All courses take the form of tutorials. Statistical methods and concepts are motivated by illustrative situations in a large scope of applications in life sciences. Basic functions in R are introduced to implement those methods.

First steps in data analysis with R

**Short description**: The course introduces the basics
of inferential statistics. It essentially provides tools to answer the
question “Is there an effect of *this* on *that*?” using
data. The scope of statistical methods in the course is limited to
common variants of the former question: “Are there differences across
group means of *that*?” and “Is there a linear relationship
between *this* and *that*?”.

**Keywords**: Hypothesis testing, Estimation, One-way
Analysis of variance, F-test, t-test, Simple linear regression model

Statistics for decision making

**Short description**: In many situations, especially in
biological applications, data analysis aims at understanding why some
profiles have a higher probability than others to belong to a class of
interest. Typically, which clinical profiles are more likely to be
affected by a disease? The course introduces statistical models to
answer such questions and the following sub-questions: Does a covariate
significantly explain variations between class probabilities? How to
select a minimal and sufficient subset of covariates explaining the
variations between class probabilities? How to use a profile of
covariates to predict the membership to a given class?

**Keywords**: Logistic linear regression model, Analysis
of deviance testing, AIC, BIC, Classification errors.

**Short description**: In molecular biology, genomics
aims at understanding how the genome works using whole-genome
measurements obtained by high-throughput biotechnology. Analyzing those
massive data raises many specific statistical issues. The most common of
those issues are addressed in this introductory course.

High-dimensional regression and classification modeling for biological data analysis

**Short description**: High-throughput technology
(imaging, spectroscopy, etc.) is now currently used for many biological
applications. Using high-dimensional data generated by those technology
to make prediction rules require ad-hoc methods. The most commonly used
of these methods are introduced in the course.

Introduction to geostatistics.

**Short description**: The course introduces the
prediction issue with geolocalized data (kriging) using a spatial
stationary dependence model (variogram modeling). Slides, R script, Data and Handbook.

Introduction to biomathematics.

**Short description**: The purpose of the course is to
introduce basic mathematical concepts motivated by biological modeling
applications. Functional
analysis slides, Enzyme
kinetics.