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Offered By: IBM

Data Science Tools

Learn about the most popular data science tools, including how to use them and what their features are.

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Data Science

28.6k+ Enrolled
(2.2k+ Reviews)

At a Glance

Learn about the most popular data science tools, including how to use them and what their features are.


In this course, you'll learn about Data Science tools like Jupyter Notebooks, RStudio IDE, and Watson Studio. You will learn what each tool is used for, what programming languages they can execute, their features and limitations and how data scientists use these tools today.

With the tools hosted in the cloud, you will be able to test each tool and follow instructions to run simple code in Python or R. To complete the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio on Cloud and demonstrate your proficiency in preparing a notebook, writing Markdown, and sharing your work with your peers.

This hands-on course will get you up and running with some of the latest and greatest data science tools.

You will Learn:

  • How to use various data science and data visualization tools hosted on Skills Network Labs
  • How to use Jupyter Notebooks including its features and why it's so popular
  • Popular tools used by R Programmers including RStudio IDE
  • IBM Watson Studio including its features and capabilities
  • How to create and share a Jupyter Notebook

Module 0 - Welcome and Course Introduction

Module 1 - Languages of Data Science

Module 2 - Data Science Tools

Module 3 - Packages, APIs, Data Sets and Models

Module 4 - GitHub

Module 5 - Jupyter Notebooks and JupyterLab

Module 6 - RStudio IDE

Module 7 - Watson Studio


  • This course is self-paced.
  • It can be taken at any time.
  • It can be audited as many times as you wish.

  • None

  • None

Romeo Kinsler

Romeo Kienzler
holds a M. Sc. (ETH) in Information Systems, Bioinformatics & Applied Statistics (Swiss Federal Institute of Technology). He has nearly two decades of experience in Software Enineering, Database Administration and Information Integration. Since 2012 he works as a Data Scientist for IBM. He published several works in the field with international publishers and on conferences. His current research focus is on massive parallel data processing architectures. Romeo also contributes to various open source projects.



Svetlana Levitan

Svetlana Levitan
is a Senior Developer Advocate with IBM Center for Open Data and AI Technologies, Svetlana has been a software engineer and technical lead for SPSS for many years. She works on open standards for machine learning model deployment PMML and ONNX. She holds PhD in Applied Math and MS in CS from University of Maryland, College Park. Svetlana loves to learn new technologies, share her expertise, and to encourage women in STEM.



Maureen McElaney

Maureen McElaney is a Developer Advocate at IBM Center of Open Source Data and Ai Technologies. She is on the LF AI Trusted AI Committee underneath the Linux Foundation. She is an organizer for Women in Machine Learning and Data Science and on the board of the Vermont Technology Alliance. She is an experienced community builder and is passionate about building diversity (of all kinds) in tech through education, mentorship, and advocacy.





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