Entity Resolution and Match Quality Evaluation in Python

Duplicate data across different systems can ruin your analyses and lead to costly business errors. Entity resolution solves this by identifying when different records refer to the same real-world entity, but you must know how to verify that your matching algorithm is actually working correctly. This text-based course guides you through the foundational concepts of record linkage, deduplication, and evaluation frameworks. By working through clear written explanations and code examples, you will learn how to set up evaluation pipelines, calculate key performance metrics, and write clean, modern Python code to verify the quality of your matched data. You will gain the confidence to handle messy datasets and ensure your data integration pipelines are accurate and reliable. What you'll learn: - Understand core entity resolution terminology, record linkage concepts, and deduplication workflows. - Configure Python record linkage tools to block, compare, and match datasets efficiently. - Calculate precision, recall, and F-score metrics to objectively evaluate match quality. - Implement modern Python type hints and structured dataframes for readable evaluation code. - Apply confusion matrices to identify false positives and false negatives in your matches. - Practice writing unit tests with pytest to validate your matching rules and threshold logic. Starting with essential definitions and theoretical foundations, the course moves step-by-step through setting up evaluation pipelines and measuring performance using practical, written Python exercises. This course is designed for beginner data analysts, database administrators, and Python developers who want to clean and reconcile messy datasets, with no prior entity resolution experience required. Read through the structured lessons and start improving your data matching accuracy today.