Data Science Modeling: From Regression to Deep Learning

Raw data is only valuable when you can extract meaningful patterns and predict future trends. Understanding how to build, evaluate, and fine-tune predictive models is a core skill for any aspiring data professional. This text-only course guides you from the fundamental concepts of data modeling to neural networks. You will gain the confidence to analyze continuous data, choose the right algorithms, and interpret model performance using industry-standard metrics. What you'll learn: - Understand foundational data science concepts and the modeling lifecycle - Apply linear and multiple regression techniques to predict continuous variables - Build deep learning neural networks for complex non-linear relationships - Evaluate model performance using modern metrics like MSE, R-squared, and cross-validation - Prepare and clean datasets using modern data-wrangling workflows - Implement basic model tracking and reproducibility practices to ensure reliable results You will start with core definitions and statistical foundations before moving step-by-step through practical regression analysis and deep learning architectures. Each concept is reinforced with clear explanations and clean Python code snippets to study at your own pace. Designed entirely for beginners, this course requires no prior modeling experience. A basic understanding of general math concepts is helpful but not required. Begin your journey into predictive data modeling today.