Course Outline

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


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


  • Data science teams
 7 Hours

Testimonials (1)

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