Build the data foundation for reliable predictive analytics and informed business decisions. In this course, you'll explore how enterprise data engineering, data pipelines, data quality, and feature engineering turn raw information into analytics-ready datasets.

You'll examine the predictive analytics data lifecycle and compare cloud data warehouses, data lakes, and modern lake architecture. Learn how batch and streaming pipelines, event-streaming components, and ingestion and processing flows support business analytics. You'll also evaluate streaming data reliability risks and explore techniques for numeric scaling, text transformation, time-series feature creation, and feature selection. As you progress, you'll consider how enterprise feature stores support consistent reuse of predictive inputs. You'll learn to compare data architecture options, design high-level ingestion and processing flows, select suitable preparation techniques, and recommend feature engineering practices for analytics use cases. Designed for learners interested in enterprise data engineering and predictive analytics, this course connects infrastructure decisions with data quality and feature readiness. Its focus is on organizing, validating, and preparing data for downstream predictive models and business reporting—not building or deploying a complete predictive model.