Teaching

Teaching philosophy

I approach mathematics, statistics, and data science teaching as an activity of understanding, practice, and communication. Courses are designed to help students produce reproducible analyses, justify their choices, and explain their results clearly.

Active learning

Classes emphasize hands-on work, applied situations, and projects where statistical concepts are used in concrete contexts.

Reproducibility

Activities use R, Quarto, GitHub, and explicit project structures so that reasoning, code, and results can be checked.

Support

Exercises, formative feedback, course assistants, and CDA resources support autonomy while preserving the central role of student understanding.

Courses taught

Université Laval

Data science

STT-1100 - Introduction to Data Science

Introductory course centered on professional situations where students take on a data science role. The course covers the analysis cycle, from data import to communication of results.

Tools: R, tidyverse, Quarto, GitHub

Approach: active learning, francophone case studies, ethical reflection on data

Scientific programming

STT-4230 - R for Scientists

Course focused on development practices: project structure, documentation, tests, collaboration, and publication with GitHub.

Tools: R, Quarto, GitHub, testthat

Approach: clarity, reproducibility, integration of modern tools

Applied statistics

STT-2200 - Data Analysis

Applied statistics course where students work from real datasets and produce reproducible reports.

Tools: R, ggplot2, dplyr, R Markdown

Approach: interpretation before automation, clear communication of results

Université du Québec à Chicoutimi

8INF404 - Introduction to Data Science and Business Intelligence

Interdisciplinary course presenting the full data science project cycle: preparation, exploration, predictive modeling, and communication.

8INF416 - Visualization and Interface

Course devoted to interactive visualization and scientific communication with R, Shiny, and ggplot2.

8STT108 - Statistical Methods for Big Data

Introduction to statistical methods for large datasets, with emphasis on model logic before automation.

Teaching recognition

I received the Star Teacher Award again in 2026. This recognition adds to the 2014, 2023, 2024, and 2025 editions listed in my CV. The Faculty of Science and Engineering describes this title as a recognition given to instructors who receive excellent student evaluations.

FSG Teaching Stars ceremony

Shared resources

Teaching portfolio

Overview of my teaching approach, activities, and achievements.

View portfolio

Teaching resources platform

Quarto templates, interactive tutorials, and reproducible examples for data science teaching.

site_ressources_SSD

GPT-CDA project

Conversational assistant supporting student success in mathematics and statistics, featured in ULaval News on May 22, 2026.

Project page ULaval News article

Teaching objective: train autonomous and rigorous data scientists who can connect theory, practice, and scientific communication.