About the Degree

Course provides an introduction to Big Data concepts, with focus on data management, date modeling, visualization, security, cloud computing, and data science from different perspectives: computer science, business, social science, bioinformatics, engineering, etc. Course introduces tools for data analytics such as SPSS Modeler, R, SAS, Python, and MATLAB. Two case study projects which are integrated with communication and business skills.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science

Application, construction, and evaluation of statistical models used for prediction and classification. Topics include data pre-processing, over-fitting and model tuning, linear and nonlinear regression models and linear and nonlinear classification models.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MA 3740 or MA 4710 or MA 4720 or MA 4780

Data mining focuses on extracting knowledge from large data sources. The course covers data mining concepts, methodology (measurement, evaluation, visualization, etc.), algorithms (classification/regression, clustering, association rules, etc.), and applications (web mining, recommender systems, bioinformatics, etc.).

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Restrictions: Permission of instructor required
• Pre-Requisite(s): (CS 3425 or MIS 3100) and (MA 2330 or MA 2320 or MA 2321) and (MA 2710 or MA 2720 or MA 3710)

Focuses on management of IS/IT within the business environment. Topics include IT infrastructure and architecture, organizational impact of innovation, change management, human-machine interaction, and contemporary management issues involving data analytics. Class format includes lecture, group discussion, and integrative case studies.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Applied Natural Resource Econ., Accounting, Data Science, Business Administration

This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): CS 4821

Development and administration of secure software systems. Topics include principles of software development, practical cryptography, program security, operating system security, database security, administration, legal and ethical issues.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): CS 3411 or CS 4411

Statistical modeling and inference for analyzing experimental data that have been observed at different points in time. Topics include models for stationary and non stationary time series, model specification, parametric estimation, model diagnostics and forecasting, seasonal models and time series regression models.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): (MA 2710 or MA 2720 or MA 3710 or MA 3715) and MA 3720

An evolutionary strategic perspective is taken viewing how technology strategy evolves from underlying technological competencies, patterns of innovation, sources of external technological knowledge and modes of transfer.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Applied Natural Resource Econ., Accounting, Data Science, Business Administration

An overview of data analysis methods including visualization, data programming, and univariate statistics such as t-test and ANOVA.

• Credits: 3.0
• Lec-Rec-Lab: (0-2-1)
• Semesters Offered: Fall - Offered alternate years beginning with the 2018-2019 academic year
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Students will learn computer programming skills in Perl for processing genomic sequences and gene expression data and become familiar with various bioinformatics resources.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall - Offered alternate years beginning with the 2017-2018 academic year
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Role of computer simulation in physics with emphasis on methodologies, data and error analysis, approximations, and potential pitfalls. Methodologies may include Monte Carlo simulation, molecular dynamics, and first-principles calculations for materials, astrophysics simulation, and biophysics simulations.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: Spring
• Pre-Requisite(s): PH 3300 and PH 4390 and (PH 2400 or PH 3410)

Set in a Linux environment, course offers exposure to Foss tools for developing computational and visualization workflows. Students will learn to translate problems into programs, understand sources of errors, and debug, improve the performance of and parallelize the code.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall, Spring
• Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate

An overview course of biostatistical methods used in the health sciences. Topics include a review of undergraduate statistical concepts, NIH, CDC, and FDA guidelines for clinical trial research, proper use of biostatistical methods including anova models, logistic regression, risk analysis, survivorship analysis and any other statistical methods that are common in the enrolled students' discipline.

