Statistical Genomics for Data Science

Genomic data is expanding at an unprecedented rate, but raw sequence data is only valuable if you can extract meaningful biological insights from it. Understanding the statistical foundations behind genomic data science is the key to turning massive datasets into breakthrough discoveries. This text-based course guides you through the essential statistical concepts and computational workflows used to analyze genomic data. You will start with foundational biology and probability concepts before progressing to advanced topics like hypothesis testing for differential expression and modern high-dimensional data analysis. By the end of this course, you will confidently interpret genomic datasets and understand the mathematical models that power modern bioinformatics. What you'll learn: - Understand the core biological and statistical terminology essential for genomic data analysis. - Apply hypothesis testing and multiple testing corrections to identify significant genetic variations. - Analyze differential gene expression using statistical models tailored for high-throughput sequencing. - Explore modern dimension reduction techniques like PCA to visualize and cluster high-dimensional genomic datasets. - Implement reproducible statistical workflows using modern data frames and standard genomic libraries. The journey begins with basic probability and genomics terminology, building up to regression models, normalization techniques, and multiple testing adjustments. You will read through clear explanations, study structured code snippets, and complete written exercises designed to reinforce your analytical skills. This course is designed for beginners in bioinformatics, data analysts transitioning to life sciences, and students wanting to understand the math behind genomic tools. No prior advanced statistics or biology background is required. Start exploring the statistical patterns hidden within the genome today.