Data Cleaning and Preparation in Python

Before you can extract valuable insights or build predictive models, your data must be accurate, consistent, and structured. Raw data is almost always messy, and learning how to systematically clean it is the most critical skill for any aspiring analyst or data scientist. In this text-based course, you will transition from struggling with corrupted datasets to confidently diagnosing and resolving data quality issues. You will learn to write clean, efficient Python code to handle common real-world anomalies, ensuring your analysis is built on a solid foundation of reliable data. What you'll learn: - Identify and correct mismatched data types, range constraints, and structural anomalies. - Handle missing values and duplicate records using robust statistical and logical strategies. - Clean and standardize text data, addressing inconsistent formatting and spelling variations. - Apply string similarity algorithms and record linkage techniques to merge disparate datasets. - Utilize modern Python practices, including basic type hints and modern dataframe configurations, to prevent future data entry errors. The course begins with foundational concepts of data quality and essential terminology before guiding you through step-by-step written explanations and practical code snippets. You will progress from fixing simple formatting errors to executing advanced record-matching workflows on complex datasets. This course is designed for beginners who have a basic understanding of Python syntax but are new to data preparation; no previous data science or advanced programming experience is required. Start reading today to master the essential art of preparing clean, analysis-ready datasets.