• Credits: variable to 4.0
• Semesters Offered: On Demand
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): MA 2720 or MA 3710

Applications and case studies focusing on contemporary issues in operations and quality management to include lean manufacturing practices, ERP, quality and environmental management systems/standards, Six Sigma, statistical process control, and other current topics.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Applied Natural Resource Econ., Accounting, Data Science, Business Administration
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or EET 2010 or CEE 3710

The course facilitates students' improvement of analytical skills, information processing techniques, and cultural competence in the globalized marketing environment. Focuses are placed on strategic marketing management, high-tech product marketing, global consumer behavior, branding, and online marketing.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Applied Natural Resource Econ., Accounting, Data Science, Business Administration

Introduces techniques and procedures to estimate and test economic and financial relationships developed in business, economics, social and physical sciences.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): (EC 2001 or EC 3002 or EC 3003) and (BUS 2100 or MA 2710 or MA 2720 or MA 3710) and (MA 1135 or MA 1160 or MA 1161)

Emphasizes database principles that are constant across different database software products through concrete examples using a relational database management system. Provides a well-rounded business perspective about developing, utilizing, and managing organizational databases.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): MIS 2000(C)

Focuses on generation and interpretation of business analytics relative to organizational decision making. Includes core skills necessary for constructing data retrieval queries in a relational database environment.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring - Offered alternate years beginning with the 2018-2019 academic year
• Pre-Requisite(s): MIS 2000 and (MIS 3100 or CS 3425)

Introductory study of the properties of polymers. Includes structure and characterization of polymers in the solid state, in solution, and as melts. Topics include viscoelasticity, rubbery elasticity, rheology and polymer processing. Applications discussed include coatings, adhesives, and composites.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Pre-Requisite(s): CH 1122 or (CH 1160 and CH 1161)

Advanced study of mechanistic organic and physical organic chemistry intended to bring the student to the level of current research activity. Topics may include methods for determining organic reaction mechanisms, chemical bonding as it applies to organic compounds, structure-reactivity relationships, molecular rearrangements, and molecular orbital theory.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: On Demand
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Advanced study of organic reactions and synthetic organic chemistry intended to bring the student to the level of current research activity. Topics may include retrosynthetic analysis and synthesis design, synthons, protecting groups, and analysis of syntheses from recent literature.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: On Demand
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Assessment of factors controlling environmental fate, distribution, and transformation of organic pollutants. Thermodynamics, equilibrium, and kinetic relationships are used to quantify organic pollutant partitioning and transformations in air, water, and sediments. Use of mass balance equations to quantify pollutant transport.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall - Offered alternate years beginning with the 2005-2006 academic year
• Pre-Requisite(s): ENVE 4501 or CEE 4501 or CH 3510

Study of the photochemical processes governing the composition of the troposphere and stratosphere, with application to air pollution and climate change. Covers radical chain reaction cycles, heterogeneous chemistry, atmospheric radiative transfer, and measurement techniques for atmospheric gases.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): CH 3510 or ENVE 4501 or ENVE 4504 or CEE 4501 or CEE 4504

This course is focused on the chemistry of atmospheric aerosols and cloud processes. Students will learn about methods for chemical characterization, the chemical composition of aerosol and the chemical reactions pertinent to secondary aerosol and cloud composition.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring - Offered alternate years beginning with the 2019-2020 academic year
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): ATM 5515(C)

Focuses on the theory and method of modern computational techniques applied to the study of molecular properties and reactivity through lecture and computer projects. Covers classical mechanical as well as quantum mechanical approaches.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: Fall - Offered alternate years beginning with the 2010-2011 academic year
• Pre-Requisite(s): CH 3520

Course covers multivariate statistics such as ANCOVA, Multiple Regression, factor analysis, clustering, machine learning, and mixture modeling.

• Credits: 3.0; Repeatable to a Max of 12
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring - Offered alternate years beginning with the 2018-2019 academic year
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): PSY 5110

This course covers the design issues concerning the implementation of database management systems, including distributed databases. The topics include data storage, index implementation, query processing and optimization, security, concurrency control, transaction processing, and recovery.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: On Demand
• Pre-Requisite(s): CS 3425

Development and administration of secure software systems. Topics include principles of software development, practical cryptography, program security, operating system security, database security, administration, legal and ethical issues.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Restrictions: May not be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): CS 3411 or CS 4411

Fundamental ideas and techniques that are used in the construction of problem solvers that use Artificial Intelligence technology. Topics include knowledge representation and reasoning, problem solving, heuristics, search heuristics, inference mechanisms, and machine learning.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore
• Pre-Requisite(s): CS 2321 and CS 3311

