Cloud Platforms for Scientific Computing

Modern scientific research often requires massive computational power and flexible storage that traditional local infrastructure cannot provide. Cloud computing offers a revolutionary solution for accessing these resources on demand. This course provides a foundational understanding of leveraging major cloud platforms to support your research workflow. You will gain the skills necessary to deploy secure, scalable, and cost-effective environments for data analysis, modeling, and simulation. What you'll learn: * Understand the core concepts of cloud computing (IaaS, PaaS, SaaS) and their specific applications in academic and research settings. * Configure virtual compute instances and scalable storage solutions optimized for handling large scientific datasets. * Apply fundamental containerization techniques to ensure scientific reproducibility and portability across different cloud environments. * Practice managing costs and optimizing resource allocation to maintain budget constraints during long-running simulations or experiments. * Design basic data pipelines using serverless functions for efficient, event-driven processing of research data. * Learn essential security best practices for protecting sensitive research data stored and processed in the cloud. We begin by defining key cloud terminology and infrastructure models, then proceed through practical steps for setting up compute and storage. The final sections focus on modernizing workflows using tools for reproducibility and cost management. This course is designed for researchers, academics, students, and anyone new to applying cloud computing principles to scientific or data-intensive tasks. No prior experience with cloud platforms is required. Start transforming your research capabilities today by mastering cloud infrastructure.