Note that you can execute your notebook to see the active runtime if it is not visible. If your Notebook is executing, you will be able to see the active runtime environment. Go to the Data Science Project home page and click the Manage tab, and then in the left pane click Environments. You have learned how to create a Jupyter Notebook, now you will manage the environment. Welcome to “Jupyter Notebooks in Watson Studio – Part 2.” After watching this video, you will be able to check the Jupyter Notebook runtime environment, use different types of Jupyter Notebook templates, and and change the kernel within a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers. Towards the end the course, you will create a final project with a Jupyter Notebook. 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. This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. You will understand what each tool is used for, what programming languages they can execute, their features and limitations. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. 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.

This course teaches you about the popular tools in Data Science and how to use them. 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.

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