Courses

Basic concepts of analytical geometry, limits and derivatives, differentials and rates, integration, definite and indefinite integrals, differentiation of logarithmic and exponential functions.

A course emphasizing the empirical and scientific approaches to disciplines involved in statistical and data analytics sciences. Students learn the historical foundation of each departmental program area. Students will also familiarize themselves with the perspectives and specialties of our department faculty while systematically and critically reviewing the expanding roles of data-centric sciences. The course focuses on teaching, writing, developing a future internship, exploring potential graduate work, and engaging in professional associations from a Christian worldview. Note: This course is designed to introduce students in statistics-related majors (Applied Statistical Analysis, Statistics and Data Analytics, Actuarial Science, Sports Analytics, etc.) to the discipline. It is not designed as a major or general education course in statistics and does not fulfill the 'introduction to statistics' requirement for any major.

Mathematical theory and applications, development of formulae, principles of statistical decision theory, descriptive measurements, probability concepts, random variables, normal distribution, inferential statistics, sampling distributions, confidence intervals, hypothesis testing, chi-squared procedures, linear regression, and the use of computers in statistics.

This course represents a basic concepts and methodology course in regression analysis using application of general linear regression models to real-life situations. Case studies are used to give practice in diagnosing practical problems, deciding on appropriate models, and knowing which inferential technique will answer the researcher's questions for the purposes of description and prediction. Regression models and model building typical of problems used in the social and behavioral sciences, the natural and health sciences, and many other disciplines are covered.

An introduction to data mining, management and statistical programming techniques using comprehensive and widely available tools like SAGE, SPSS, SAS and R. Students learn exploratory data analysis, coding and manipulation of variables, database management applying statistical concepts. Modeling and simulation experiments on a variety of applied data sets.

A continuation of Statistical Computing I using comprehensive and widely available tools like SAGE, SPSS, SAS and R. Advanced techniques will be covered including (but not limited to) numerical linear algebra, optimization and nonlinear equations, the EM algorithm, Laplace approximations, quadrature methods, simulation methodology, sampling, Monte Carlo and bootstrap methods.

This course studies experimental designs with corresponding models and analyses critical for students in the empirical sciences. Course topics include estimation, test of hypothesis, analysis of variance and a variety of topics in experimental design. Decisions and practical considerations which minimize experimental error and avoid confounding results are dealt with in real life contexts.

Sampling theory and practice are presented in this course through a study of simple random samples, stratified random samples, cluster sampling, estimating sample size, ratio estimates, subsampling, two-state sampling and analysis of sampling error. This is a critical course for students in education and the social, medical, biological and management sciences where sampling is a fundamental step in virtually every statistical procedure and critical to meaningful survey research.

The first semester of a two-semester course providing a systematic development of the theories of probability and statistics. Students learn and use fundamental concepts of probability models, random variables and their distributions, reduction of data, estimation, testing of hypotheses, univariate normal inference, and statistical decision theory. The first semester is required for BA and BS statistics majors of all concentrations.

econd semester course in a systematic development of the theories of probability and statistics. Topics include analysis of categorical data, multivariate distributions, nonparametric inference, linear models and analysis of variance. As time permits, the theory underlying Markov chain, Monte Carlo, quasi-likelihood, empirical likelihood, statistical functionals, generalized estimating equations, the jackknife, and the bootstrap are addressed.

The focus of this class is an independent research project that the student undertakes under the direction of a faculty member who acts as a research advisor. Content varies from year to year and is determined by both the instructor's and student's interests.

The course is designed to be a culminating experience for senior students. The course gives students through writing, seminar and conference participation, an opportunity to demonstrate their skill and proficiency in the field of statistics. In some cases, this may be coupled with internships.

Continued study and applications of integration: volumes, lengths, surface of revolution; derivatives and integrals involving trigonometric functions, infinite series, expansion of functions, hyperbolic functions, law of the mean, partial fractions, polar coordinates, and conic sections.

This course is designed to teach students some of the basic computational skills of Linear Algebra in the context of Differential Equations. Students will learn to use the basic operations of matrices, study systems of linear equations and find the determinant, eigenvalues and eigenvectors of a matrix. The student will apply these tools in the qualitative study of solutions to systems of Differential Equations.

Study and applications of vector analysis, partial differentiation, multiple integration, Jacobians, theorems of Green and Stokes, and divergence theorem.

This course provides the tools to create and critically evaluate data visualizations. Focus will be on statistical graphics, graphics that display statistical data. Additionally, recent advances in the field of information visualization will be covered.

A course to introduce statistical models of advanced least squares regression and standard ANOVA techniques and extensions to categorical data. Students will conceptually understand linear mixed effects models, log linear and generalized linear models for count data; and survival models for the analysis of lifetime data. In addition, students will apply these models to real data, to discern patterns and conclusions, and present their results.

A course in application of parametric and nonparametric single and multivariable data analytic techniques to sports. Topics will include: linear regression, correlation, confounding and interactions, variable selection, categorical predictors and outcomes, logistic regression, factor analysis, discriminant analysis, and regression techniques with longitudinal data.

This course teaches strategies for visual analyses of business data to inform business strategies across a variety of industries. Students will learn how to create and interpret charts, graphs, infographics, interactive dashboards and communicate the visual insights to customers and employers. A variety of visualization tools will be used for practice and preparation for industry certifications, e.g. SAS, Tableau, GIS.

This course trains students in developing spatial analyses for business decisions and strategy across a variety of industries, e.g. business, logistics, real estate, healthcare, marketing analytics, finance. Students will learn how to perform market and customer segmentation, identify patterns in consumer behavior and how they change over time, and others. This course prepares towards SAS, Tableau, GIS certification and is ideal for students who wish to have careers requiring analytics, strategy and/or consulting.

This course teaches business models to explore and predict specific applied business environments, e.g. customer retention, consumer decisions, sales forecasting, text analytics, etc. Concepts combine application of business models and strategy for marketing, finance, production, process, and managerial targets. This course is ideal for students who prepare for careers requiring analytics, strategy and/or consulting and prepares for industry certifications, e.g. SAS, Tableau, GIS.

This course teaches how to develop business analytics that improve customer experiences and meet employers' expectations. Students will learn design thinking strategies to identify hidden customer needs, define problems and opportunities, elicit and gather project requirements. They will then practice aligning these user needs with analytics models and tie them into a winning business strategy. This course is ideal for students who prepare for careers requiring analytics, strategy, and/or consulting for a variety of industries.

An introduction to the fundamental concepts of financial mathematics including basic interest theory. These concepts will be applied in calculating present and accumulated values for various streams of cash flows as a basis for future use in: reserving, valuation, pricing, asset/liability management, investment income, capital budgeting, and valuing contingent cash flow.

A continuation of the fundamental concepts of financial mathematics including the basics of financial economics and an introduction to financial instruments, including derivatives, and the concept of no-arbitrage as it relates to financial mathematics.