<p>Bayesian statistics is an effective tool for solving some inference problems when the available sample is too small for more complex statistical analysis to be applied. This course teaches the main concepts of Bayesian data analysis. It focuses on how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, model checking, and validation.</p> <p>The course introduces the framework of Bayesian Analysis. Complex mathematical theory will be sidestepped in favor of a more pragmatic approach, featuring computational methods implemented in the Python library PyMC3. We present several instances of analysis scenarios.</p> <p>All the codes of the course are uploaded on the Github repository:&nbsp;<a href="https://github.com/PacktPublishing/-Introduction-to-Bayesian-Analysis-in-Python" target="_blank">https://github.com/PacktPublishing/-Introduction-to-Bayesian-Analysis-in-Python</a></p> <h1>Style and Approach</h1> <p>The user is expected to know basic Python programming. Knowledge of scientific Python packages such as NumPy, SciPy, Matplotlib, Seaborn, and Pandas is a plus but not mandatory. However, a basic understanding of probability is necessary to grasp the fundamentals of the Bayesian framework. Familiarity with the notion of random variables and distributions will enable you to follow along with the course.</p>