This Genomic Data Analysis course introduces learners to the fundamentals of analyzing genetic data using modern bioinformatics tools. It is designed for beginners who want to understand how genomic datasets are processed, analyzed, and interpreted in research and healthcare contexts.

The course begins with an introduction to genomic data analysis and the concept of SNPs (single nucleotide polymorphisms), which are key genetic variations used in population genetics and disease studies. Learners gain a clear understanding of how SNP data is structured and used in real-world research.

A major section focuses on working with SNP genotype data files and using PLINK, a widely used tool in genomics. Learners are guided through setting up PLINK, converting genotype data formats, and performing essential quality control steps to ensure reliable results.

The course also introduces R and RStudio, which are essential tools for statistical computing and bioinformatics analysis. Learners apply these tools to explore genetic datasets and perform analysis workflows.

In addition, the course covers Principal Component Analysis (PCA), a key technique used to reduce data complexity and identify patterns in SNP datasets. Practical examples using PLINK demonstrate how PCA is applied in genomic research.

By completing this course, learners gain foundational skills in genomics, bioinformatics tools, and statistical genetics analysis.