Jupyter for Data Science Teams

• Programming experience in languages such as Python, R, Scala, etc.
• A background in data science

Audience

• Data science teams

Jupyter is an open-source, web-based interactive IDE and computing environment.

This instructor-led, live training (online or onsite) introduces the idea of collaborative development in data science and demonstrates how to use Jupyter to track and participate as a team in the "life cycle of a computational idea".  It walks participants through the creation of a sample data science project based on top of the Jupyter ecosystem.

By the end of this training, participants will be able to:

• Install and configure Jupyter, including the creation and integration of a team repository on Git.
• Use Jupyter features such as extensions, interactive widgets, multiuser mode and more to enable project collaboraton.
• Create, share and organize Jupyter Notebooks with team members.
• Choose from Scala, Python, R, to write and execute code against big data systems such as Apache Spark, all through the Jupyter interface.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• The Jupyter Notebook supports over 40 languages including R, Python, Scala, Julia, etc. To customize this course to your language(s) of choice, please contact us to arrange.

Introduction to Jupyter

• Overview of Jupyter and its ecosystem
• Installation and setup
• Configuring Jupyter for team collaboration

Collaborative Features

• Using Git for version control
• Extensions and interactive widgets
• Multiuser mode

Creating and Managing Notebooks

• Notebook structure and functionality
• Sharing and organizing notebooks
• Best practices for collaboration

Programming with Jupyter

• Choosing and using programming languages (Python, R, Scala)
• Writing and executing code
• Integrating with big data systems (Apache Spark)

Advanced Jupyter Features

• Customizing Jupyter environment
• Automating workflows with Jupyter
• Exploring advanced use cases

Practical Sessions

• Hands-on labs
• Real-world data science projects
• Group exercises and peer reviews

Summary and Next Steps