teaching
Courses taught at UBC, primary instructor across Master of Data Science, statistics, and science programs (2024-2026), reaching 550+ graduate and undergraduate students.
Two academic years as Postdoctoral Teaching and Learning Fellow at UBC (2024-2026). The fellowship followed on the MDS Teaching Assistant Award I received as a TA in the same program (2021/22). Along the way I developed curriculum across the MDS program: PrairieLearn assessment and rubric design, Quarto-based course websites, and GenAI-resilient assessment design. For capstone supervision and mentoring, see the CV.
2024-2026
DSCI 591: Capstone Project
Industry-partnered capstone supervision and evaluation, 8-10 week applied data science projects with external organizations.
DSCI 551: Descriptive Statistics and Probability for Data Science
Probability foundations and descriptive statistics, distributions, estimation, and simulation-based reasoning.
DSCI 542: Communication and Argumentation
Technical communication for data scientists, evidence-based argumentation, audience-aware writing, and presentation.
DSCI 531: Data Visualization I
Principled data visualization with Altair and ggplot2, grammar of graphics, perception, and critical assessment of AI-assisted visualization.
DSCI 521: Data Science, Tooling
Computing platforms and workflows for data science, shells, version control, reproducible environments, and collaborative development.
DSCI 513: Databases and SQL for Data Science
Relational databases, SQL querying, data modeling, and retrieval pipelines for data science practice.
STAT 302: Introduction to Probability
Probability theory for statistics students, random variables, distributions, expectation, limit theorems.
SCIE 113 & SCIE 300: Science Communication
Communication of scientific ideas across audiences, first-year seminar (SCIE 113) and third-year science communication (SCIE 300).
DSCI 100: Statistics and Data Science
Introduction to data science and statistical reasoning, reproducible workflows, exploratory data analysis, and introductory machine learning.