Design and analysis of advanced algorithms. Topics include algorithms for complex data structures, probabilistic analysis, amortized analysis, approximation algorithms, and NP-completeness. Design and analysis of algorithms for string-matching and computational geometry are also covered.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Pre-Requisite(s): CS 4321

Advanced topics in the design, analysis, and performance evaluation of parallel algorithms. Topics include advanced techniques for algorithm analysis, memory models, run time systems, parallel architectures, and program design, particularly emphasizing the interactions of these factors.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): CS 4431 and CS 4331

Covers time and order in distributed systems; mutual exclusion, agreement, elections, and atomic transactions; Distributed File Systems, Distributed Shared Memory, Distributed System Security; and issues in programming distributed systems. Uses selected case studies.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Pre-Requisite(s): CS 4411

Introduction to Graphics Processing units (GPU) and multi-core systems, their architectural features and programming models, stream programming and compute unified driver architecture (CUDA), caching architectures, linear and non-linear programming, scientific computing on GPUs, sorting and search, stream mining, cryptography, and fixed and floating point operations.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall, Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): CS 3411 and CS 3421

Introduction to scientific and information visualization. Topics include methods for visualizing three-dimensional scalar and vector fields, visual data representations, tree and graph visualization, large-scale data analysis and visualization, and interface design and interaction techniques.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Pre-Requisite(s): CS 4611 or CS 5611

Current issues in human-computer interaction (HCI), evaluation of user interface (UI) design, and usability testing of UI. Course requires documenting UI design evaluation, UI testing, and writing and presenting a HCI survey, concept or topic paper.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): CS 4760

Course topics include current topics in artificial intelligence including agent-based systems, learning, planning, use of uncertainty in problem solving, reasoning, and belief systems.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): CS 4811

This course covers the four main paradigms of Computational Intelligence, viz., fuzzy systems, artificial neural networks, evolutionary computing, and swarm intelligence, and their integration to develop hybrid systems. Applications of Computational Intelligence include classification, regression, clustering, controls, robotics, etc.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: On Demand
• Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate

Introduction to Graphics Processing Units (GPU) and multi-cores, their architectural features and programming modesl, stream programming, and compute unified driver architecture (CUDA), caching architectures, linear and non-linear programming, scientific computing on GPUs, sorting and search, stream mining, cryptography, and fixed and floating point operations.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall, Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): CS 3411 and EE 4173

Theory of probability, random variables, and stochastic processes, with applications in electrical and computer engineering. Probability measure and probability spaces. Random variables, distributions, expectations. Random vectors and sequences. Stochastic processes, including Gaussian and Poisson processes. Stochastic processes in linear systems. Markov chains and related topics.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Major(s): Electrical Engineering, Electrical Engineering; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior

Detecting and estimating signals in the presence of noise. Optimal receiver design. Applications in communications, signal processing, and radar.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Electrical Engineering, Computer Engineering
• Pre-Requisite(s): EE 5500

Introduces the concepts of wireless sensor networks. Topics include sensor network coverage and sensor deployment, time synchronization and sensor node localization, network protocols, data storage and very, collaborative signal processing. Introduce sensor network programming network reliability and tolerance.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: On Demand
• Pre-Requisite(s): (CS 4461 or EE 4272 or EE 5722) and (EE 3170 or EE 3173) and (CS 1129 or CS 2141)

This course covers the four main paradigms of Computational Intelligence, viz., fuzzy systems, artificial neural networks, evolutionary computing, and swarm intelligence, and their integration to develop hybrid systems. Applications of Computational Intelligence include classification, regression, clustering, controls, robotics, etc.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: On Demand
• Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate

This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): CS 4090

This course is designed for graduate students majoring in forestry, wildlife, ecology, and natural resource management and data science to develop fundamental but essential skills for data presentation and visualization through generating informative graphs with R.

