Data Preprocessing and Feature Engineering for Beginners

Raw data is rarely ready for analysis or machine learning, often containing gaps, inconsistencies, and errors. Mastering data preprocessing is the crucial first step to ensuring your data models produce accurate and reliable results. This course guides you from raw data collection to clean, model-ready datasets. You will understand how to structure variables, handle anomalies, and engineer features that improve model performance. What you'll learn: - Understand foundational data types and ethical collection methods - Clean datasets by identifying and treating missing values and outliers - Transform variables using scaling, normalization, and encoding techniques - Engineer new features to extract maximum predictive power from raw data - Analyze relationships between variables using correlation checks - Apply modern data preparation workflows to both numerical and categorical text data The course begins with core definitions and data collection ethics before progressing to hands-on data cleaning, transformation, and feature engineering techniques. You will read through clear explanations, conceptual breakdowns, and practical code-based examples. This course is designed entirely for beginners, and no prior data science experience is required to get started. Start building a solid foundation in data preparation today.