What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. 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. To end the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.
Open Source Tools for Data Science - Part 2
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Reviews
Sep 25, 2020
The course is interesting. It presents large spectrum of tools. It could be more helpful to provide general information on different tools and focus on few of them such as R, GitHub for example.
Jun 29, 2019
This course is very nice to understand Python, Zappelin and R Studio basics on code and concepts, in which you will get hands on along with creating a free IBM Cloud and Watson Studio account.
From the lesson
Data Scientist's Toolkit
This week, you will get an overview of the programming languages commonly used, including Python, R, Scala, and SQL. You’ll be introduced to the open source and commercial data science tools available. You’ll also learn about the packages, APIs, data sets and models frequently used by data scientists.
Taught By
Aije Egwaikhide
Senior Data Scientist
Svetlana Levitan
Senior Developer Advocate with IBM Center for Open Data and AI Technologies
Romeo Kienzler
Chief Data Scientist, Course Lead
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