Master Data Science DevOps: Containerization with Docker & Kubernetes

What you will learn:

Beginner-friendly Docker introduction
Hands-on Docker command exercises
Docker Compose mastery
Docker Swarm understanding
CI/CD pipeline implementation
Git integration for version control
Cloud deployment strategies (AWS, Azure)
Container monitoring and management
Infrastructure as Code (IaC) basics
Real-world DevOps case studies

Description

Elevate your data science career with our comprehensive guide to DevOps and containerization!

In today's data-driven world, efficient data science workflows are crucial. This course bridges the gap between data science and software engineering, teaching you to leverage DevOps principles and container technologies like Docker and Kubernetes to streamline your projects.

Learn to:

Build reproducible and portable data science environments using Docker.
Master essential Docker commands for efficient container management.
Understand and utilize Docker Compose and Docker Swarm for advanced orchestration.
Implement Continuous Integration/Continuous Deployment (CI/CD) for automated workflows.
Collaborate seamlessly with software developers and manage versions effectively with Git.
Optimize resource usage and scale your applications for larger datasets.
Deploy your applications to cloud platforms (AWS, Azure).
Monitor and manage your containerized environments using best practices.
Explore Infrastructure as Code (IaC) and its integration into data science.
Analyze real-world case studies and apply industry best practices.

This course empowers you to create efficient, reproducible, and scalable data science pipelines. Whether you're a solo data scientist or part of a larger team, you'll gain the skills to confidently manage and deploy your projects. Enroll today and transform your data science capabilities!

Curriculum

Introduction to DevOps & Data Science

This section begins with a foundational understanding of DevOps principles and their relevance in the data science field. The first lecture, "Understanding the Basics of DevOps and Its Application in Data Science," provides a comprehensive overview of how DevOps methodologies can improve efficiency, collaboration, and reproducibility in data science projects. This is crucial for understanding the overarching framework of the course.

CI/CD and Version Control

This section focuses on streamlining your workflow through continuous integration and continuous deployment (CI/CD). The lecture, "Continuous Integration and Continuous Deployment (CI/CD)," covers the implementation of CI/CD pipelines, essential for automating the testing and deployment of data science applications. It highlights best practices for integrating version control (like Git) to ensure seamless collaboration and track changes efficiently.

DevOps Principles in Data Science

Deepening your understanding of DevOps practices, this section explores the practical application of DevOps principles within data science. The lecture, "The application of DevOps principles in data science," delves into specific strategies and techniques for applying DevOps to improve the entire data science lifecycle, from development to deployment and maintenance.

Containerization Fundamentals

This section introduces containerization technologies, primarily focusing on different types of containers and their use cases. The lecture, "Examining the Different Types of Containers," provides a detailed look at various container technologies and how they facilitate reproducible data science environments. This section sets the stage for practical application in later modules.

Container Management & Monitoring

Here, you will learn how to effectively manage and monitor containers in a production environment. The lecture, "Monitoring and managing containers in a production environment," covers crucial techniques for overseeing container health, performance, and resource usage. This is vital for maintaining the stability and efficiency of your deployments.

Optimization, Deployment & Scaling

This section focuses on optimizing resource usage and scaling your applications. The lecture, "Optimize resource usage and efficient deployment and scaling of applications," guides you through strategies for efficient resource allocation, deployment techniques, and scaling your applications to handle increasing workloads and data volumes, ensuring your applications remain performant and reliable.