Clustering for Entity Resolution: A Guide to Data Matching

Messy, duplicate data can ruin database integrity and lead to inaccurate analysis. Understanding how to group identical entities across disparate data sources is a critical skill for any modern data professional. This text-only course guides you through the core concepts of clustering within entity resolution, from basic distance metrics to modern scalability techniques. You will learn how to design workflows that accurately identify and group records representing the same real-world entities without relying on perfect unique identifiers. What you'll learn: - Understand the fundamentals of entity resolution, record linkage, and deduplication. - Apply distance metrics and similarity measures to text and numeric data fields. - Configure modern blocking techniques to scale your clustering algorithms efficiently. - Master hierarchical and partition-based clustering methods tailored for entity matching. - Evaluate clustering quality using precision, recall, and pairwise metrics. - Practice designing robust workflows that handle high-dimensional text data through vector-based concepts. The course starts with essential data matching terminology and foundational concepts before moving into practical blocking strategies, clustering algorithms, and validation techniques through structured text explanations and code snippets. Designed for beginner data analysts, database administrators, and software developers, this course requires no prior experience with entity resolution. Start reading today to master the art of matching and deduplicating complex datasets.