For an up to date schedule of UBDS^3 2026 please use this link.
During week 1, students are introduced to various examples of biological data and the concepts behind their analysis. Afternoons are dedicated to practicing biological data analysis skills. Students with no programming experience can join structured courses to learn programming in R or Python from scratch. Students with prior programming experience can deepen their knowledge of data analysis through statistical and machine learning workshops offered by our faculty members. The workshops assume that you are comfortable working in either R or Python, so that you can focus on the course material rather than basic programming skills. Each day, at least two workshops will be offered, allowing you to choose the topic that interests you most.
Week 2 is dedicated to the work on mini research projects in groups of 4-7 people lead by out faculty in their respective research areas.
Importing data in R, data types and data wrangling
Visualization, grammar of graphics, ggplot2
PCA
Regression
Testing (ANOVA, t-test)
Clustering
Tidy data
R-Python interoperability
Basic python toolkit: pandas, numpy, matplotlib, seaborn
Data manipulation and preprocessing (Dealing with outliers, encoding features, scaling data)
Linear and non-linear models, Regression and classification
Metrics
Advanced ML and DNN (keras, torch, tf, building NN)
TBA, see the faculty expertise areas