Data Science Fundamentals: Preprocessing, Regression, and Evaluation

Data science relies on clean datasets and reliable predictive models to convert raw information into actionable business insights. This course offers a beginner-friendly foundation in core data science concepts, guiding you step-by-step through data preparation, predictive modeling, and model evaluation. You will begin by learning key terminology and foundational theory before diving into practical analytical techniques. Through clear written guides and structured code snippets, you will develop a practical understanding of how data pipelines function in modern environments. What you'll learn: - Understand foundational data science concepts, core terminology, and analytical workflows. - Apply data preprocessing techniques to handle missing values, manage outliers, and scale features. - Build simple linear regression models to predict continuous numeric outcomes. - Construct multiple linear regression models to analyze complex multi-variable relationships. - Evaluate model performance and accuracy using standard metrics such as RMSE and MAE. - Learn modern data handling practices for clean, reproducible data analysis. The course opens with essential definitions and data principles before walking you through practical preprocessing methods. You will then explore regression techniques step-by-step, ending with effective strategies for measuring model error and precision. This course is tailored for beginners, aspiring data analysts, and professionals seeking a solid ground in data science without requiring prior experience. Start reading today to build your foundational data science skillset.