Data Science MS Course Information

Fundamental topics in algorithm design, analysis, and implementation. Analysis fundamentals include asymptotic notation, analysis of control structures, solving recurrences, and amortized analysis. Design and implementation topics include sorting, searching, and graph algorithms. Design paradigms include greedy algorithms, divide-and-conquer algorithms, and dynamic programming.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore
• Pre-Requisite(s): (CS 2311 or MA 3210) and CS 2321

The course covers classical statistical machine learning. Topics include classification (k-nearest neighbors, decision trees, random forests, logistic regression, naive bayes, neural networks), regression, clustering (partitional and hierarchical clustering), and evaluation.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): DATA 2201 and (MA 2710 or MA 2720 or MA 3710 or MA 3720)

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

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

Data mining focuses on extracting knowledge from large data sources. The course covers data mining concepts, methodology algorithms (e.g. classification, clustering, association rules) and several applications (e.g. text mining, recommender systems, etc.)

• 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): DATA 2201 and (MA 2710 or MA 2720 or MA 3710)

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: Permission of instructor required; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): CS 4801

Students will study computer systems that process and understand human language. We focus on state-of-the-art natural language processing (NLP) algorithms that are trained on data. Students will read, analyze, and present current research in NLP.

• 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): CS 5841

This course introduces students to clinical data and artificial intelligence (A1) methods in healthcare. Health AI topics such as machine learning, deep learning, risk prediction, medical image analysis, natural language processing of clinical text, computer vision, and the integration of AI, bias in algorithm development, bioethics, and regulation into the clinical environment are covered.

• 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 4650

Course will cover concepts and techniques used to analyze big data. We will cover the most important big data processing frameworks (e.g. Hadoop, spark) and GPU techniques. The students will acquire the knowledge of Hadoop architecture, MapReduce, Spark and the capability of programming to analyze big data.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): SAT 4650

Introduces the general concepts and algorithms of machine learning (ML) with their implementation and applications to practical problems of modeling, detection, estimation, prediction, and control. Applications include cybersecurity, healthcare, robot vision, remote sensing, automation, and natural language processing.

• 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
• Pre-Requisite(s): SAT 4310 or SAT 4650

Set in a Linux environment, students will learn to design computational workflows, translate problems into programs, understand sources of errors, and debug, profile and parallelize the code. Successful completion of FOSS101 and earning its Digital Badge are required prior to registration

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

Introduces disruptive innovation concepts and provides occasions for their application to timely and relevant cases. Provides an understanding of technology management and innovation processes as they occur inside and outside of organizations.

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

Focuses on project definition, selection, planning, scheduling, implementation, performance monitoring, evaluation and control. Emphasis will be on product, service and process development and emerging concepts related to development on the internet. Some advanced concepts in resource constraint management and design matrix are included.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring, Summer
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or EET 2010 or CEE 3710 or BUS 2100

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): Data Science, Engineering Management, Applied Natural Resource Econ., Accounting, Business Administration

Focuses on understanding IT for competitive advantage and as an agent of transformation. Topics include managing IT infrastructure and architecture, facilitating information distribution throughout the enterprise, business applications for machine learning and artificial intelligence, and other emerging trends and technologies.

• 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): (MIS 2100 and MIS 3200(C)) or (CS 2321 and CS 3141) or BA 5200 or UN 5550

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. Some prior experience with R is expected.

• 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 or MA 5701

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, Spring, Summer
• 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

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

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MA 4770(C) or (MA 4700 and MA 5701)

The theory of Bayesian inference. Topics include prior specifications, basics of decision theory, Markov chain, Monte Carlo, Bayes factor, linear regression, linear random effects model, hierarchical models, Bayesian hypothesis testing, Bayesian model selection.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, in even years
• Pre-Requisite(s): MA 4330 and MA 4710 and MA 4760

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 or MA 5701) and (MA 3720 or EE 3180 or MA 4700)

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, in odd years

Using statistical tools to analyze data from ecology, forestry and environmental science. Topics include multiple linear, curvilinear and non-linear regression, hierarchical grouped data and mixed-effects models. Emphasis is placed on application of tools to real-world data using R.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Co-Requisite(s): FW 5412

Use of R for basic data manipulation, statistical summary and statistical analysis. Topics include installing R, data import, handling and manipulation, basic statistics, graphical outputs and fitting of linear, non-linear and mixed-effects models.

