Bayesian Networks with Simulated Data in Python

Understanding how variables influence one another is a cornerstone of modern decision science and machine learning. This text-based course guides you through the foundational concepts of probabilistic graphical models, showing you how to represent complex real-world dependencies using Bayesian networks. You will learn to construct these networks from scratch, simulate realistic datasets to test your assumptions, and run inference queries to solve practical decision-making problems. By reading through clear explanations and structured code walk-throughs, you will transition from understanding basic probability theory to building functional causal models in Python. You will study how to define conditional probability distributions, structure directed acyclic graphs, and validate your models using modern Python packaging and clean coding standards. What you'll learn: - Understand the core mathematical principles of Bayesian networks and conditional independence - Design directed acyclic graphs to represent real-world causal relationships - Generate high-quality simulated data to test and validate your network structures - Query trained Bayesian networks to calculate posterior probabilities for decision-making - Implement clean Python code using modern type hints and structured programming practices - Evaluate model performance and adjust network parameters based on simulated outcomes This course begins with essential terminology, probability basics, and structural definitions before moving into step-by-step implementation. You will explore how to write, run, and refine your network queries entirely through written explanations and code examples. This course is designed for beginner data analysts, programmers, and aspiring data scientists who want to explore probabilistic modeling. No prior experience with Bayesian statistics is required, though a basic familiarity with Python variables and loops is helpful. Start reading today to master the fundamentals of causal modeling with Python.