Winter 2022

Introductory Computation and Data Science for the Social Sciences (GGR274)/Introductory Computation and Data Science for the Social Sciences (EEB125)

Previous Courses

An Introduction to Statistical Reasoning and Data Science - STA130

Data Science I - JSC270

Data Science II - JSC370

Design Of Scientific Studies - STA305

Data Science Methods, Collaboration, and Communication - STA2453

Introduction to Statistical Methods and Ideas - STA220

Short Courses and Workshops

Virtual Workshop on Analysis of Unstructured Text Data SSC - Data Science and Analytics Section - Spring 2020. Developed jointly with Dave Campbell.

DHSI Summer Institute: 2017 and 2018. Introduction to Machine Learning in the Digital Humanities. Developed jointly with Paul Barrett.

Data Science Contests

I have been involved in developing and organizing several data science contests, including ASA DataFest@UofT, and DataFest: COVID-19 Virtual Data Challenge.

During the 2019 Statistical Society of Canada Annual Meeting in Calgary, Alberta, I gave a presentation on ASA DataFest and Beyond (slides, handout on hosting a data science contest.) These resources may be helpful if you are thinking about organizing a DataFest contest at a university.

Experiential Learning

A Data Science contest is one way students are able to gain practical data science experience during university. This paper I presented at the 10th International Conference on Teaching Statistics (ICOTS10) outlines other possibilities.

My Teaching Philosophy in a Nutshell

I try to convey my enthusiasm for statistics and data science, so students can develop and build their interest in the subject. If I’m not excited by the material, then it’s unlikely that students will become engaged. So, I always try to teach a class that I would like to attend as a student.

Teaching a class that is engaging, yet leads to a deeper understanding of statistics, requires presenting material that involves interesting stories and meaningful applications. I often try to incorporate my experiences as a consultant statistician from areas such as medical research and international law. It’s really important for students to experience practical aspects of statistics such as: data collection, data wrangling or evaluation of the validity of a conclusion based on a statistical analysis.

Striking a balance between the underlying mathematical concepts and the big picture so that students learning difficult statistical ideas remain engaged is crucial. Jordan Ellenberg, in How Not To Be Wrong, decries the myth that, only “mathematical geniuses” are capable of sound statistical reasoning. This myth not only discourages students from pursuing careers in the field, but future writers, doctors, high school teachers, and politicians from majoring in statistics or data science.