• Credits: 2.0
• Lec-Rec-Lab: (1-0-2)
• Semesters Offered: Spring - Offered alternate years beginning with the 2017-2018 academic year
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Regression as a tool for the analysis of forest and environmental science data. Topics include multiple linear, curvilinear and non-linear regression, hierarchical and grouped data and mixed-effects models. Emphasis is placed on application of tools to real-world data.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring - Offered alternate years beginning with the 2018-2019 academic year
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Use of R for basic data manipulation, statistical summary and regression. Topics include installing R, data import and export, basic statistics, graphics and fitting of linear, non-linear and mixed-effects models.

• Credits: 1.0
• Lec-Rec-Lab: (0-1-0)
• Semesters Offered: Spring - Offered alternate years beginning with the 2018-2019 academic year
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Co-Requisite(s): FW 5411

Remote sensing principles and concepts. Topics include camera and digital sensor arrays, types of imagery, digital data structures, spectral reflectance curves, applications, and introductory digital image processing.

• Credits: 3.0
• Lec-Rec-Lab: (2-1-0)
• Semesters Offered: Fall
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Co-Requisite(s): FW 5541

Use of geographic information systems (GIS) in resource management. Studies various components of GIS in detail, as well as costs and benefits. Laboratory exercises use ArcGIS software package to solve resource management problems.

• Credits: 4.0
• Lec-Rec-Lab: (3-0-3)
• Semesters Offered: Fall
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710

This course moves beyond the fundamentals of GIS to explore the application of GIS technology to environmental monitoring and resource management issues. Students learn graphic modeling techniques, network analysis, 3D visualization, geodatabase construction and management, and multivariate spatial analysis.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): FW 5550

Course provides exposure to data collection techniques, web mapping applications, and advanced database structures. Students will investigate GIS system design, GIS project planning and data management, learn map atlas creation and cartographic techniques, and discuss geospatial ethics.

• Credits: 3.0
• Lec-Rec-Lab: (1-0-4)
• Semesters Offered: Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): FW 5550

Presents the theory and quantitative procedures of digital image processing using remotely sensed data. Emphasizes image acquisition, preprocessing, enhancement, transformation classification techniques, accuracy assessment, and out-products. Discusses linkages to GIS. Also covers evaluating applications of the technology to current resource management problems via peer-reviewed literature.

• Credits: 4.0
• Lec-Rec-Lab: (3-0-3)
• Semesters Offered: Spring - Offered alternate years beginning with the 2016-2017 academic year
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): FW 5540

Exploration of how to develop comprehensive plans to mitigate the impact of natural hazards on humans. Requires a project and report.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: On Demand
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Will prepare students, including those with no seismology background, to interpret seismic and acoustic signals from volcanoes. Topics: basic seismology, monitoring techniques, tectonic and volcanic earthquakes, infrasound, deformation over a range of time scales.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-1)
• Semesters Offered: Spring
• Pre-Requisite(s): (MA 1160 or MA 1161 or MA 1135) and GE 2000 and PH 2100

Introduction to quantitative analysis and display of geologic data using R/Matlab, covering basic R/Matlab syntax and programming, and analysis of one-dimensional (e.g. time series) and two-dimensional datasets (e.g. spatial data). Techniques are applied to geological datasets. Requires an in-depth project, report, and presentation.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-1)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Principles and application of reflection seismic techniques. Includes acquisition, data processing, and 2D/3D data interpretation. Project and report required.

• Credits: 3.0
• Lec-Rec-Lab: (2-1-0)
• Semesters Offered: On Demand
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

This course covers the handling of spatial and temporal data for knowledge discovery. Major topics include spatial interpolation, clustering, association analysis, and supervised and unsupervised classification. Students will learn how to use geostatistical and pattern recognition tools for geoscience applications.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-1)
• Semesters Offered: Fall
• Pre-Requisite(s): GE 3250

A study of fundamental ideas in linear algebra and its applications. Includes review of basic operations, block computations; eigensystems of normal matrices; canonical forms and factorizations; singular value decompositions, pseudo inverses, least-square applications; matrix exponentials and linear systems of ODEs; quadratic forms, extremal properties, and bilinear forms.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): (MA 2320 or MA 2321 or MA 2330) and MA 3160

