Bayesian data analysis has gained significant popularity as a statistical inference method, offering a more flexible approach compared to traditional methods reliant on fixed constants like confidence levels or p-values. This course aims to provide a comprehensive understanding of Bayesian data analysis, highlighting its distinctions from the classical approach and emphasizing its indispensability in the data science field. Throughout the course, you will delve into various topics, including A/B testing, decision analysis, and linear regression modeling, all approached from a Bayesian perspective. By analyzing real-world datasets related to advertising, sales, and bike rentals, you will gain practical experience in applying Bayesian techniques to extract meaningful insights. To facilitate your learning journey, this course will also introduce you to the PyMC3 library. By leveraging this powerful tool, you will be able to efficiently design, fit, and interpret Bayesian models, enhancing your ability to effectively analyze data. By the end of this course, you will have acquired a solid foundation in Bayesian data analysis, enabling you to confidently incorporate this methodology into your data science toolkit.