Cloud Data Engineering and Machine Learning Fundamentals

In a world driven by data, the ability to process and analyze massive datasets at scale is a critical skill for modern technology professionals. This course provides a comprehensive introduction to building robust data architectures and machine learning workflows using Cloud Platform services. You will move from understanding basic cloud concepts to designing end-to-end pipelines that handle both batch and streaming data. By the end of this course, you will be able to navigate the lifecycle of data from ingestion to analysis and predictive modeling. What you'll learn: - Understand the core architecture of cloud-based data storage and processing services - Build scalable data pipelines for batch and real-time streaming using Dataflow - Analyze massive datasets efficiently with BigQuery using modern SQL patterns - Apply machine learning principles to develop and deploy models with Vertex AI - Configure automated data workflows to ensure reliable and consistent delivery - Practice data visualization techniques to communicate insights effectively The curriculum starts with foundational cloud terminology and data storage basics before moving into practical applications like stream processing and model orchestration. You will read through detailed technical explanations and code-based examples designed to simulate real-world data engineering scenarios. This course is designed for beginners interested in cloud technology and data science. No prior experience with data engineering or machine learning is required to begin. Start building your foundation in cloud data engineering today.