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 I | Minitab/R |
| STAT 270 | Introduction to probability & statistics | R |
| STAT 315 | Applied Regression Analysis | R |
| STAT 402 | Applied GLMs & survival analysis | R |
How I teach it
Approach
John Tukey said that the best part of being a statistician is that you get to “play in everyone’s backyard.” That is what drew me to the subject as an undergraduate, and it is what I try to pass on.
Service teaching carries a particular challenge. Many students arrive because a statistics course is required for their program, expecting something abstract and intimidating. I treat that as the opportunity it is: these are exactly the people who will go on to use statistics in a lab, a clinic, or a business, and who benefit most from seeing it work. New concepts get introduced through real applications — drawn from the news, from published research, or from studies the students themselves propose to answer questions they care about. The aim is that they leave looking at everyday claims with a statistical eye.
Classes are built around participation rather than transcription. Students work through problems collectively, applying what they have just learned, which builds the judgment that carrying a method from a textbook into a real setting requires. A well-chosen graphic, quotation, or cartoon can open a genuine conversation about a statistical idea, and I use them freely; a class people enjoy is a class they remember.
Computing is woven through the courses rather than bolted on at the end. Students work in R — in RStudio and Jupyter notebooks — with real data and reproducible workflows, so they leave with a habit that transfers to a thesis, a lab, or a job rather than a set of formulas. Course materials and solution keys are written in LaTeX for clarity.
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)