Efficient Record Linkage with Splink and Python

Duplicate records and fragmented datasets can quietly ruin your data analysis and machine learning models. Splink offers a powerful, Python-based solution for fast, accurate entity resolution at scale. In this course, you will master the foundational concepts of record linkage, learning how to configure blocking rules to drastically reduce unnecessary comparisons. You will learn to prepare your data, implement efficient indexing strategies, and resolve matching entities without exhausting your computational resources. What you'll learn: - Understand the core principles of entity resolution and probabilistic record linkage - Configure efficient blocking rules in Splink to minimize computational overhead - Prepare and clean raw datasets using modern Python dataframe practices - Apply term frequency adjustments to improve match accuracy - Analyze and evaluate model performance using linkage metrics - Integrate Splink with SQL-based backends like DuckDB for faster processing Starting with foundational definitions and key terminology, you will progress from basic data preparation to writing advanced blocking rules and evaluating your deduplication results. This course is designed for data analysts, engineers, and beginners to data matching who want to clean up duplicate datasets using Python. No prior experience with record linkage is required. Start reading today to master efficient entity resolution and clean your data with confidence.