Statistical Inference and Modeling for High-Throughput Data

High-throughput experiments generate massive amounts of data, but extracting meaningful biological insights requires robust statistical methods. This text-based course guides you through the foundational concepts of statistical inference, helping you separate true signals from noise in high-dimensional datasets. By completing this course, you will transition from understanding basic statistical terms to confidently modeling complex high-throughput data, applying false discovery rate controls, and interpreting genomic experimental results. What you'll learn: - Understand foundational statistical concepts, including hypothesis testing, p-values, and statistical power. - Analyze high-throughput datasets using modern modeling techniques and normalization strategies. - Apply false discovery rate (FDR) control methods to manage multiple testing challenges. - Model high-dimensional data using linear models and empirical Bayes approaches. - Evaluate data quality and identify batch effects in large-scale biological experiments. - Practice interpreting statistical outputs from genomic and high-throughput workflows. The curriculum begins with essential terminology and statistical basics before progressing to hands-on modeling scenarios and modern error-correction techniques. You will read clear explanations and work through conceptual exercises designed to build your analytical confidence. This course is designed for beginners, researchers, and data analysts who want to understand the math and logic behind high-throughput data analysis, with no prior advanced statistics background required. Start reading today to master the statistical foundations of modern high-throughput science.