Data Preprocessing for Entity Resolution

Inaccurate, inconsistent, and messy data can completely derail your data matching and record linkage efforts. To connect duplicate records across disparate systems, you must first master the art and science of data preparation. This written course guides you through the foundational concepts of data quality, showing you exactly how to clean and standardize your inputs before running matching algorithms. You will learn how to identify, isolate, and resolve common data anomalies across text, numeric, and location fields. By establishing a solid preprocessing pipeline, you will significantly reduce false positives and false negatives, ensuring highly reliable data matching outcomes. What you'll learn: - Understand the core principles of entity resolution and why preprocessing is critical for accuracy - Standardize and clean text data by handling punctuation, casing, and white spaces - Normalize numeric fields and dates to ensure consistent formats across different data sources - Resolve location and address discrepancies using parsing and standardization techniques - Apply basic vectorization and string distance preparation concepts to set up downstream matching - Implement modern data quality checks using Python-based validation practices Starting with fundamental definitions and key data quality terminology, the course moves systematically through structured text normalization, numeric standardization, and geographic data cleaning. You will read through practical examples and review structured code snippets that demonstrate how to transform raw, noisy datasets into clean, match-ready inputs. This course is designed for beginner data analysts, database administrators, and aspiring data engineers who want to understand the mechanics of data cleaning. No prior experience with entity resolution is required, though a basic familiarity with data handling concepts is helpful. Start reading today to build cleaner datasets and achieve highly accurate matching results.