Chapter 3 How will this book teach computational biology?
“The rise of computer programming, computational power, and modern statistical approaches may…” allow “…scientists to ask new questions and to extract more information from data than ever before.” (Touchon & McCoy 2016, Ecosphere)
3.1 What you’ll learn
3.1.1 General skills that you’ll learn
- Statistical computing using R, RStudio, and rmarkdown
- Data analysis, from t-tests to mixed models in R
- Data visualization, with an emphasis on ggplot2
- Data science, from data management best practices to data cleaning with dplyr
- Computational reproducibility, from formatting scripts to using rmarkdown to write reproducible reports
3.1.2 Computational biology skills you’ll learn
- Working with sequence data
- alignments
- Phylogenetics
- …
3.2 Teaching approach
- Always explore and visualize data
- Step-by-step instructions
- Frequently refreshing and review
- Comprehensive and self-contained
- Worked example
- “Modify this code” tasks
- Code completion tasks (key steps missing)
- Broken code tasks (fix non-functional code)
- Links to Jupyter notebooks (not yet implemented)
- “Active Learning Notebooks” - all content and code in .Rmd format
3.3 Requirements
- R
- RStudio
- External packages loaded via RStudio