This course blends biology, statistics, and programming to equip learners with skills in genome-wide data analysis — helping decode the genetic blueprint at scale.
You’ll start with foundational biology and progress into bioinformatics algorithms, data wrangling, and statistical analysis using Python and R. The course covers genome annotation, pathway analysis, machine learning in genomics, and visualization using tools like Bioconductor, Seaborn, and ggplot2.
Learn to build and automate analysis pipelines, integrate multi-omics datasets, and derive actionable insights for research, diagnostics, and precision medicine. Perfect for bioinformaticians, data scientists, or life science graduates transitioning into computational biology.
Some basics help, but we cover essential Python and R in context throughout the course.
Yes. Projects are based on real human, microbial, and plant genome datasets.
Absolutely. These are in-demand roles across R&D, clinical genomics, and biotech companies.
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