Data Science Pipelines: Models, Tools, and Cloud Environments

In the world of data science, moving from a local prototype to a scalable production pipeline is a critical transition. This text-based course guides you through the foundational tools, cloud environments, and automated feature engineering techniques needed to build robust data pipelines. You will transition from writing isolated scripts to understanding how data models are prepared, managed, and deployed in modern cloud setups. What you'll learn: - Understand the core concepts of data science models, pipelines, and cloud infrastructure - Perform automated feature engineering using Python and FeatureTools to streamline data preparation - Explore cloud environments and container basics for scalable model deployment - Implement modern dataframe practices and efficient data storage formats for faster processing - Structure clean, reproducible Python code for reliable data pipelines - Evaluate and select the right deployment tools for your data science workflows The course starts with essential terminology and foundational architecture before diving into feature engineering, pipeline construction, and cloud deployment strategies. You will learn entirely through clear, written explanations and practical code examples. Designed for beginner data scientists, analysts, and developers looking to understand the full lifecycle of data models, this course requires no prior cloud experience. Start building scalable, cloud-ready data pipelines today.