Modeling and Simulation (GC)

Survey of core concepts and methods of modeling and simulation. This course includes a broad array of computer laboratory based exercises, and a student-designed project by the end of the course.

Prerequisites: none

Seminar course in which students present and defend the modeling and simulation work undertaken in the required discipline-based capstone/individual study course.

Prerequisites: none

Survey of core concepts, methods, and applications of data-driven modeling and big data analysis. Each topic is designed as a weekly module and the course includes a broad array of computer laboratory based exercises, real world problems based case studies, and a student-designed term project.

Prerequisites: none

A continuation of AET 638.

Prerequisites: none

The course explores big data in structured and unstructured data sources. Emphasis is placed on big data strategies, techniques and evaluation methods. Various data analytics are covered. Students experiment with big data through big data analytics, data mining, and data warehousing tools.

Prerequisites: none

This course discusses concepts and techniques for design, development and evaluation of user interfaces. Students will learn the principles of interaction design, interaction styles, user-centered design, usability evaluation, input/output devices, design and analysis of controlled experiments and principles of perception and cognition used in building efficient and effective interfaces. Group project work.

Prerequisites: none

The study of methods and techniques for building econometric models with the goal of forecasting and measurement of the economic relationships by integrating economic theory and statistics in it.

Prerequisites: none

This course is designed to cover basic tools in time series analysis and to equip students with quantitative skills to analyze the financial market. Pre-req: ECON 207 or with permission by the instructor.

Prerequisites: none

Application of EE computer modeling and simulation tools. Design of experiments, Taguchi methods, automated data acquisition, and analysis methods.

Prerequisites: none

This course covers the analysis of continuous and discrete multivariate systems, linear models of stochastic and non-stochastic systems, and analog and digital sampled data systems. Issues examined include controllability, stability, observability, tensor properties, signal spectra, state equations, optimization, and computer simulation. A variety of case studies of advanced systems also examined.

Prerequisites: none

This course covers the analysis of non-linear continuous and discrete systems and devices. Topics covered include non-linear circuit analysis, non-linear stochastic and non-stochastic system models, limit cycles, oscillators, stability, non-linear wave functions. Computer simulation will be utilized in conjunction with selected case studies in advanced non-linear systems.

Prerequisites: none

Mathematical modeling of living systems. Entropy and information. Thermodynamic constraints. Feedback and feedforward mechanisms in metabolic processes. Metabolic heat generation and loss. Energy flow in living systems. Atomic and molecular bonds in biological systems. Engineering analysis of the cardiovascular, renal, immune, endocrine and nervous systems; analysis of specific disease states.

Prerequisites: none

Four major sets of ideas will be covered: (1) Introduction to Spatial Organization, (2) Network Analysis, (3) Allocation Methods, and (4) Urban Transportation. The emphasis is on these approaches to understanding the geography of transport by description, explanation, and normative or optimal methods.

Prerequisites: none

This course will cover basic strategies for conducting field surveys and gathering from the real world data appropriate to mapping the earth's surface. Emphasis will be upon simple but reliable techniques, ranging from compass-and-pacing to global positioning systems (GPS).

Prerequisites: none

Comprehensive examination of GIS for manipulation and analysis of spatially-referenced data, including data structure and organization, input and output problems, data management, and strategies for analytical work.

Prerequisites: none

This is an introductory course on theories and techniques of remote sensing. Focus will be placed on providing students with a general overview of the application of remote sensing to practical problems, and hands-on experience for image processing and analysis.

Prerequisites: none

This course will introduce students to the fundamental knowledge and techniques of open-source GIS and geospatial data analysis. Students will learn the basic and advanced GIS functions in QGIS, a popular open-source GIS with advanced capabilities. The major topics that will be covered include open-source GIS data standards; working with projections and available GIS data; making maps in QGIS; spatial and attribute data query, editing, and manipulation; multi-criteria overlay analysis; raster image styling and analysis; 3D and terrain analysis; spatial pattern analysis; spatial interpolation; automating map creation and complex workflows using processing models; customize and extend QGIS.

Prerequisites: none

Descriptive statistics, probability, hypothesis testing, introduction to non-parametric statistics, correlation, introduction to regression analysis, spatial statistics and principles of data representation in graphs, tables and statistical results.

Prerequisites: none

This offering will include a variety of selected technical topics in geography, including (but not limited to) manual cartographic drafting and negative scribing, photomechanical techniques in production cartography, aerial photo interpretation, and advanced coverage of digital analysis of satellite-derived remote sensor data and global positioning systems.

Prerequisites: none

Survey of theoretical frameworks for spatial analysis and geographic quantitative methods. Includes basic and advanced spatial analysis principles and methods for studying and examining spatial patterns. Designed to equip students with the knowledge and skills necessary for carrying our research projects that demand spatial point pattern analysis and analysis of areal units.

Prerequisites: none

This offering will include supervised project work in raster-based and/or vector-based GIS, using problems and data drawn from local or regional agencies or other professional-level organizations with whom the Geography Department maintains a relationship. Students must have completed one of the prerequisite courses, or professional-level experience.

