Data Analysis for Genomics course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Foundations of Genomic Data Science
Estimated time: 16 hours
- Understand DNA sequencing technologies and high-throughput data generation
- Learn basic R programming for genomic data analysis
- Explore data wrangling and visualization techniques
- Develop statistical reasoning for biological datasets
Module 2: Statistical Analysis of Genomic Data
Estimated time: 16 hours
- Study hypothesis testing in genomics contexts
- Analyze differential gene expression using statistical methods
- Understand multiple testing correction techniques
- Interpret biological significance of statistical results
Module 3: RNA-seq and High-Throughput Data
Estimated time: 16 hours
- Explore RNA sequencing workflows and data structures
- Process and normalize RNA-seq datasets
- Apply regression models to gene expression data
- Visualize complex gene expression patterns
Module 4: Reproducible Research Practices
Estimated time: 12 hours
- Learn best practices for reproducible research
- Document analytical workflows using R Markdown
- Share and version control genomic analyses
Module 5: Genomic Data Interpretation
Estimated time: 12 hours
- Integrate statistical findings with biological knowledge
- Evaluate functional significance of genomic results
- Use public databases for annotation and enrichment
Module 6: Final Project
Estimated time: 20 hours
- Analyze a real-world RNA-seq dataset from start to finish
- Produce a reproducible report with visualizations and conclusions
- Demonstrate mastery of genomic data analysis workflow
Prerequisites
- Basic understanding of biology and genetics
- Familiarity with R programming
- Introductory knowledge of statistics
What You'll Be Able to Do After
- Analyze high-throughput genomic datasets using R
- Perform differential gene expression analysis
- Apply statistical inference to biological data
- Implement reproducible research practices in bioinformatics
- Interpret and report genomic findings in context