DS 1000 (Data Science Concepts)
Students will learn how to visualize and analyze continuous and categorical data from various domains, using modern data science tools. Concepts of distributions, sampling, estimation, confidence intervals, experimental design, inference, correlation will be introduced in a practical, data-driven way.
STATS 1023B (Statistical Concepts) / STATS 2037B (Statistics for Health)
An examination of statistical issues aiming towards statistical literacy and appropriate interpretation of statistical information. Common misconceptions will be targeted. Assessment of the validity and treatment of results in popular and scientific media. Conceptual consideration of study design, numerical and graphical data summaries, probability, sampling
variability, confidence intervals and hypothesis tests.
STATS 3860B Generalized Linear Models / STATS 9155B Statistical Modelling II
In this course we use the R statistical software package to study both applied and theoretical aspects of different extensions to the linear regression model framework. Course topics include: logistic regression, Poisson log-linear models, contingency tables, multinomial regression, mixed effect models, and nonparametric regression. Additional topics may include generalized additive models, trees and neural networks as time allows.
STATS 4850G / 9850G Advanced Data Analysis
Modern methods of data analysis including ridge regression, Lasso, linear discriminant analysis, nonparametric regression, bootstrap, EM algorithm, classification trees, neural networks, dimensionality reduction techniques, and clustering. If time allows additional topics may be taught.