This course introduces students to the fundamentals of big data analysis in pharmacology, with a strong focus on single-cell RNA sequencing (scRNA-Seq). Students will learn to work with large biological datasets, process and analyze them using R and the Seurat package, and explore real-world applications in drug discovery and biomedical research.

Through hands-on coding exercises and biological case studies, students will gain practical skills in data manipulation, visualization, dimensionality reduction, clustering, and differential expression analysis. The course also covers batch effect correction, drug response studies, and the integration of multi-omics data, providing a comprehensive foundation for applying big data techniques to pharmacology and biotechnology.

No prior coding experience is required, making this course ideal for biology and pharmacology students looking to develop computational skills for modern biomedical research.