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Jupyter for Data Science Teams Training Workshop

This hands-on workshop is designed for data science teams looking to master Jupyter Notebooks for collaborative data…

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
1Overview of Jupyter and its ecosystem *Introduction to Jupyter Notebook, JupyterLab, and JupyterHub *Explanation of Jupyter's role in data science workflows 2.Installation and setup *Step-by-step guide to installing Jupyter on various platforms (Windows, macOS, Linux) *Configuring Jupyter settings for optimal performance and customization 3.Configuring Jupyter for team collaboration *Setting up JupyterHub for multi-user collaboration *Managing user permissions and access control in JupyterHub environments

Collaborative Features
1.Using Git for version control *Introduction to version control concepts and Git *Integrating Git with enhanced functionality and productivity

Creating and Managing Notebooks
1.Notebook structure and functionality *Understanding the components of a Jupyter notebook: cells, kernels, and markdown *Exploring different cell types and their usage (code, markdown, raw) 2.Sharing and organizing notebooks *Methods for sharing notebooks with team members and external stakeholders *Organizing notebooks into projects and directories for efficient management and retrieval

Programming with Jupyter
1.Choosing and using programming languages (Python, R, Scala) *Overview of supported programming languages in Jupyter and their respective kernels *Best practices for selecting the appropriate language for specific data science tasks 2.Writing and executing code *Writing code in Jupyter cells and executing them interactively *Understanding code execution order and kernel interruptions 3.Integrating with big data systems (Apache Spark) *Overview of Apache Spark integration with Jupyter for big data processing *Running Spark jobs and analyzing large datasets within Jupyter notebooks

Advanced Jupyter Features
1.Customizing Jupyter environment *Personalizing Jupyter interface and themes for improved user experience *Installing and managing Jupyter extensions for additional functionality 2.Automating workflows with Jupyter *Leveraging Jupyter for automating repetitive tasks and data processing workflows *Creating custom scripts and extensions to streamline complex workflows within Jupyter

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