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.
Course info
Rating
(12)
Level
Beginner
Updated
Nov 5, 2019
Duration
2h 11m
Table of contents
Course Overview
Getting Started with Jupyter Notebooks
Understanding Jupyter Notebooks
Creating Shareable Analyses in Jupyter Notebooks
Working with Cloud-hosted Jupyter Notebooks
Description
Course info
Rating
(12)
Level
Beginner
Updated
Nov 5, 2019
Duration
2h 11m
Description

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.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
(Music) Hi, my name is Janani Ravi, and welcome to this course on Create and Share Analytics with Jupyter Notebooks. A little about myself. I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. As data science and data analytics become even more popular and more specialized, the number and variety of tools and technologies out there can often seem overwhelming. Python has exploded in popularity in recent years, largely because it makes analyzing and working with data so simple. In this course, 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. Jupyter is an execution environment rather than a fully-fledged IDE, but even so, notebooks have various important features that are worth understanding thoroughly. 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'll 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 distributive and collaborative use cases.