Jupyter for Data Science Teams Training Course
Jupyter is an open-source, web-based interactive development environment and computing platform.
This instructor-led training (online or on-site) introduces the concept of collaborative development in data science and showcases how Jupyter can be utilized to manage and engage as a team throughout the "life cycle of a computational idea." Participants will go through the process of creating a sample data science project using the Jupyter ecosystem.
By the end of this training, participants will be able to:
- Install and configure Jupyter, including setting up and integrating a team repository on Git.
- Leverage Jupyter features such as extensions, interactive widgets, multiuser mode, and more to facilitate project collaboration.
- Create, share, and organize Jupyter Notebooks with team members.
- Select from Scala, Python, R, or other languages to write and execute code against big data systems like Apache Spark through the Jupyter interface.
Course Format
- Interactive lecture and discussion sessions.
- Extensive exercises and practice opportunities.
- Hands-on implementation in a live-lab setting.
Customization Options for the Course
- The Jupyter Notebook supports over 40 languages, including R, Python, Scala, Julia, among others. To tailor this course to your preferred language(s), please contact us to arrange.
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
Requirements
- Programming experience in languages such as Python, R, Scala, etc.
- A background in data science
Audience
- Data science teams
Need help picking the right course?
Jupyter for Data Science Teams Training Course - Enquiry
Testimonials (1)
It is great to have the course custom made to the key areas that I have highlighted in the pre-course questionnaire. This really helps to address the questions that I have with the subject matter and to align with my learning goals.
Winnie Chan - Statistics Canada
Course - Jupyter for Data Science Teams
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