Using Jupyter Notebooks for Data Science Analysis in Python LiveLessons

2+ Hours of Video Instruction

Create an end-to-end data analysis workflow in Python using the Jupyter Notebook and learn about the diverse and abundant tools available within the Project Jupyter ecosystem.

Overview

The Jupyter Notebook is a popular tool for learning and performing data science in Python (and other languages used in data science). This video tutorial will teach you about Project Jupyter and the Jupyter ecosystem and get you up and running in the Jupyter Notebook environment. Together, we’ll build a data product in Python, and you’ll learn how to share this analysis in multiple formats, including presentation slides, web documents, and hosted platforms (great for colleagues who do not have Jupyter installed on their machines). In addition to learning and doing Python in Jupyter, you will also learn how to install and use other programming languages, such as R and Julia, in your Jupyter Notebook analysis.

Skill Level

• Beginner
• Intermediate

• Create a start-to-finish Jupyter Notebook workflow: from installing Jupyter to creating your data analysis and ultimately sharing your results
• Use additional tools within the Jupyter ecosystem that facilitate collaboration and sharing
• Incorporate other programming languages (such as R) in Jupyter Notebook analyses

• Users new to Jupyter Notebooks who want to use the full range of tools within the Jupyter ecosystem
• Data practitioners who want a repeatable process for conducting, sharing, and presenting data science projects
• Data practitioners who want to share data science analyses with friends and colleagues who do not use or do not have access to a Jupyter installation

• Basic knowledge of Python.
• Download and install the Anaconda distribution of Python here. You can install either version 2.7 or 3.x, whichever you prefer.
• Create a GitHub account here (strongly recommended but not required).
• If you are unable to install software on your computer, you can access a hosted version via the Project Jupyter website (click on “try it in your browser”) or through Microsoft’s Azure Notebooks.