Foundations of Data Cleaning: Duplicates and Missing Data

Working with real-world data often means confronting messy datasets filled with inconsistencies. Learning to identify and resolve these issues is a fundamental step towards accurate analysis. This course will transform your ability to approach raw data, equipping you with the essential skills to clean and prepare it for any analytical task. What you'll learn: Understand the fundamental concepts of data quality and common data issues. Apply effective strategies for detecting and managing duplicate records in datasets. Recognize and categorize various types of missing data, including NaN values. Implement robust techniques for handling missing data, such as imputation and deletion. Practice assessing data completeness and consistency through basic profiling methods. Grasp the critical importance of clean data for reliable analysis and accurate insights. The course begins with foundational definitions and progresses through practical identification methods, various handling strategies, and best practices for maintaining data integrity. This course is designed for absolute beginners with no prior experience in data cleaning or data analysis. No specific software knowledge is required. Start your journey to becoming a confident data cleaner today.