
This hands-on workshop is designed for data science teams looking to master Jupyter Notebooks for collaborative data analysis, experimentation, and visualization. Participants will learn how to efficiently use Jupyter as a tool for developing, documenting, and sharing their data science workflows. The course covers advanced features of Jupyter, such as interactive widgets, integration with data science libraries, and best practices for collaboration.
By the end of the workshop, teams will have the skills to leverage Jupyter Notebooks for building, sharing, and presenting data science models and analyses, while working seamlessly in collaborative environments.
What Will You Learn?
- ✔ Gain proficiency in creating, running, and sharing Jupyter Notebooks for data science tasks.
- ✔ Learn how to integrate data science libraries and frameworks with Jupyter for data processing, analysis, and modeling.
- ✔ Understand best practices for documenting and sharing Jupyter notebooks for better collaboration within data science teams.
- ✔ Explore advanced features like interactive widgets, widgets for dashboards, and visualization tools to make notebooks more dynamic.
- ✔ Understand how to set up and manage JupyterHub for team-based environments.
- ✔ Master the creation of reproducible data science workflows and use version control for notebooks.
Course Curriculum
Introduction to Jupyter
Collaborative Features
Creating and Managing Notebooks
Programming with Jupyter
Advanced Jupyter Features
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