Repository containing materials and exercises for the Hands-on Biological Data Science course, offered at Ludwig Maximilian University of Munich (LMU).
- Learn to use R and RStudio for biological data analysis
- Understand core concepts in data wrangling, statistics, and genetics
- Apply your knowledge through hands-on exercises and final projects
- Build reproducible workflows and plots relevant to human genetics and life sciences
The course is structured into 6 learning modules, combining self-study, interactive sessions, assignments, and a final assignment presentation.
| Module | Content | Format | Workload (UE) |
|---|---|---|---|
| Module 1 | Course Kick-off & Setup | Introduction Session + R Installation | 1 |
| Module 2 | R Basics & Data Operations | Self-study + Exercise 1 | 5 |
| Module 3 | Statistics in R | Self-study + Quiz + Exercise 2 | 5 |
| Module 4 | Genetics & GWAS | Genetics Session + Exercise 3 | 4 |
| Module 5 | Final Assignment | Self-study | 3 |
| Module 6 | Presentations & Wrap-up | Final session | 2 |
| — | Total Workload | — | 20 UE |
Note: 1 Unterrichtseinheit (UE) ≈ 45 minutes
- Introduction session to course & tools
- Installing and configuring R and RStudio
- 📁 Materials:
- 🔍 Learning Outcomes:
- Understand course structure
- Install software and load packages
- Self-study: Core R syntax, tidyverse, data import/export, basic plotting
- Exercise 1: Tasks on data manipulation and different data types, reading, plotting
- 📁 Materials:
- 💻 Exercises:
- 🔍 Learning Outcomes:
- Learn to manipulate and visualize data
- Use tidyverse functions
- Write basic functions
- Self-study: Key statistical concepts and their implementation in R
- Exercise 2: Statistics quiz and exercise in R
- 📁 Materials:
- 💻 Exercises:
- 🔍 Learning Outcomes:
- Understand p-values, CI, normal distributions
- Perform t-tests and basic inference
- Introduscion session to complex genetics and GWAS
- Exercise 3: Interpret GWAS output
- 📁 Materials:
- 💻 Exercises:
- 🔍 Learning Outcomes:
- Understand genetic traits and SNP association
- Manipulate and visualize GWAS data
- Self-study: Analyze and interpret a GWAS dataset
- Deliverable: Create plots, answer questions, prepare for presentation
- 📁 Materials:
- 🔍 Learning Outcomes:
- Conduct end-to-end analysis
- Communicate results effectively
- Final Assignment presentations
- Wrap-up discussion & feedback
- 📁 Materials:
- 🔍 Learning Outcomes:
- Present data analysis results clearly
- Reflect on course content
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
You are free to share and adapt the material for any purpose, even commercially, as long as appropriate credit is given.