This tutorial provides an introduction to Bayesian networks and their applications in data analysis. Bayesian networks are graphical models that encode probabilistic relationships among variables, offering several advantages over traditional statistical methods. They can handle missing data, learn causal relationships, combine prior knowledge with data, and avoid overfitting. The tutorial covers the construction of Bayesian networks from prior knowledge, methods for learning parameters and structure, and techniques for handling incomplete data. It also discusses the relationship between Bayesian-network techniques and supervised and unsupervised learning, and includes a real-world case study to illustrate the concepts. The tutorial emphasizes the Bayesian approach to probability and statistics, explaining how it differs from classical probability and how it can be used to update beliefs based on data.This tutorial provides an introduction to Bayesian networks and their applications in data analysis. Bayesian networks are graphical models that encode probabilistic relationships among variables, offering several advantages over traditional statistical methods. They can handle missing data, learn causal relationships, combine prior knowledge with data, and avoid overfitting. The tutorial covers the construction of Bayesian networks from prior knowledge, methods for learning parameters and structure, and techniques for handling incomplete data. It also discusses the relationship between Bayesian-network techniques and supervised and unsupervised learning, and includes a real-world case study to illustrate the concepts. The tutorial emphasizes the Bayesian approach to probability and statistics, explaining how it differs from classical probability and how it can be used to update beliefs based on data.