Since the widespread availability of generative artificial intelligence (GenAI), particularly Large Language Models (LLMs), fundamental questions have emerged about the future of coding in data science. Some predict that data scientists will no longer need traditional coding skills, while others question whether LLMs might replace data scientists entirely. However, these discussions have largely proceeded without empirical evidence of how practicing data scientists actually use these tools.

This study addresses this gap by surveying trained, practicing data scientists to understand if and how they integrate LLMs into their workflows, particularly for writing and editing code and performing other data science tasks. Building on our recent investigation of data science educators' perspectives on LLMs, this research examines real-world usage patterns among practitioners to bridge the gap between current practice and educational preparation.

Our findings will contribute to the data science community in two critical ways. First, by documenting how data scientists are actually working with LLMs four years after their initial release, we provide actionable insights that allow practitioners to learn and adopt effective strategies for integrating these tools into their work. Second, we inform data science education by evaluating whether current pedagogies adequately prepare students for this evolving landscape.

This research will help answer key pedagogical questions: Should coding education emphasize code reading, tracing, and editing over writing large amounts of de novo code? Should greater focus be placed on writing high-quality documentation and specifications, given their value as prompt context for LLMs? Should testing receive increased emphasis to enable verification of LLM-generated code? By grounding these questions in empirical evidence of practitioner behaviour, we aim to provide data-driven guidance for evolving data science curricula.

To join this seminar virtually, please request Zoom connection details from hr.ops@stat.ubc.ca.

This talk is one of the Teaching and Learning in Statistics and Data Science Seminar Series. 

The Computer-Based Testing Facility (CBTF) aims to solve a key problem in teaching and learning: helping instructors run digital assessments at scale securely and equitably. The easiest way to describe the CBTF is to imagine it as a network-filtered computer lab dedicated to running digital assessments in 50-minute increments throughout the day, invigilated by trained proctors. Students are typically given a multi-day window to write their tests at a time and location convenient for them. With network filtering, centralized invigilation, a distributed exam model, and flexibility for students and instructors, the mission of the CBTF is to spur pedagogical innovation at the university in a broad range of classes, programs, and departments. Though not the initial motivation, recently the CBTF has also been used to maintain exam integrity in the face of modern AI tools, particularly for computer-based exams where any element of programming is needed under controlled environments. This session will be useful for a range of people including faculty members teaching courses, administrators, IT staff, grad students as well as anyone with an interest in pedagogy and innovative teaching methods. We’ll also hear about the experience of several Statistics faculty members in using the CBTF in their courses as pilots in previous terms. There will be plenty of time for Q&A and a larger conversation around migration to computer-based testing and different learning technologies. The CBTF currently supports a variety of assessment options including Canvas, PrairieLearn, MTA and others. We will also discuss the advantages and disadvantages of different learning technologies in the CBTF from a pedagogical, logistical, and financial perspective.

To join this seminar virtually, please request Zoom connection details from hr.ops@stat.ubc.ca. 

Building data literacy requires intentional design—both inside and outside the classroom. As statistics educators, we are uniquely positioned to help learners not only analyze data but also communicate, question, and connect with it in meaningful ways. This talk will explore several initiatives that promote inclusive and engaging approaches to developing data literacy across different educational levels.

First, I will discuss a range of outreach activities designed to introduce statistical thinking to elementary and secondary students through play, storytelling, and authentic data contexts. These activities—ranging from constructing visualizations using the Spotify API to exploring sampling methods with geodes and “dinosaur fossils”—have been implemented at events such as Florence Nightingale Day and Pursue STEM. Such initiatives align with calls to cultivate early data literacy and “real-world statistical reasoning” among pre-tertiary learners (Ben-Zvi & Garfield, 2004; Ridgway, 2016).

Second, I will highlight innovations in postsecondary statistics education, focusing on the integration of Universal Design for Learning (UDL) principles (CAST, 2018) in a large third-year course (STA304: Surveys, Sampling, and Observational Data). Through flexible grading, grace period, and generative AI policies, the course design supports diverse learners while maintaining academic rigor. Student feedback illustrates how flexibility can enhance motivation, equity, and engagement—findings that echo recent work on inclusive assessment and learning autonomy in statistics education (Engel, 2017).

Together, these projects demonstrate how flexibility, communication, and creativity can support inclusive data literacy education across age groups. By integrating outreach and UDL-informed teaching, we can expand access to data-driven inquiry and foster a more diverse and data-confident generation of learners.

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca. 

Most universities offer centralized teaching development programs for Teaching Assistants (TAs), but discipline-specific initiatives – particularly in statistics and actuarial science – are often limited or informal. In response to this gap, the Department of Statistics and Actuarial Science at the University of Waterloo launched a comprehensive TA Program in 2023. This initiative encompasses all aspects of graduate teaching assistantships and includes the Foundations for University Teaching in Statistics and Actuarial Science certificate training program.

Developed in collaboration with the university’s Centre for Teaching Excellence, our program provides structured, sequential training tailored to the unique demands of statistics and actuarial science courses. It equips incoming and current graduate TAs with the skills needed to confidently and effectively fulfill their roles, including proctoring, grading, facilitating tutorials, and preparing and delivering lecture content.

In this talk, we will outline the state of our TA training prior to 2023, share the motivations behind the creation of our program, and describe its current structure. We will present data on TA participation, share feedback from past trainees, and discuss future directions for the program, including its potential adaptation by other departments and institutions.

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca. 

To join this seminar virtually: Please request Zoom connection details from ea@stat.ubc.ca.

Abstract:  Hidden Markov models (HMMs) are commonly used to identify latent processes from observed time series, but it is challenging to fit them to large and complex time series collected by modern sensors. Using data from threatened resident killer whales (Orcinus orca) off the western coast of Canada as a case study, we provide solutions to three common challenges faced when identifying latent behaviour from complicated biologging data. First, biologging time series often violate common assumptions of HMMs when collected at high frequencies. We thus propose a hierarchical approach which utilizes moving-window Fourier analysis to capture fine-scale dependence structures. Second, modern technology allows researchers to directly label the latent process of interest, but rare labels can have a negligible influence on parameter estimates. We introduce a weighted likelihood approach that increases the relative influence of labelled observations. Third, applying HMMs to large time series is computationally demanding, so we propose a novel EM algorithm that combines a partial E step with variance-reduced stochastic optimization within the M step. These solutions allow researchers to model biologging data with HMMs that are more interpretable, accurate, and efficient to fit than existing methods.