Descriptive statistics, random variables, probability and sampling distributions, estimation, hypothesis testing, regression, analysis of variance, non-parametric techniques. May not be taken for credit after completion of STAT 3153.

The goal of this course is to introduce the basic computer programming using Python and R to the undergraduate students who are interested in working in the rapidly growing fields of data science and data analytics. Python and R are two open-source programming languages with a large data scientist community. This course will introduce the basic programming skills and tools necessary to efficiently collect, process, visualize, and analyze the datasets. Hands-on projects will be given to help students gain experience with software packages.

This course introduces the methods for fitting and interpreting regression models. Topics include simple linear regression (SLR), multiple linear regression (MLR), model checking, variable selection methods, dummy variables, diagnostic measures, logistic regression, and time series analysis. Instruction will include the use of statistical programming language.

A balanced approach emphasizing both theory and applications will be taken. Topics include descriptive statistics, exploratory data analysis, probability and probability models, discrete and continuous random variables, confidence intervals, hypothesis testing, and control charts. Students will be required to collect data, use a current statistical software package to analyze the data, and make inferences based upon the data analysis as part of an individual and/ or group project.

Statistical process control is an important topic for anyone interested in applying statistics in industry. This course focuses on theory and methods of quality monitoring including process capability, control charts, acceptance sampling, quality engineering, and quality design.

In this course we develop knowledge of the fundamental probability tools for quantitatively assessing risk. The application of these tools to problems encountered in actuarial science is emphasized. A thorough command of the supporting calculus is assumed. A very basic knowledge of insurance and risk management is assumed.

This course is a continuation to STAT 3203. At the end of the course, a students is prepared to take Exam P of the Society of Actuaries.

Statistical tools to analyze univariate and multivariate categorical responses. Emphasis is given to Generalized Linear Models, including logistic regression and loglinear models.

This course introduces students to both design and analysis of experiments as well as statistical computing. Emphasis is given to develop an understanding of experimental methods and major experimental designs. Students will be required to design and carry out an experiment, use a current statistical software package to analyze the data, and make inferences based upon the analysis.

This is an introductory course in mathematical statistics. Topics include distribution functions (both discrete and continuous), multivariate distributions, distributions of functions of random variables, and statistical inference.

This course will include analysis of variance, one factor experiments, experimental design with two or more factors, linear and multiple regression analysis, and categorical data analysis.

This is an introductory course in Financial Mathematics. The student will learn about the different types of interest (simple interest, discount interest, compound interest), annuities, debt retirement methods, and investing in stocks and bonds.

This is a continuation of STAT 4283. Topics include loans, bonds, cash flow and portfolios, immunization, derivatives and options. At the end of this course, a student is prepared to take Exam FM of the Society of Actuaries.

This course is directed towards advanced undergraduates in statistics, mathematics, or related quantitative fields. The focus of the course is an accessible overview of the field of machine learning and provide the students with valuable hands-on experience by illustrating how to implement each of the machine learning methods using Python. Topics covered include Decision Tree, Support Vector Machines, and the kernel methods, AdaBoost and GBDT method, Logistic regression, and neural network, and more.

This course is directed towards advanced undergraduates or master's students in statistical or related quantitative fields. The focus of the course is an accessible overview of the field of statistical learning and provide the students with valuable hands-on experience by illustrating how to implement each of the statistical learning methods using R or other statistical programming language. Topics covered include: regression techniques, classification methods, linear model selection and regularization, unsupervised learning, and more.

Statistical tools to analyze univariate and multivariate categorical responses. Emphasis is given to Generalized Linear Models, including logistic regression and loglinear models.

This course introduces students to both design and analysis of experiments as well as statistical computing. Emphasis is given to develop an understanding of experimental methods and major experimental designs. Students will be required to design and carry out an experiment, use a current statistical software package to analyze the data, and make inferences based upon the analysis.

The focus of the course is an accessible overview of the field of machine learning and provide the students with valuable hands-on experience by illustrating how to implement each of the machine learning methods using Python. Topics covered include Decision Tree, Support Vector Machines, and the kernel methods, AdaBoost and GBDT method, Logistic regression, and neural network, and more.

This course is directed towards advanced undergraduates or master's students in statistical or related quantitative fields. The focus of the course is an accessible overview of the field of statistical learning and provide the students with valuable hands-on experience by illustrating how to implement each of the statistical learning methods using R or other statistical programming language. Topics covered include: regression techniques, classification methods, linear model selection and regularization, unsupervised learning, and more.