Bayesian Statistics for Engineers: Genomics and Bioinformatics

Bayesian statistics provides a powerful framework for making sense of complex, high-dimensional datasets in modern bioinformatics and genomics. This course introduces software engineers and data practitioners to essential probabilistic modeling techniques used to extract insights from genetic information. What you'll learn: Understand core terminology, conditional probability, and Bayes' theorem fundamentals; Explore probabilistic models used to identify key genes and disease markers in biological data; Apply Markov Chain Monte Carlo sampling methods to reason about uncertainty; Implement Bayesian regression models tailored for genomic analysis; Utilize modern probabilistic programming tools in Python to construct reproducible statistical pipelines. The written lessons begin with foundational concepts and probability theory before guiding you through real-world computational workflows. This course is created for engineers and analytical professionals new to Bayesian statistics, requiring no previous background in biology or genomics. Start reading today to master statistical tools for computational biology.