Data Science Foundations and Cloud Deployment with AWS

Modern data science goes far beyond local notebooks; deploying models to the cloud is now an essential skill for every aspiring data professional. This text-based course guides you through the core concepts of data science and shows you how to leverage cloud infrastructure to scale your workflows. You will start with foundational data science terminology and basic machine learning concepts before moving step-by-step into cloud integration. By reading through clear, written explanations and practical code examples, you will understand how to transition your data models from your local environment to cloud-hosted pipelines. What you will learn: Understand the fundamentals of data preprocessing, feature engineering, and model training; Configure cloud storage and data pipelines using AWS services like S3 and SageMaker; Deploy machine learning models as scalable web services in the cloud; Implement modern MLOps principles to monitor and maintain your deployed models; Practice writing clean, production-ready Python code for cloud environments; Explore basic vector database concepts and retrieval-augmented generation patterns. The course begins with essential definitions and data preparation techniques, then guides you through building your first models, and finishes with practical cloud deployment strategies. This course is designed for beginners who have a basic understanding of Python and want to learn cloud-based data science. No prior cloud experience is required. Start reading today to bridge the gap between local data analysis and scalable cloud deployment.