• Credits: 1.0
• Lec-Rec-Lab: (0-1-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Co-Requisite(s): FW 5411

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: (0-2-2)
• Semesters Offered: Fall, in even years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

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, and translational research informatics. Students will see medical informatics from a diverse scope of healthcare industry organizations. Scientific writing and communication will be encouraged.

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

Course addresses complex medical decisions, evidence-based medicine, disease management and comprehensive laboratory informatics. Topics include correlation, differential diagnosis, disease progression, precision medicine, telemedicine, machine learning, deep learning, computer vision, WLP, data preprocessing, AI modeling to improve patient outcomes, and safety.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): SAT 4650

Course will consist of the legal and regulatory requirements and security privacy concept principles regarding data management. Best practices of how organizations manage information risk through risk assessment practices and procedures will be conducted.

• 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 College(s): College of Computing, College of Engineering, College of Business

This course explores the foundations of population health informatics, including information architecture, data standards and confidentiality. We will examine key concepts related to registries, electronic health records, epidemiological databases, biosurveillance, health promotion, and quality reporting in population health management.

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

Study of artificial intelligence and machine learning in cybersecurity. Topics include fundamentals of common machine learning and deep learning algorithms, intelligent threat detection and analysis, user behavior analytics, machine learning in hacking, and automated cybersecurity systems.

• 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 3812 and SAT 4310

This course teaches how to quantify data out of images, typically from optical microscopes. It covers thresholding, image derivatives, edge-detection, watershed, multi-scale and steerable filters, 3D image processing, feature extraction, PCA, classification, convolutional neural networks, particle tracking, and diffusion analysis.

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

Study of financial statement analysis and concepts of valuation utilizing accounting based financial information. Methods are applied to encompass decision making, communication, and judgment using problems, cases, and projects.

• 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): Accounting Analytics, Accounting, Forensic Accounting, Accounting and Analytics; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior

This class covers the collection, reporting, and analysis of financial information with emphasis on the use of that information to support decision making.

• 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., Business Administration, Engineering Management, Accounting and Analytics, Accounting

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): Data Science, Engineering Management, Applied Natural Resource Econ., Accounting, Business Administration

Focuses on project definition, selection, planning, scheduling, implementation, performance monitoring, evaluation and control. Emphasis will be on product, service and process development and emerging concepts related to development on the internet. Some advanced concepts in resource constraint management and design matrix are included.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring, Summer
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or EET 2010 or CEE 3710 or BUS 2100

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): Data Science, Engineering Management, Applied Natural Resource Econ., Accounting, 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 or MA 1121)

Analysis of asset and liability management of financial institutions and the role of financial institutions in the U.S. and international economy.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Pre-Requisite(s): (EC 3003 or FIN 3000)

Introduction to the principles of finance. Topics include financial mathematics, the capital investment decision, financial assets valuation, and the risk-return relationship

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall, Spring, Summer
• Pre-Requisite(s): ACC 2000 and (MA 1020 or MA 1030 or MA 1031 or MA 1032 or MA 1120 or MA 1135 or MA 1160 or MA 1161 or MA 1121 or MA 2160 or ALEKS Math Placement >= 61 or CEEB Calculus AB >= 2 or CEEB Calculus BC >= 2 or ACT Mathematics >= 22 or SAT MATH SECTION SCORE-M16 >= 540)

Covers the pricing and use of options, financial futures, swaps, and other derivative securities.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): EC 3400 or FIN 3000 and (MA 2710 or MA 2720 or MA 3710)

Develops an entrepreneurial mindset and a personal toolkit of methods and practices that enables students to create and evaluate entrepreneurial opportunities, marshal resources, and engage in entrepreneurial teams driven by creativity, leadership, smart action, and innovation.

