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Curriculum Designed by Experts
The Jupyter for Data Science Teams Corporate Training program is designed to help professionals harness the power of Jupyter Notebooks for data-driven decision-making. Participants will learn to build, share, and manage data science projects collaboratively. This course covers interactive computing, visualization tools, and workflow optimization techniques, enabling teams to streamline their data analysis and reporting processes efficiently. Ideal for organizations aiming to boost their analytical capabilities.
Jupyter for Data Science Teams training is a specialized program that teaches professionals how to collaborate effectively using Jupyter notebooks. The course focuses on interactive coding, visualization, and documentation, enabling teams to streamline workflows and share insights seamlessly. Participants gain hands-on experience with Python, R, and data visualization tools while learning best practices for version control and multi-user collaboration. This training helps organizations boost efficiency, improve productivity, and deliver impactful data science solutions.
1.1 Overview of Jupyter and its ecosystem
- Introduction to Jupyter Notebook, JupyterLab, and JupyterHub
- Explanation of Jupyter's role in data science workflows
1.2 Installation and setup
- Step-by-step guide to installing Jupyter on various platforms (Windows, macOS, Linux)
- Configuring Jupyter settings for optimal performance and customization
1.3 Configuring Jupyter for team collaboration
- Setting up JupyterHub for multi-user collaboration
- Managing user permissions and access control in JupyterHub environments
2.1 Using Git for version control
- Introduction to version control concepts and Git
- Integrating Git with enhanced functionality and productivity
3.1 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)
3.2 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
4.1 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
4.2 Writing and executing code
- Writing code in Jupyter cells and executing them interactively
- Understanding code execution order and kernel interruptions
4.3 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
5.1 Customizing Jupyter environment
- Personalizing Jupyter interface and themes for improved user experience
- Installing and managing Jupyter extensions for additional functionality
5.2 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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