Teaching
Teaching philosophy
I approach teaching mathematics, statistics, and data science as a process of understanding and exploration.
Learning is based on three principles:
- Learning by doing: practice and hands-on use of tools like R or Quarto help students build meaning.
- Learning through collaboration: team projects and discussion are central in my courses.
- Transparent learning: every activity is documented and reproducible, embedding scientific rigor in student work.
I rely on project-based learning, formative assessment, and a supportive environment where rigor and curiosity can coexist.
Courses taught
Université Laval — Department of Mathematics and Statistics
STT-1100 — Introduction to Data Science
This introductory course focuses on professional situations where students assume a data science role.
Learning goals: master the full analysis pipeline, from data import to communication of results.
Tools: R, tidyverse, Quarto, GitHub.
Course notes: STT-1100_notes_de_cours
Philosophy: active learning, francophone case studies, ethical reflection on data.
STT-1100 GPT: course-specific conversational assistant, to support student learning.
STT-4230 — R for scientists
Course focused on good development practices: project structure, documentation, and collaboration through GitHub.
Tools: R, Quarto, GitHub, testthat.
Resource: Course notes from Sophie Baillargeon.
Philosophy: clarity, reproducibility, and integration of modern tools.
STT-4230 GPT: course-specific conversational assistant, to support student learning.
UlavalSSD: UlavalSSD R package, featuring practical utilities for data science at Université Laval.
STT-2200 — Data Analysis
An applied statistics course where students work from real datasets and produce reproducible reports.
Tools: R, ggplot2, dplyr, RMarkdown.
Philosophy: interpretation before automation, clear communication of results.
Université du Québec à Chicoutimi (UQAC)
8INF404 — Introduction to Data Science and Business Intelligence
An interdisciplinary course introducing the full data science project cycle: preparation, exploration, and predictive modeling.
Tools: R, shiny, Quarto.
Philosophy: connect technical work to decision-making, with an emphasis on communication.
8INF416 — Visualization and Interface
Course devoted to interactive visualization and scientific communication.
Tools: R (Shiny), ggplot2.
Philosophy: visual clarity, accessibility, and rigor in graphical messaging.
8STT108 — Statistical Methods for Big Data
Introduction to statistical methods applied to large datasets (big data).
Tools: R, tidyverse, parallel computing.
Philosophy: emphasize model logic before automation.