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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.
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 gets you up and running in the Jupyter Notebook environment. Together, we’ll build a data project 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.
Lesson 1: Project Jupyter and the Jupyter Ecosystem
1.1 What are Project Jupyter and the Jupyter Notebook?
1.2 How Jupyter facilitates collaboration and sharing in data science
1.3 Differentiate between the Jupyter Notebook and other Jupyter projects
1.4 Find resources and connect with the Jupyter community through Jupyter.org
1.5 Learn through example using the Gallery of Interesting Jupyter Notebooks and GitHub
1.6 Contribute to the Jupyter ecosystem via GitHub
1.7 Participate in open source computing through NumFOCUS
Lesson 2: Creating Data Science Analyses in the Jupyter Notebook
2.1 Determine which Python version to install
2.2 Install Jupyter using the Anaconda distribution of Python
2.3 Start your Jupyter Notebook using the command-line interface (CLI)
2.4 Start your Jupyter Notebook using the Anaconda Navigator
2.5 Run an ephemeral Interactive Jupyter Notebook on the web
2.6 Run Jupyter Notebooks in the cloud using Azure Notebooks
2.7 Run Jupyter Notebooks using Nteract
2.8 Navigate the Jupyter Notebook environment
2.9 Maintain good notebook hygiene
2.10 Perform quantitative exploratory data analysis (EDA) in your Jupyter Notebook using Python
2.11 Perform Visual Exploratory data analysis (EDA) in your Jupyter Notebook using Python
2.12 Create Jupyter Notebooks with different kernels (including R)
2.13 Install the R kernel
Lesson 3: Sharing Jupyter Notebooks
3.1 Work with .ipynb files
3.2 Install nbconvert
3.3 Convert your Jupyter Notebook to different formats: HTML, PDF, and .py
3.4 Create dynamic presentation slides from your Jupyter Notebook using RISE
3.5 Share Jupyter Notebooks using GitHub and nbviewer
3.6 Access Jupyter Notebooks using Azure Notebooks
3.7 Compare and merge Jupyter Notebooks with nbdime
Lesson 4: Exploring New Jupyter Projects In-Depth
4.1 Understand the basics of JupyterHub
4.2 Install and explore JupyterLab
4.3 Work with others using Real Time Collaboration
4.4 Enhance your analysis with interactive Jupyter Widgets
4.5 Share custom environments with Binder and BinderHub