Cleaning NYC Property Sales Data with Python

Real-world data is rarely clean, especially when dealing with complex public records like property transactions. This text-based course guides you through the process of transforming messy real estate datasets into analysis-ready assets. You will learn how to approach raw datasets systematically, diagnosing issues and applying robust Python techniques to resolve them. By working through the specific challenges of NYC property sales records, you will build practical data cleaning skills that translate to any real-world analytics project. What you'll learn: Understand foundational data cleaning concepts and common real estate data anomalies; Identify and handle missing values, duplicate entries, and structural inconsistencies; Detect and manage outliers in property prices and square footage using statistical methods; Apply modern pandas techniques, including nullable data types and method chaining; Standardize address formats and categorical variables for clean aggregation; Validate your cleaned datasets to ensure data integrity and readiness for analysis. The course begins with core definitions and data loading basics, then progresses step-by-step through handling nulls, duplicates, and outliers using written explanations and code-focused exercises. This course is designed for beginners with a basic understanding of Python syntax who want to gain practical data preparation skills. Start reading today to master the essential art of data cleaning.