Graduate Statistics Courses
COS531 Modern Applied Statistical Learning (3 credits)
Modern statistical learning focuses on the application of complex computing techniques and statistical inference to extract information from data to help address trends and patterns, forecast potential future problems, and make business decisions. This course is designed to provide students with hands-on, practical experience in statistical learning methods such that they can apply them to solving real-world problems. Students enhance their understanding of statistical analysis and inference while getting trained on industry-standard software packages such as R.
COS536 Applied Machine Learning (3 credits)
This is required course for Data Science Master’s Program. It extends some topics of DAS541 Data Mining for Business and focuses on the theoretical basis and applications of the state-of-the-art machine learning algorithms.
COS541 Big Data and Data Engineering (3 credits)
This course introduces students to the fundamental concepts, techniques, and tools used in managing and analyzing large volumes of data, focusing on data engineering principles and practices. Topics covered include data ingestion, storage, processing, and analysis in the context of big data technologies and platforms.
DAS522 Exploratory Data Analysis and Visualization (3 credits)
Students will learn the art and science of converting data into graphical representations that make complex information accessible and actionable. This course covers the basics of R programming, data cleaning, transformation techniques, and the use of Tableau for creating compelling dashboards. The curriculum is designed to help students master the skills needed to present data in visual formats that reveal patterns, trends, and correlations.
DAS541 Data Mining for Business (3 credits)
This course seeks to equip students with a solid understanding of opportunities, techniques, and critical challenges in using data mining and predictive modeling in a business setting. The focus is to enable students to develop the ability to translate business challenges into data mining problems and apply predictive modeling technologies to improve business decisions.
DAS631 Generative AI: Foundation and Application (3 credits)
This graduate-level course provides a comprehensive introduction to the foundations and applications of Generative AI, with a focus on Transformer-based models and Diffusion Models. Students will explore how these state-of-the-art generative models can be used to generate novel and realistic content, including text, images, and other modalities. The course will cover the underlying principles, architectures, and training techniques of these generative models. Students will gain a deep understanding of the theoretical and practical aspects of generative AI, including how these models work, their strengths and limitations, and their real-world applications. Additionally, the course will explore the societal impact, ethical considerations, and future directions of generative AI, addressing issues such as bias, privacy, and responsible development. Students will have the opportunity to work on hands-on projects, implement generative models, and explore the latest advancements in the field.
STA502 Probability Theory (3 Credits)
Fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, laws of large numbers and the central limit theorem.
STA511 Advanced Regression Analysis (3 Credits)
This course is intended for graduate students in data science. It provides the methods and applications of fitting and interpreting multiple regression models. The primary emphasis is on the method of least squares and its many varieties.
STA512 Statistical Inference (3 Credits)
This course will introduce the underlying theories and methods of statistical data display, analysis, inference, statistical decision-making and ANOVA. The course will cover topics including basic concepts of probability, maximum likelihood estimation, sufficiency, completeness, ancillary, unbiasedness, consistency, efficiency, asymptotic approximations, ANOVA, and regression.
STA515 Applied Statistics I (3 Credits)
Applied Statistics I is designed as an intermediate statistics course tailored for students with no prior statistical background. This foundational course will provide essential skills and knowledge, ensuring that all students, regardless of their previous experience, can successfully engage with more advanced statistical topics later in the program.
STA521 Design and Analysis of Experiments (3 Credits)
In this course students learn how to use the methods of statistical design of experiments (DOE) in order to design efficient experiments, analyze results correctly and present them in a clear fashion. Statistical DOE is used widely in both industry and academia. Graduate and undergraduate students from any field of science or engineering can use the methods learned in the course in their projects and research.
STA539 Time Series Analysis (3 credits)
This course introduces the basic time series analysis and forecasting methods. Topics include time series regression and exploratory data analysis, stationary processes, ARMA/ARIMA models, spectral analysis, model and forecasting using ARMA/ARIMA models, nonstationary and seasonal time series models, multivariate time series, state-space models, and forecasting techniques.
