Data Scrubbing Foundations for Machine Learning

Raw data is rarely ready for machine learning models, often containing missing values, inconsistent formatting, and redundant features that degrade model performance. Learning how to clean and preprocess this data is the most critical step in any data science workflow. This course guides you through the foundational principles of data scrubbing, transforming messy, real-world datasets into clean, model-ready inputs. You will gain a clear understanding of how data preparation directly impacts model accuracy and learn how to make informed preprocessing decisions. What you'll learn: Understand key terminology and foundational concepts of data quality and preprocessing; Identify and handle missing or corrupted data using modern imputation strategies; Encode categorical variables accurately using one-hot encoding while avoiding data leakage; Apply dimensionality reduction techniques to simplify complex datasets; Clean datasets efficiently using modern dataframe library standards. The course begins with core definitions and data quality concepts before moving step-by-step through practical scrubbing techniques, from handling null values to final feature selection. You will read structured explanations and analyze clean code patterns to build a solid foundation in data preparation. This course is designed for beginners entering the data science and machine learning space, with no prior data cleaning experience required. Start reading today to build cleaner, more reliable machine learning models.