AWS Cloud Data Engineering and Machine Learning Fundamentals

Organizations today rely on the cloud to transform raw data into actionable insights and intelligent applications. Understanding how to orchestrate these complex systems is a vital skill for anyone entering the modern tech landscape. This course provides a clear path for beginners to understand how cloud services, big data frameworks, and machine learning models work together in a professional environment. You will move from learning basic infrastructure to grasping the architecture of modern data lakes and predictive modeling. By the end of this course, you will have a solid conceptual foundation in cloud-based data science and engineering. What you'll learn: - Understand core cloud infrastructure including compute, storage, and security foundations. - Configure data ingestion and processing pipelines using Kinesis and Glue. - Apply big data principles with Hadoop and Spark architectures. - Explore machine learning model development and deployment through SageMaker. - Practice data warehousing and business intelligence using Redshift and Athena. - Learn modern MLOps foundations and serverless data architecture patterns. The curriculum begins with essential cloud terminology and architecture basics before progressing into data engineering frameworks and practical machine learning implementation. You will work through written explanations and code-based scenarios designed to build your technical confidence. This course is designed for absolute beginners and aspiring data professionals. No prior experience with cloud platforms or data science is required. Begin your path toward mastering cloud-based data solutions today.