Key Information
About the content
Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!
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.
Syllabus
- 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
Instructors
Romeo Kienzler
Chief Data Scientist
IBM
Svetlana Levitan
Senior Developer Advocate with IBM Center for Open Data and AI Technologies
IBM
Maureen McElaney
Developer Advocate at IBM Center of Open Source Data and Ai Technologies
IBM
Platform
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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.
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.
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