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

Provides an understanding of the IS development and modification process and the evaluation choices of a system development methodology. Emphasizes effective communication with users and team members and others associated with the development and maintenance of the information system. Stresses analysis and logical design of departmental-level information system.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): MIS 2100 or CS 1122 or CS 1131

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 and processing data using appropriate programming languages. Introduces concepts related to data pipelining.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): (MIS 2100 or CS 1122 or CS 1131) and (MIS 3100 or CS 3425)

Examines current IS/IT topics and issues in greater depth from a managerial perspective. A single offering of this course will concentrate on one or two topics, which will vary.

• Credits: 3.0; Repeatable to a Max of 6
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: On Demand
• Pre-Requisite(s): MIS 2000 or MIS 2100 or CS 1122 or CS 1131

Introduces students to models, theories, practices, and sociocultural issues pertinent to consumers' decision making and lifestyle choices. Discussions will be based on a variety of disciplines: psychology, sociology, economics, and anthropology.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MKT 3000

Focuses on data-driven consumer insights for marketing decision-making. Topics include scientific research methodology, survey research, social media data-analysis, multivariate data analysis, information visualization, and report writing and presentations.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): (MA 2710 or MA 2720 or MA 3710 or BUS 2100) and MKT 3000

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)

The course focuses on the principles and applications of the methods for molecular modeling of large molecules. The students will learn the principles of molecular mechanics (MM), molecular dynamics (MD), combined quantum mechanics and molecular mechanics (QM/MM) and their applications for understanding molecular and biomolecular systems.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-2)
• Semesters Offered: Fall
• Pre-Requisite(s): CH 3510 and CH 2510

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: Spring, in odd years
• Pre-Requisite(s): 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

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
• Pre-Requisite(s): CH 3520

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

This covers fundamentals of computer security. Topics include practical cryptography, access control, security design principles, physical protections, malicious logic, program security, intrusion detection, 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

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

Quantum Computing (QC) with emphasis on computational aspects of physical quantum systems. Topics include classic vs. quantum computation, introduction to Quantum Mechanics (QM), quantum information, quantum gates and circuits, teleportation, reversible computation, Fourier sampling, Simon's algorithm, Grover's algorithm, Shor's algorithm.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Permission of instructor required; May not be enrolled in one of the following Class(es): Freshman, Sophomore
• Pre-Requisite(s): CS 4321

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 and CS 4461

This covers fundamentals of computer security. Topics include practical cryptography, access control, security design principles, physical protections, malicious logic, program security, intrusion detection, 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

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

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, Electrical & Computer Engineer; 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: Fall, Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering
• Pre-Requisite(s): EE 5500

Fundamentals of image processing are covered including image representation, geometric transformations, binary image processing, compression, space and frequency domain processing. Computer programming in MATLAB and Python required.

• 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; Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering

Sensing modes, signal and image processing for industrial robotic automation processes. Emphasis placed on widely used sensors, including cameras and 3-D sensors for process control and computer vision for autonomous navigation.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): EE 5522

This course is designed for students majoring in molecular biology, computer science, data science and related majors to develop fundamental but essential skills for manipulating, preprocessing, and analyzing high throughput gene expression data for pattern extraction and knowledge discovery.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: Fall, in even years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): FW 4099 or CS 1121 or CS 1122 or CS 1131 or CS 1141 or CS 2321

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, in odd years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Using statistical tools to analyze data from ecology, forestry and environmental science. Topics include multiple linear, curvilinear and non-linear regression, hierarchical grouped data and mixed-effects models. Emphasis is placed on application of tools to real-world data using R.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Co-Requisite(s): FW 5412

Use of R for basic data manipulation, statistical summary and statistical analysis. Topics include installing R, data import, handling and manipulation, basic statistics, graphical outputs and fitting of linear, non-linear and mixed-effects models.