Prerequisites: none

Topics vary in physical, cultural, economic, political, and historical geography, as well as environmental conservation and geographic techniques.

Prerequisites: none

Discussion and analysis of contemporary issues in the field of Geographic Techniques. Designed to allow in-depth focus on current problems/issues that geographers will encounter in their professional practice. Topics vary according to instructor.

Prerequisites: none

Introduction to statistical analysis as applied to the health sciences. Examines concepts and methods of statistical procedures applied to health problems and issues.

Prerequisites: none

This course presents the theory, computations, and applications of partial differential equations and Fourier series.

Prerequisites: MATH 223 and MATH 321 with “C” (2.0) or better or consent.

This course presents topics from mathematical analysis of both discrete and continuous models taken from problems in the natural sciences, economics, and resource management.

Prerequisites: MATH 223 and MATH 247 with “C” (2.0) or better or consent.

This course provides an introduction to techniques and analysis involved with solving mathematical problems using technology. Topics included are errors in computation, solutions of linear and nonlinear equations, numerical differentiation and integration, and interpolation.

Prerequisites: MATH 122 and MATH 247 with “C” (2.0) or better or consent.

This course is a continuation of MATH 470. Topics included are the algebraic eigenvalue problem, least-squares approximation, solutions of systems of nonlinear equations, and numerical solutions of ordinary differential equations.

Prerequisites: (MATH 470 or MATH 570) and MATH 223 with “C” (2.0) or better or consent.

A short course devoted to a specific mathematical topic. May be repeated for credit on each new topic.

Prerequisites: none

Applications of discrete and continuous mathematics to deterministic problems in the natural sciences, computer science, engineering, and economics. Applied problems will be developed within the mathematical framework of dimensional analysis, asymptotic analysis, perturbation theory, stability, and bifurcation.

Prerequisites: MATH 321 and (MATH 417 or MATH 517) and (MATH 447 or MATH 547) or consent.

Can be used for any graduate level applied mathematics course not offered as a regular course. Distinct offerings may be repeated for credit.

Prerequisites: (MATH 417 or MATH 517) and (MATH 422 or MATH 522) and (MATH 447 or MATH 547) or consent.

Optimal conditions for constrained and unconstrained optimization problems, and a comprehensive description of the most powerful, state-of-the-art, techniques for solving continuous optimization problems. Large-scale optimization techniques are emphasized in the course.

Prerequisites: MATH 517 and MATH 547

This course is an in-depth study of solving ordinary differential equations and partial differential equations numerically. Runge-Kutta methods and general multi-step methods are developed for ordinary differential equations. Finite Difference Method and Finite Element methods are developed for partial differential equations. Error control and step size changing for both stiff and non-stiff equations are analyzed.

Prerequisites: MATH 321 and (MATH 470 or MATH 570) or consent.

This course is an in-depth study of solving algebraic eigenvalue problems, least-square problems, direct and iterative methods for solving linear systems, and their applications.

Prerequisites: (MATH 447 or MATH 547) and (MATH 470 or MATH 570) or consent.

A graduate course in an area of mathematics not regularly offered. May be repeated for credit on each new topic.

Prerequisites: none

A short course devoted to a specific mathematical topic. May be repeated for credit on each new topic.

Prerequisites: none

Energy method and residual approaches, 2D and 3D problems, in stress anaylsis, application to steady and transient heat flow, hydrodynamics, creeping flow, solution methods.

Prerequisites: none

Numerical methods for solving linear systems of equations, solution of non-linear equations, data interpolation, numerical differentiation, numerical integration, numerical solution of ordinary and partial differential equations.

Prerequisites: none

Investigation, review, and application of emerging computer aided tools for engineering. Advanced FEA; optimization.

Prerequisites: none

Numerical methods (finite difference, finite volume, finite element) used for solving partial differential and integral equations of the type commonly occuring in fluid mechanics and heat transfer. Numerical solutions for selected problems in fluid mechanics and heat transfer. Use of CFD software.

Prerequisites: none

Preparation of a master's degree alternate plan paper under the direction of the student's graduate advisor. Pre-req: consent

Prerequisites: none

Thesis research.

Prerequisites: none

Thesis preparation.

Prerequisites: none

Student culminating experience in lieu of a thesis.

Prerequisites: none

A culminating project related to basic or applied research

Prerequisites: none

Research under the supervision of the student's advisor leading to a thesis.

Prerequisites: Consent

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Prerequisites: none

Course provides students with the opportunity to focus on a research problem that is related to their area of nursing practice. Students work with a nursing faculty advisor (committee chairperson) in developing the thesis proposal, writing the thesis, and preparing to disseminate the results of the study. With the advisor's approval, the thesis is submitted for oral defense as part of the requirements for the MSN degree.

Prerequisites: none

An advanced learning experience working in small group settings on applied projects and problem solving. The team project produced in the studio meets graduate student's capstone project requirements.

Prerequisites: none