Containerization for Data Scientists

Containerization for Data Scientists equips participants with essential skills to deploy and manage data science applications using containerization technologies. The course emphasizes hands-on projects that simulate real-world scenarios, enabling learners to grasp the intricacies of container orchestration, scaling, and deployment. By engaging in collaborative exercises and sharing outcomes in Cademix Magazine, participants will enhance their professional visibility and contribute to the community.
The curriculum is designed to provide a comprehensive understanding of the container ecosystem, focusing on tools such as Docker and Kubernetes. Participants will explore best practices for building, deploying, and maintaining scalable data science applications. The final project will challenge learners to create a fully functional data science application within a containerized environment, ensuring they leave with practical experience and a portfolio piece that highlights their capabilities.
Introduction to Containerization and its Importance in Data Science
Overview of Docker: Installation, Configuration, and Basic Commands
Building Custom Docker Images for Data Science Projects
Managing Docker Containers: Lifecycle, Networking, and Storage
Introduction to Kubernetes: Architecture and Key Concepts
Deploying Applications on Kubernetes: Pods, Services, and Deployments
Scaling Applications with Kubernetes: Horizontal and Vertical Scaling
Monitoring and Logging in Containerized Applications
Best Practices for CI/CD in Data Science Workflows
Final Project: Developing a Containerized Data Science Application

To equip participants with the skills to effectively utilize containerization technologies in data science, enabling them to deploy and manage applications efficiently.

Graduates, job seekers, business professionals, and optionally researchers or consultants interested in data science and containerization technologies.

Basic knowledge of programming (Python preferred) and familiarity with data science concepts.