This course introduces topics in time series analysis as they relate to financial data. Topics include properties of financial data, moving average and ARMA models, exponential smoothing, ARCH and GARCH …
This course focuses on applying data analytic techniques to business, including customer analytics, business analytics, and web analytics through mining of social media and other online data. Several projects are …
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This course provides the opportunity to offer a new topic in the subject area of Statistics.
This course introduces various topics in machine learning, including regression, classification, resampling methods, linear model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. The statistical software R …
This course provides a platform for exploring advanced statistical modeling and analysis techniques through the lens of state-of-the-art sports analytics. Prerequisite: A prior course in mathematical statistics, a prior course …
Reading and study programs in areas of interest to individual students. For students interested in topics not covered in regular courses. Students must obtain a faculty advisor to approve and …
Students will work in teams on a capstone project. The project will involve significant data preparation and analysis of data, preparation of a comprehensive project report, and presentation of results. …
Linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, autocorrelation in time series data, polynomial regression, and nonlinear regression. Conceptual discussion in lectures …
Topics include lifetime distributions, hazard functions, competing-risks, proportional hazards, censored data, accelerated-life models, Kaplan-Meier estimator, stochastic models, renewal processes, and Bayesian methods for lifetime and reliability data analysis. Prerequisite: MATH …
Studies the basic time series models in both the time domain (ARMA models) and the frequency domain (spectral models), emphasizing application to real data sets. Conceptual discussion in lectures is …
This course covers the main designs and estimation techniques used in sample surveys: simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation, and non response and other non …
Studies experimental designs for randomized clinical trials, sources of bias in clinical studies, informed consent, logistics, and interim monitoring procedures (group sequential and Bayesian methods). Prerequisite: A basic statistics course …
This course introduces a plethora of methods in data mining through the statistical point of view. Topics include linear regression and classification, nonparametric smoothing, decision tree, support vector machine, cluster …
Introduces statistical methods used for causal inference, particularly for quasi-experimental data. Focus is on the potential outcomes framework as an organizing principle and examining the estimation of treatment effects under …
Introduces philosophy and methods of exploratory (vs confirmatory) data analysis: QQ plots; letter values; re-expression; median polish; robust regression/anova; smoothers; fitting discrete, skewed, long-tailed distributions; diagnostic plots; standardization. Emphasis on …
"Topics include importing data from various sources into R/SAS, manipulating and combining datasets, transform variables, "clean" data so that it is ready for further analysis, manipulating character strings, export datasets, …
This course provides the opportunity to offer a new topic in the subject area of statistics.
Introduces various topics in machine learning, including regression, classification, resampling methods, linear model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. The statistical software R is incorporated …
Research into current statistical problems under faculty supervision.