Program
Course topics
Faculty expertise areas
proteomics, single-cell and spatial transcriptomics, pharmacology, cancer genomics, epidemiology, quantitative, statistical genetics and complex traits, ecology, population biology, and climate change, phylogenetics, evolutionary and functional genetics and genomics, gut metagenomics, computational neuroscience and biophysics.
Statistical topics
Data visualisation and the grammar of graphics
Hypothesis testing
Regression analysis
Principal component analysis and other (non-linear) low-dimensional embeddings
Clustering
Classification
Introduction to machine learning
Computational topics
Basics of programming in R and Python: data structures and functions
Notebooks (quarto)
Principles of tidy data
Tidyverse for R
Pandas/Seaborn for Python
Bioconductor for biological sequence data and matrix-like omics data
Open science practices and working with GitHub
Biological topics
Biological sequence analysis
Read alignments, variant calling
Single-cell analysis
Proteomics data analysis
Working with image data
