Causal Chain Graphs in Python for Bayesian Networks

Understanding how one event leads to another is critical for modern data analysis and decision-making. Causal chain graphs within Bayesian networks allow you to map out these sequential relationships mathematically. By taking this course, you will transition from understanding basic probability to building functional causal models in Python. You will gain the confidence to structure, analyze, and interpret causal dependencies in your data. What you'll learn: - Understand the foundational concepts of probability, conditional independence, and directed acyclic graphs. - Represent sequential cause-and-effect relationships using causal chain structures. - Implement Bayesian networks in Python using modern probabilistic graphical model libraries. - Perform probabilistic inference to predict outcomes based on observed evidence. - Apply type hints and clean coding practices to ensure your causal models are robust and maintainable. The course starts with essential terminology and the mathematical basis of Bayesian networks. You will then progress through written explanations and code-based scenarios that show you how to construct, query, and validate chain graphs step-by-step. This course is designed for beginners to causal modeling, data analysts, and Python developers looking to expand their statistical toolkit. No prior experience with Bayesian networks is required. Start learning how to map and analyze causal pathways in Python today.