12 Python labs designed · 600+ students mentored · 55 teams guided
Supervised and unsupervised statistical learning, with evaluation through train/test error and cross-validation. Topics span regression and classification, logistic models, dimensionality reduction, gradient-based optimization, trees and forests, clustering, and neighbour-based prediction, supported by computational practice.
Calendar summary · Catalogue & source ↓
How visual and interactive displays help people reason about data. Design and algorithms draw on perception, cognition, graphic design, and human–computer interaction, with attention to communication and ethics. Practical work includes critique, implementation, and evaluation of visualizations.
Calendar summary · Catalogue & source ↓
Foundations of inference, including model parameters, estimators and their properties, estimation approaches, interval estimates, testing, likelihood, and linear models. Computational work applies these ideas to datasets and simulation.
Calendar summary · Catalogue & source ↓
How visual and interactive displays help people reason about data. Design and algorithms draw on perception, cognition, graphic design, and human–computer interaction, with attention to communication and ethics. Practical work includes critique, implementation, and evaluation of visualizations.
Calendar summary · Catalogue & source ↓
For Data Science Specialist students: formulate questions about complex datasets and investigate them with exploratory analysis, visualization, statistics, and machine learning. Case studies and cross-disciplinary collaboration develop reproducible software workflows, ethical practice, teamwork, and communication for different technical audiences.
Calendar summary · Catalogue & source ↓
Survey design and bias, including randomized responses; sampling approaches using strata, clusters, and unequal selection probabilities; inference for population quantities and ratio estimates. Observational studies examine missingness, bias, and the distinction between association and causation.
Calendar summary · Catalogue & source ↓
Five courses, one reference. Summaries of the official Faculty of Arts & Science calendar descriptions.
Appointments: Winter 2026
For Data Science Specialist students: formulate questions about complex datasets and investigate them with exploratory analysis, visualization, statistics, and machine learning. Case studies and cross-disciplinary collaboration develop reproducible software workflows, ethical practice, teamwork, and communication for different technical audiences.
Source: A&S Calendar ↗ (opens in new tab) · checked 2026-10-20
Appointments: Summer 2026
Foundations of inference, including model parameters, estimators and their properties, estimation approaches, interval estimates, testing, likelihood, and linear models. Computational work applies these ideas to datasets and simulation.
Source: A&S Calendar ↗ (opens in new tab) · checked 2026-10-20
Appointments: Fall 2025
Survey design and bias, including randomized responses; sampling approaches using strata, clusters, and unequal selection probabilities; inference for population quantities and ratio estimates. Observational studies examine missingness, bias, and the distinction between association and causation.
Source: A&S Calendar ↗ (opens in new tab) · checked 2026-10-20
Appointments: Fall 2026 · Winter 2026
How visual and interactive displays help people reason about data. Design and algorithms draw on perception, cognition, graphic design, and human–computer interaction, with attention to communication and ethics. Practical work includes critique, implementation, and evaluation of visualizations.
Source: A&S Calendar ↗ (opens in new tab) · checked 2026-10-20
Appointments: Winter 2027 (upcoming)
Supervised and unsupervised statistical learning, with evaluation through train/test error and cross-validation. Topics span regression and classification, logistic models, dimensionality reduction, gradient-based optimization, trees and forests, clustering, and neighbour-based prediction, supported by computational practice.
Source: A&S Calendar ↗ (opens in new tab) · checked 2026-10-20