Objectives and competences

Upon completing this course unit, students should be able to:

1. Recognize and formulate analytical problems in microbiological contexts in a structured manner
2. Identify appropriate tools and methods for different types of data and scientific questions
3. Critically interpret results of statistical and computational analyses in scientific literature
4. Apply basic concepts of statistics and data visualization using R
5. Familiarize themselves with machine learning approaches and their applications in microbiology
6. Develop autonomy to learn new tools and methods independently
7. Communicate data analysis results clearly and appropriately

Teaching Methodologies

General pedagogical approach:

Teaching is structured to maximize exposure to ideas and tools, with realistic expectation that students will develop fluency gradually throughout their careers.

Specific strategies:

Theoretical classes:

• Presentation of fundamental concepts and analytical frameworks
• Discussion of case studies from scientific literature
• Demonstration of different approaches to similar problems
• Expository format with discussion, without practical component in these classes

Theoretical-practical classes:

• Live demonstrations in R (live coding by the instructor)
• Simple guided exercises performed in class
• Discussion of outputs and interpretation
• Collaborative troubleshooting of common problems

Seminar:

• Preparation of critical reflections on scientific articles
• Autonomous development of project components
• Exploration of tutorials and complementary tools
• Participation in discussion forums (reading and contributions)

Synchronous tutorial guidance:

• Online sessions distributed throughout the semester
• Collective troubleshooting and guidance sessions
• Participation optional but strongly recommended

Autonomous work:

• Practical exercises
• Base scripts provided, students complete and adapt
• Application of methods demonstrated in classes
• Analysis of provided datasets with guided questions

Reading and study:

• scientific articles, tutorials, support materials

Final mini-project:

• development, analysis, report and presentation
• Critical reflections on scientific articles
• Study of concepts, additional exploration

Tools and resources:

• R and RStudio (guided installation in first class)
• Pre-processed datasets provided (no prior bioinformatics needed)
• Code templates for all exercises
• Blackboard as central platform
• Active discussion forum
• Resource library

Syllabus

Module 1

• Introduction to data science in microbiology
• Types of microbiological data
• Introduction to R and RStudio
• Data organization
• Descriptive statistics and exploratory visualization
• Special types of data

Module 2

• Formulation of testable hypotheses from microbiological data
• Common statistical tests: when and how to choose
• Microbial diversity analysis: concepts of alpha and beta diversity
• Introduction to models: correlations, simple regressions, interpretation
• Visualization of statistical results
• Critical interpretation: significance vs. biological relevance

Module 3

• Machine learning: fundamental concepts, types of problems
• Classification and prediction: examples in microbiology
• Introduction to high-throughput data analysis: metagenomes, multi-omics
• Data integration: concept and practical examples
• Limitations and common pitfalls in data analysis
• How to continue learning: resources, communities, best practices