Create and Share Analytics with Jupyter Notebooks
This course covers the important aspects of working with Jupyter notebooks, including installation and the role of kernels, magic functions, and running shell commands. In addition, the power of cloud-hosted Jupyter notebooks is explored on AWS, Microsoft Azure as well as the Google Cloud Platform.
What you'll learn
Python has exploded in popularity in recent years, largely because it makes analyzing and working with data so incredibly simple. Jupyter is an execution environment rather than a fully-fledged IDE, but even so, notebooks have various important features that are worth understanding thoroughly. In this course, Create and Share Analytics with Jupyter Notebooks, you will learn how Jupyter notebooks are a key driver of Python’s popularity, by providing an incredibly intuitive, interactive environment for executing Python programs. First, you will learn how to get up and running with Jupyter notebooks, and how best to leverage features such as markdown to enhance the readability of your code. Next, you will discover how more advanced features such as magic functions work, and how the next generation offering from Jupyter, named JupyterLab goes even further towards a fully-fledged development environment. Finally, you will round out your knowledge by working with cloud-hosted Jupyter notebooks on each of the major cloud platforms. When you’re finished with this course, you will have the skills and knowledge to leverage the full power of Jupyter notebooks and Jupyterlab, particularly in the context of cloud-hosted notebooks for distributed and collaborative use-cases.
Table of contents
- Version Check 0m
- Module Overview 1m
- Prerequisites and Course Outline 1m
- Introducing Jupyter Notebooks 6m
- Demo: Windows - Installing Anaconda and Jupyter Notebooks 5m
- Demo: Windows - Installing Jupyter Notebooks Using Pip 4m
- Demo: MacOS - Installing Anaconda and Jupyter Notebooks 5m
- Demo: MacOS - Installing Anaconda and Jupyter Notebooks Using the Command Line Installer 4m
- Demo: Running Jupyter Notebooks and Jupyter Lab in Docker Containers 5m
- Demo: MacOS - Installing Jupyter Lab Using Pip 5m
- Module Summary 1m
- Module Overview 1m
- Demo: Exploring the Notebook Interface 7m
- Demo: Restarting the Kernel 4m
- Demo: Customizing Shortcuts 3m
- Demo: Notebook Limits and Shutting down Kernels 4m
- Demo: Using Python 2 and Python 3 Kernels 4m
- Demo: Using R and Python 3 Kernels 3m
- Demo: Exploring Line and Cell Magic Commands 7m
- Module Summary 2m
- Module Overview 2m
- Running Hosted Jupyter Notebooks on the Cloud 4m
- Demo: Creating and Working with Notebook Instances on Amazon SageMaker 5m
- Demo: Uploading Notebooks to SageMaker and Using the Terminal Window 2m
- Demo: Exploring and Working with Azure Notebooks 3m
- Demo: Hosted Notebooks on a GCP Deep Learning Virtual Machine 4m
- Demo: Uploading Files and Running Code on GCP 2m
- Summary and Further Study 2m