Text Similarity and Entity Resolution with Edit Distance

Inaccurate, duplicate, and messy text data can ruin your data analysis and database integrity. Understanding how to algorithmically compare short strings is the key to solving entity resolution and data deduplication challenges. This text-based course guides you from the fundamental mathematics of string comparison to implementing high-performance text matching workflows. You will learn how to measure similarity between names, addresses, and product titles, enabling you to merge duplicate records with confidence. What you'll learn: Understand the core concepts of entity resolution, record linkage, and data normalization; Implement classic edit distance algorithms including Levenshtein and Jaro-Winkler; Apply substring-based similarity metrics to handle partial matches and typos; Optimize text matching performance using modern, fast Python libraries; Handle real-world data challenges such as Unicode normalization and case sensitivity; Build a basic deduplication pipeline to identify and group matching text records. You will start with foundational concepts of string metrics and clean data preparation before moving on to step-by-step algorithmic implementations and optimization strategies for larger datasets. This course is designed for beginner data analysts, software developers, and database administrators who want to clean text data. No prior experience with string similarity algorithms is required, though a basic understanding of Python is helpful. Start reading today to master the mechanics of text similarity and clean your data like a professional.