This Genomic Data Analysis course is a beginner-friendly introduction to bioinformatics and genetic data analysis. It is designed for learners who want to understand how genomic data is processed, analyzed, and interpreted using modern computational tools.

The course begins with an introduction to genomic data and single nucleotide polymorphisms (SNPs), which are the basic units of variation in genetic studies. Learners will understand how SNP data is structured and used in real biological research.

It then introduces PLINK, a widely used bioinformatics tool for handling and analyzing genotype data. Students will learn how to start working with PLINK, change genotype data formats, and perform essential data processing tasks.

A key part of the course focuses on data quality control, which ensures that genetic datasets are accurate and reliable for analysis. Learners will also explore how to work with R and RStudio for statistical computing and genomic analysis.

In addition, the course covers principal component analysis (PCA), a method used to reduce data complexity and identify patterns in genetic variation. Practical examples using SNP data help learners understand real-world applications.

By the end of this course, learners will have a strong foundation in genomic data analysis, PLINK workflows, and basic bioinformatics techniques. This course is ideal for beginners in genetics, biology, and data science.