Feature Engineering for Data Science: Preparing Clean Data

Raw data is rarely ready for machine learning models. To build accurate predictive models, you must first learn how to clean, transform, and structure your data effectively through feature engineering. This text-only course guides you through the essential techniques of feature engineering, helping you turn messy real-world datasets into clean inputs that boost model performance. You will learn to: * Understand foundational feature engineering concepts and why data preparation is critical for model success. * Handle missing data systematically using imputation and indicators without introducing bias. * Encode categorical variables using dummy variables, one-hot encoding, and modern mapping techniques. * Transform date and time variables into structured, model-friendly numerical features. * Apply scaling and normalization to ensure features are on a comparable scale. * Implement clean, reproducible data preparation pipelines using modern Python dataframe libraries. You will begin by exploring basic terminology and foundational data concepts before moving step-by-step through practical transformation methods. The course concludes with a consolidated workflow demonstrating how to combine these techniques into a cohesive data preparation pipeline. Designed specifically for beginners, this course requires only a basic familiarity with data concepts and no prior advanced machine learning experience. Start mastering the art of feature engineering and unlock the true potential of your data science models today.