Covers simple, multiple, and polynomial regression; estimation, testing, and prediction; weighted least squares, matrix approach, dummy variables, multicollinearity, model diagnostics and variable selection. A statistical computing package is an integral part of the course.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715

Covers construction and analysis of completely randomized, randomized block, incomplete block, Latin squares, factorial, fractional factorial, nested and split-plot designs. Also examines fixed, random and mixed effects models and multiple comparisons and contrasts. The SAS statistical package is an integral part of the course.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715

Application, construction, and evaluation of statistical models used for prediction and classification. Topics include data visualization and exploratory methods, the normal theory regression model, logistic and Poisson regression, linear and quadratic discriminant analysis, and classification with logit models.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MA 3740 or MA 4710 or MA 4720 or MA 4780

Basic algorithmic and computational methods used in the solution of fundamental combinatorial problems. Topics may include but are not limited to backtracking, hill-climbing, combinatorial optimization, linear and integer programming, and network analysis.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall - Offered alternate years beginning with the 2010-2011 academic year
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Review of basic graph theory followed by one or more advanced topics which may include topological graph theory, algebraic graph theory, graph decomposition or graph coloring.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall - Offered alternate years beginning with the 2003-2004 academic year
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): MA 5301 or MA 4209

A graduate-level study of the Lebesgue integral including its comparison with the Riemann integral; the Lebesgue measure, measurable functions and measurable sets. Integrable functions, the monotone convergence theorem, the dominated convergence theorem, and Fatou's lemma.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall - Offered alternate years beginning with the 2005-2006 academic year
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Design and analysis of algorithms for the numerical solution of systems of linear algebraic equations, least-square problems, and eigenvalue problems. Direct and iterative methods will be covered.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): MA 4330 or MA 4630

Numerical solution of unconstrained and constrained optimization problems and nonlinear equations. Topics include optimality conditions, local convergence of Newton and Quasi-Newton methods, line search and trust region globalization techniques, quadratic penalty and augmented Lagrangian methods for equality-constrained problems, logarithmic barrier method for inequality-constrained problems, and Sequential Quadratic Programming.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring - Offered alternate years beginning with the 2002-2003 academic year
• Pre-Requisite(s): MA 4330 or MA 4610 or MA 4630 or MA 5627

Introduction to design, conduct, and analysis of statistical studies, with an introduction to statistical computing and preparation of statistical reports. Topics include design, descriptive, and graphical methods, probability models, parameter estimation and hypothesis testing.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Random vectors and matrix algebra. Multivariate Normal distribution. Theory and application of multivariate techniques including discrimination and classification, clustering, principal components, canonical correlation, and factor analysis.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): (MA 4710 or MA 4720) and MA 2320

Application of statistical methods to solve problems in genetics such as locating genes. Topics include basic concepts of genetics, linkage analysis and association studies of family data, association tests based on population samples (for both qualitative and quantitative traits), gene mapping methods based on family data and population samples.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring - Offered alternate years beginning with the 2015-2016 academic year

Introduction to computationally intensive statistical methods. Topics include resampling methods, Montes Carlo simulation methods, smoothing technique to estimate functions, and methods to explore data structure. This course will use the statistical software S-plus.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MA 4770(C)

Statistical modeling and inference for analyzing experimental data that have been observed at different points in time. Topics include models for stationary and non stationary time series, model specification, parametric estimation, model diagnostics and forecasting, seasonal models and time series regression models.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): (MA 2710 or MA 2720 or MA 3710 or MA 3715) and MA 3720

Structure of 2-way contingency tables. Goodness-of-fit tests and Fisher's exact test for categorical data. Fitting models, including logistic regression, logit models, probit and extreme value models for binary response variables. Building and applying log linear models for contingency tables.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring - Offered alternate years beginning with the 2005-2006 academic year

An overview of numerical and computer methods to analyze and visualize physics problems in mechanics, electromagnetism, and quantum mechanics. Utility and potential pitfalls of these methods, basic concepts of programming, UNIX computing environment, system libraries and computer graphics are included.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: Fall
• Pre-Requisite(s): PH 2020 and PH 3410

