Social Feed Ranking: Data Strategy and Feature Engineering

Ever wondered how social media platforms decide which posts appear at the top of your feed? Building an effective ranking system requires a smart combination of data strategy, social graph analysis, and real-time feature engineering. In this introductory course, you will explore the foundational concepts of feed ranking systems. You will learn how user interactions, network connections, and content metadata are transformed into powerful features that drive engagement while respecting modern privacy standards. What you'll learn: - Understand the core terminology and architecture of social feed ranking systems. - Analyze social graph data to extract meaningful user relationship features. - Design real-time data pipelines for processing user interactions as they happen. - Engineer high-quality features from text, media, and user engagement metrics. - Apply modern privacy-preserving techniques to protect user data in ranking algorithms. - Explore how vector embeddings and modern recommendation patterns enhance feed relevance. The course begins with key terminology and the basic architecture of content delivery, ensuring you have a firm grasp of foundational definitions. From there, you will progress to social graph structures, real-time pipeline design, and modern privacy considerations. This course is designed for beginners, software engineers, and data enthusiasts looking to understand the mechanics of recommendation systems, with no advanced prerequisites required. Start reading today to build a strong foundation in social media data engineering.