An application-focused approach to linear algebra used in data science. Topics include matrices, Gaussian elimination, vector spaces, inner products, orthogonality, least squares, eigenvalues/vectors, matrix factorizations, singular value decomposition and principal component analysis, quadratic forms, data/image processing, and other topics pertinent to data analysis.

An introduction to the foundations and applications of statistics. Topics include basic concepts of data collection sampling and experimental design, descriptive analysis and graphical displays of data, probability concepts and expectations, normal and binomial distributions, sampling distributions and the Central Limit Theorem, confidence intervals and hypothesis testing, likelihood-based statistics, ANOVA, correlation and simple linear regression.

An application-focused approach to regression analysis and related techniques. Topics include simple and multiple linear regression, weighted and generalized least squares estimators, polynomial regression, exponential regression, model selection, categorical variables and ANOVA, logistic regression, principal component analysis, time series analysis, and other applications of statistics as relevant. Prerequisite: MATH 546

This course provides a comprehensive, application-focused overview of essential statistical methods for data science. Topics include data collection techniques, descriptive statistics, and exploratory data analysis. Foundational concepts such as sampling distributions and the Central Limit Theorem set the groundwork for estimation, confidence intervals, and hypothesis testing. Students will explore techniques in ANOVA and categorical data analysis, as well as nonparametric techniques and permutation tests. Advanced methods include the bootstrap, linear and logistic regression, generalized linear models, and linear discriminant analysis, equipping students with a versatile toolkit for real-world data analysis and decision-making in data-driven contexts.