An introduction to computational methods for the social sciences. The course provides an introduction to complexity theory and Agent-Based Modeling. Students will apply what they have learned in this course to develop a pilot simulation to understand any social phenomena of their choosing.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: On Demand
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore

This course investigates relationships between the world's population, population change, population distribution, resource consumption, and environmental and social consequences. Addresses local and global relationships and the population processes (mortality, fertility, and migration) involved.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Pre-Requisite(s): SS 5400(C) or SS 3760 or FW 3760

Course covers fundamental subjects such as medical decision support systems, telemedicine, medical ethics and biostatistics. Topics include consumer health informatics, international health care systems, global health informatics, translational research informatics and homecare. Students will see medical informatics from diverse perspectives. Scientific writing and communication will be encouraged.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Students will develop problem solving skills through the application of a commonly used high-level programming language. Topics include: nature of the programming environment; fundamentals of programming languages; structured programming concepts; object-oriented programming concepts; desirable programming practices and design; and debugging and testing techniques.

• Credits: 3.0
• Lec-Rec-Lab: (0-2-1)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Course provides basic concepts in medicine and human pathophysiology to introduce a molecular understanding of human metabolism and disease. Topics also include physical examination of patient, taking medical history, laboratory medicine, disease management and treatment, medical diagnostics, clinical workflow, and medical special/subspecialities.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Course addresses complex medical decisions, evidence-based medicine, disease management and comprehensive laboratory informatics. Topics include improving physical order entry and healthcare, using medical literature, clinical case discussions, meaningful use of medical data, enhancing patient and care-giver education, disease prevention, and public health and environmental health informatics.

• Credits: 3.0
• Lec-Rec-Lab: (0-2-1)
• Semesters Offered: Fall, Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Identifies database solutions and key elements of an enterprise data warehouse. Explains how to apply best practices for development of data warehouses, the role of business intelligence and data mining in supporting the strategic business decision process, and OLAP (Online Analytical Processing) and its use in reporting and analyzing database and data warehouse information. Defines security practices for a data warehouse environment.

• Credits: 3.0
• Lec-Rec-Lab: (0-2-1)
• Semesters Offered: Fall, Summer
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): SAT 3210

Provides an overview of techniques used in the design of secure systems with a primary focus on real-world case studies. Students will examine attacks on deployed systems and investigate how these vulnerabilities have been addressed. Practical advantages and shortcomings of several notions of provable security will also be examined. Students will be expected to read, understand, and present recent research papers.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): SAT 5111

An introduction to the building and administration of web applications. Topics covered include: Apache web server development; Tomcat application server; HTML; cascading style sheets; JavaScript; JQuery; server side includes; server side application development; web services; SSL/TLS; and authentication/authorization.

• Credits: 3.0
• Lec-Rec-Lab: (0-2-2)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): SAT 5002

High-level review of geospatial data acquisition systems, sensors and associated processing technologies. Course considers geospatial metadata generation principles, interoperability, and major tools for manipulation with geospatial data. Course may help in transition of non-geospatial majors to geospatial field.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: On Demand
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Fundamentals of GIS data, aerial photographs, satellite imagery, and airborne/terrestrial laser scanning data. Characteristics of remotely sensed data and the information that can be extracted. Term project on how to combine and fuse to a specific application.

• Credits: 3.0
• Lec-Rec-Lab: (0-2-1)
• Semesters Offered: On Demand
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Set in a Linux environment, course offers exposure to Foss tools for developing computational and visualization workflows. Students will learn to translate problems into programs, understand sources of errors, and debug, improve the performance of and parallelize the code.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall, Spring
• Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate

Course provides an introduction to Big Data concepts, with focus on data management, date modeling, visualization, security, cloud computing, and data science from different perspectives: computer science, business, social science, bioinformatics, engineering, etc. Course introduces tools for data analytics such as SPSS Modeler, R, SAS, Python, and MATLAB. Two case study projects which are integrated with communication and business skills.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science