Teaching
Applied statistics and statistical computing — mostly for students who’ll use it in another field.
I teach across the introductory-to-advanced range at the University of the Fraser Valley, and have taught at Langara College and the University of British Columbia. Much of it is service teaching — statistics for students in the sciences, health sciences, business, and social sciences — where the goal is for students to run and interpret their own analyses, not just pass an exam.
At UFV
Courses
| Course | Topic | Software |
|---|---|---|
| STAT 104 | Introductory statistics | Minitab |
| STAT 106 | Statistics for business & social science | Minitab/R |
| STAT 270 | Introduction to probability & statistics | R |
| STAT 315 | Intermediate statistics | R |
| STAT 402 | Applied GLMs & survival analysis | R |
How I teach it
Approach
I lean on real data and reproducible workflows — R in Jupyter notebooks and RStudio — so students leave with a habit they can carry into a thesis, a lab, or a job, not just a set of formulas. Course materials and solution keys are written in LaTeX for clarity, and computing is woven through the course rather than bolted on at the end.
Professional development
Keeping the toolkit current
Statistical practice moves, and I train to stay with it. Recent professional development includes workshops and courses in:
- Analyzing Large Datasets with the Julia Language
- Version Control with Git and GitHub
- Modular Design and Automated Testing with R
- Introduction to Big Data & Machine Learning for Survey Researchers & Social Scientists
- Advanced Visualization in R: R Shiny
- Introduction to Effective Visualization
- Introduction to Discrete Choice Modeling in R
- Introduction to Statistical Machine Learning in R
- Data Science and Analytics: Introduction to Data Cleaning, Querying, and Modelling at Scale
- Open Source Solutions for Online Teaching, Assessment and Grading
- Exploratory Data Analysis using R Markdown
- Instructional Skills Workshop (ISW)