Regression Models for Data Analytics: Foundations and Practical Estimation

Understanding the relationships between variables is the cornerstone of predictive data analytics. This text-based course guides you through the fundamental concepts of regression analysis, helping you transform raw data into actionable predictions. By the end of this course, you will transition from understanding basic statistical correlations to constructing, evaluating, and interpreting your own regression models, laying a solid foundation for advanced data science and analytics. What you'll learn: 1. Understand the fundamental principles of dependent and independent variables. 2. Differentiate between point and interval estimation to make precise predictions. 3. Build simple and multiple linear regression models using modern data analysis concepts. 4. Evaluate model performance using key metrics like R-squared, MAE, and RMSE. 5. Apply train-test split techniques to prevent overfitting and ensure model reliability. 6. Interpret regression coefficients and statistical significance to drive data-informed decisions. The course begins with foundational statistical concepts and core definitions, then moves progressively into practical model building, estimation techniques, and performance evaluation. This program is designed for aspiring data analysts and beginners with no prior modeling experience. Start reading today to master the core predictive modeling techniques used by modern data professionals.