STA541 Survival Analysis (3 credits)
This course introduces basic concepts and methods for analyzing survival time data obtained from following individuals until occurrence of an event or their loss to follow-up. Students will learn the characteristics of survival (time to event) data and building the link between distribution, survival, hazard functions, non-parametric, semi-parametric, and parametric models and two-sample test techniques. During the class, students will also learn how to use R to analyze survival data.
STA561 Statistical Consulting (3 credits)
The goal of this course is to teach statistics students to be effective statistical consultants.
STA571 Advanced Statistical Computing (3 Credits)
The course will cover advanced computational algorithms designed primarily for fitting complex Bayesian hierarchical models. These include MCMC, variational inference, Hamiltonian Monte Carlo, stochastic optimization among others. Lectures involve a general description of methodology followed by demonstration of algorithms. The computational techniques will be discussed without a concrete focus on a particular programming language. General guidance will be provided on how to make the codes/algorithms efficient, devise and run large scale simulations and submit jobs to high performance computing.
STA615 Applied Statistics II (3 Credits)
Applied Statistics II focuses on regression analysis and other classical statistical methods, which are critical for a thorough understanding of statistical modeling. This change aims to strengthen the students' grasp of core statistical concepts, making them well-prepared for practical applications and advanced study.
STA621 Sampling Methods (3 credits)
This is a graduate level statistics course. It covers estimation of population mean, total, and proportion using different sampling methods.
STA631 Multivariate Analysis (3 credits)
The course will cover important statistical methods and relevant theory for analyzing continuous multivariate data.
STA635 Bayesian Statistics (3 credits)
Bayesian statistical methods combine information from similar experiments, account for complex spatial, temporal, and other correlations, and also incorporate prior information or expert knowledge (when available) into a statistical analysis.
STA641 Applied Longitudinal Data Analysis (3 Credits)
This course is designed to provide an overview of statistical models and methodologies for analyzing repeated measures with a particular emphasis on analyzing longitudinal data. Its analysis requires much more sophisticated methodologies due to the correlation among observations. This course covers modern statistical techniques for longitudinal data from an applied perspective. Topics include characteristics of the longitudinal design, graphical exploration of the mean and correlation structure, linear mixed effects models and multilevel modeling, maximum likelihood and restricted maximum likelihood estimation, modeling the variance-covariance structures, inference for random effects, logistic and Poisson mixed effects model for binary and count data, marginal models and generalized estimating equations, and model diagnostics. Analysis of real and substantial data sets using statistical software SAS will be integrated throughout.
STA651 Categorical Data Analysis (3 credits)
This is a graduate level statistics course. Topics include description and interference using proportions and odd ratios, multi-way contingency tables, logistic regression and other generalized linear models, and log linear models.
STA665 Financial Accounting (3 credits)
Catalog Description: This course provides an introduction to financial accounting as the “language of business.” It emphasizes the analysis and evaluation of accounting information from the perspective of both stakeholders and managers in the processes of planning, decision-making, and control.
STA671 Linear Models (3 credits)
This course is an introduction to the formulation and use of the general linear model, including parameter estimation, inference and the use of such models in a variety of settings. Emphasis will be split between understanding the theoretical formulation of the models and the ability to apply the models to answer scientific questions.
STA701 Generalized Linear Models (3 credits)
The course provides an in-depth coverage of the theory of generalized linear models. Topics covered are numerical algorithms, exponential family, modeling checking, logistic regression, loglinear models, estimating equations.
STA711 Advanced Topics in Statistical Modeling (3 credits)
This course focuses on advanced topics in statistical modeling, providing students with a deeper understanding of sophisticated techniques and methodologies used in data analysis.
STA745 Nonparametric Statistics (3 credits)
This course teaches students applications and principles of nonparametric statistics, which include classical rank-based methods and selected categorical data analysis and modern nonparametric methods.
STA751 Applied Statistics Project or Thesis (3 credits)
Under the guidance of a supervisor, one or two-semester course consists of writing and a public defense. Students shall formulate a research question of relevance within the field of study, choose a methodology that can be used to find answers to the research question, use concepts, theories and methods to analyze and answer the research question and present a written, scientifically convincing argumentation to justify the results. Each student is given a supervisor who will advise the student in the research task included in the course and in the writing of the thesis.