBM watson contents need to be updated and lots of issues while creating project. hopefully it gets resolved for other students

By Valentine D

Apr 19, 2020

A very good course for anyone who wants an understanding of the opensource tools available for Data Science. I had a very good experience and learnt a lot.

By Oleg E

Jul 26, 2022

Хороший курс, информативный. С каждым шагом становится понятнее, что и к чему в больших данных. Было интересно. Хорошая подача материала. Мне понравилось.

By jorge

Apr 6, 2020

It was helpfull to know which tools you can use, a small presentation of all of them. After, you have to find new tutorial to learn about them more deeply

By Ekwoge E B

Jun 10, 2019

This course help open a very broad area of tools to use for Data Science I had no idea even existed. I am thrilled and motivated to work on this even more

By Maria N L

May 16, 2019

Excellent introduction to basic open source tools used in data science; however, the content needs updating since the UI of IBM Watson Studio has changed.

By Raíssa B T

Mar 3, 2020

I liked very much the content. I know is an easy course, but I'd like to see more of what can be done, the languages etc. Thanks IBM for all this effort!