# Welcome

## Course description

The objective of this course is to show students how statistics is used in practice to answer a specific question, by introducing a series of important model-based approaches.

The students will learn to select and use appropriate statistical methodologies and acquire solid and practical skills by working-out examples on real-world data sets from various areas including medicine, genomics, ecology, and others.

All analyses will be conducted mainly with the R software, possibly with interfacing to Python. No strong knowledge neither of R nor Python programming is required (only basic scripting).

## Schedule (tentative)

**Teachers** : Julien Chiquet (lecture + 1 PC), Geneviève Robin (2 PC)

**Course Evaluation**: 2 individual homework assignments + a final exam/project

**Course Language**: French with all material in English

Statistical tests (x2)

- Two-populations comparison
- Power analysis
- Multiple Testing

Regression models (x2)

- Linear and Non Linear Regression models
- Nonlinear regression models
- Inference Diagnostic, Model comparison

Mixed effects models (x2)

- Linear mixed effects models
- Nonlinear mixed effects models

Mixture models and model-based clustering (x3)

- Gaussian mixture models for data clustering
- Hierarchical and Spectral clustering for Graph
- Stochastic Block Models
- (Variational) EM algorithm