• Credits: 1.0
• Lec-Rec-Lab: (0-1-0)
• Semesters Offered: Spring
• 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

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, Spring
• 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: (3-0-1)
• Semesters Offered: Spring, in odd years
• 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, in even years
• Pre-Requisite(s): (MA 1160 or MA 1161 or MA 1121 or MA 1135) and GE 2000 and PH 2100

Students will gain a solid foundation in time series analysis through theory and applications to geoscience data. Methods include regression, ARIMA models, linear Gaussian state-space models, and frequency-domain estimation. Work will be done in Python.

• 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): GE 5280

This advanced graduate-level course is designed to equip students with essential skills and techniques to perform geospatial data analysis using Python. This course emphasizes both theoretical concepts and hands-on practice, offering comprehensive insights into spatial data handling, visualization, statistical analysis, and application to real-world geospatial problems. Students will engage directly with real-world datasets through guided tutorials, comprehensive Jupyter notebook examples, and interactive classroom discussions.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-1)
• Semesters Offered: Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): GE 5280

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: Spring, in odd years

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 R programming language 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 or MA 5701

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, in even years
• 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, in odd years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): MA 5301 or MA 4209

Design and analysis of algorithms for problems in linear algebra. Covers floating point arithmetic, condition numbers, error analysis, solution of linear systems (direct and iterative methods), eigenvalue problems, least squares, and singular value decomposition. Includes the use of appropriate software including high performance computational libraries.

• 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, in even years
• Pre-Requisite(s): MA 4330 or MA 4610 or MA 4630 or MA 5627

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, in odd years

Construction, evaluation, and application of generalized linear models to analyze different types of data. Topics include logistic and Poisson regression, multinomial logit models, random effects and mixed effect models, models for repeated measures and longitudinal data. Introduce theory on GLM fitting, hypothesis testing, and diagnostic models.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: On Demand
• Pre-Requisite(s): MA 4710 and (MA 4760 or (MA 4700 and MA 5701))

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

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: Fall
• Pre-Requisite(s): (PH 2021 or PH 2020) and PH 3410

The course focuses on modern problem solving in Astronomy and Astrophysics through statistical inference, machine learning algorithms and data mining techniques. Students will be presented with data sets and research problems in astrophysics and will learn how to formulate solutions.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: Spring, in even years
• Pre-Requisite(s): PH 4390

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: (0-2-2)
• Semesters Offered: Spring, in odd years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): PSY 5110

Three, 4 hour workshops on advanced responsible conduct of research. Covers topics necessary for this training, including ethical standards, publication practices, peer review process, conflict of interest and societal expectations.

• Credits: 1.0
• Lec-Rec-Lab: (1-0-0)
• Semesters Offered: Summer
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Credits may count as free or technical electives based on academic department. Requires advisor approval, good conduct and academic standing, registration with Career Services, and an official offer letter from the employer.

• Credits: variable to 2.0
• Semesters Offered: Fall, Spring, Summer
• Restrictions: Permission of department required; Must be enrolled in one of the following Level(s): Graduate

Credits may count as free or technical electives based on academic department. Requires advisor approval, good conduct and academic standing, registration with Career Services, and an official offer letter from the employer.

• Credits: variable to 2.0
• Semesters Offered: Fall, Spring, Summer
• Restrictions: Permission of department required; Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): UN 5002

Credits may count as free or technical electives based on academic department. Requires advisor approval, good conduct and academic standing, registration with Career Services, and an official offer letter from the employer.

• Credits: variable to 2.0; May be repeated
• Semesters Offered: Fall, Spring, Summer
• Restrictions: Permission of department required; Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): UN 5003

Credits may count as free or technical electives based on academic department. Requires advisor approval, good conduct and academic standing, registration with Career Services, and an official offer letter from the employer.

• Credits: variable to 2.0; May be repeated
• Semesters Offered: Summer
• Restrictions: Permission of department required; Must be enrolled in one of the following Level(s): Graduate