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Accounting |
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ACG 2020 - Accounting for Managers
Credits: 3
Prerequisites: None
Course Description: This course is designed to enable the student to understand and apply the fundamental concepts and procedures of both financial and managerial accounting. Topics include basic accounting terminology, financial statement analysis and interpretation, internal control, cost behavior, cost-volume-profit analysis, budgeting, and the use of accounting data in making informed, ethical decisions..
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ACG 2021 - Principles of Financial Accounting
Credits: 3
Prerequisites: None
Course Description: This course is an introduction to the principles of financial accounting and focuses on wealth and income measurement and the preparation and interpretation of financial statements. The recording and reporting of financial activity, balance sheets, income statements, changes in equity, cash flows, underlying assets, liabilities, and equities are also presented.
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ACG 2071 - Principles of Managerial Accounting
Credits: 3
Prerequisites: ACG 2021 - Principles of Financial Accounting
Course Description: This course covers tools used by management for cost reporting and control including statements, analytical tools, and reports.
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American History |
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AMH 2010 - American History to 1877
Credits: 3
Prerequisites: None
Course Description: This course will survey American history from just prior to the initial exploration and settlement of the Americas to the period of Reconstruction. The course will discuss the English colonies in North America; the American Revolution; the United States Constitution; Antebellum America; the American Civil War; and Reconstruction. This course meets communication/writing-intensive requirements (W).
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AMH 2020 - American History Since 1877
Credits: 3
Prerequisites: None
Course Description: This course presents a survey of the emergence of modern America as an industrial and world power; the Progressive Era; WWI; the Great Depression and the New Deal; WWII; and the Cold War era. This course meets communication/writing-intensive requirements (W).
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AMH 2930 - Special Topics
Credits: 1-3
Prerequisites: None
Course Description: Selected topics in the history of the United States. Topic will be determined by the instructor. This course meets communication/writing-intensive requirements (W).
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Applied Information Technology |
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AIT 2XXX - Quantitative Methods 1
Credits: 3
Prerequisites: STA2032 Statistics & Probability for Science, Technology, and Engineering
Course Description:
| Advanced concepts in statistical analysis. Linear models and experimental design, multiple regression analysis, analysis of variance with multiple classification, analysis of covariance, repeated measures analysis of variance, multiple comparison techniques, and diagnostic procedures and transformations are discussed in this course. |
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Art History |
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ARH 2000 - Art Appreciation
Credits: 3
Prerequisites: None
Course Description: Introduction to the artistic experience through the examination of different ideas, approaches and purposes of art. This course meets communication/writing-intensive requirements (W).
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Astronomy |
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AST 3222 - Introduction to Astrophysics
Credits: 3
Prerequisites: PHY 2048 - Physics 1 with a C or better
Co-requisite or Prerequisite: PHY 2049 - Physics 2 or None
Course Description: Comprehensive survey of the universe and its appearance from earth seasons, tides, eclipses. The solar system, stellar evolution and galaxies, quasars, pulsars, black holes.
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AST 3271 - Astrophysics Laboratory
Credits: 1
Prerequisites: PHY 3101 - Introduction to Modern Physics and PHY 3101L - Modern Physics Laboratory
Course Description: An introduction to experiments methodology, data analysis and interpretation, calibration techniques, scientific model validation, data presentation and communication of results. The experiments are chosen for astrophysical relevance and include magnetic fields, optical interference and diffraction, wave polarization, line spectroscopy, photoelectric effect and radioactive decay.
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AST 4220 - Astrophysics 1
Credits: 3
Prerequisites: PHY 2049 - Physics 2 and PHY 2049L - Physics 2 Laboratory and AST 3222 - Introduction to Astrophysics
Course Description: This course introduces concepts and theories describing the physical and mathematical treatment of the properties of the Universe and the bodies within it, including the formation, structure, and evolution of stars, binary stars, stellar nucleosynthesis, white dwarfs, neutron stars, and black holes.
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AST 4221 - Astrophysics II
Credits: 3
Prerequisites: AST 4220 - Astrophysics 1
Course Description: The Physics of stellar objects: Classification of stars, nature of stellar spectra, Physics of stellar structure. The Sun, evolution of stars, neutron stars, black holes, binary systems. Galactic Astrophysics: Physics of the milky way, galactic structure, galactic evolution, large scale structure of the universe, active galaxies, cosmology, origin of the universe.
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Aviation Management |
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EAS 3101 - Fundamentals of Aerodynamics
Credits: 3
Prerequisites: EAS 4010 - Flight Performance Mechanics
Course Description: This course is an introductory course in to aerodynamics, which will apply the fundamental concepts of fluid mechanic to aerodynamic applications with both differential and control volume analysis. Topics covered include inviscid, incompressible flow over airfoils and finite wings, including boundary layer theory and two dimensional airfoil theory, as well as inviscid compressible flowing including normal, oblique and expansion shock waves.
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EAS 4010 - Flight Performance Mechanics
Credits: 3
Prerequisites: EGN 3321 - Dynamics and EGN 3331 - Strength of Materials
Course Description: This course is an introductory course into aircraft flight mechanics. Topics include introduction to airfoil theory, aircraft performance measures, wing design, lift/drag on wing performance, and an introduction to jet propulsion systems.
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EAS 4200 - Introduction to Aero Structures
Credits: 3
Prerequisites: EGN 3331 - Strength of Materials
Course Description: This course will apply the theories of mechanics introduced in EGN 3331 - Strength of Materials to aerospace structures. Topics covered will include stress analysis of aircraft components (i.e., wing spars/box beams, fuselages, wings), introduction to aero elasticity and the phenomena of wing flutter, structural/loading discontinuities in closed and open section beams, and bending, shear and torsion analysis in thin-walled beams.
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Biological Sciences |
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BSC 1010 - Biology 1
Credits: 3
Prerequisites: None
Co-requisite: BSC 1010L - Biology 1 Laboratory
Course Description: In this course students will study the chemistry of life, cell structure and function, photosynthesis, cellular respiration genetics, evolution, and the diversity of life. This course meets communication/writing-intensive requirements (W).
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BSC 1010L - Biology 1 Laboratory
Credits: 1
Prerequisites: None
Co-requisite: BSC 1010 - Biology 1
Course Description: Students will participate in laboratory experiments designed to reflect the topics presented in BSC 1010 - Biology 1 . This course meets communication/ writing-intensive requirements (W).
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BSC 1011 - Biology 2
Credits: 3
Prerequisites: BSC 1010 - Biology 1 and BSC 1010L - Biology 1 Laboratory
Co-requisite: BSC 1011L - Biology 2 Laboratory
Course Description:
In this course, students will study an introduction to ecology, evolution, biodiversity, and environmental science, history of life, forces that have shaped the diversity of life, changes in the biosphere due to human activity and how detrimental the impact of these changes may be.
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BSC 1011L - Biology 2 Laboratory
Credits: 1
Co-requisite: BSC 1011 - Biology 2
Course Description: Students will participate in laboratory experiments designed to reflect the topics presented in BSC 1011. This course meets communication/writing-intensive requirements (W).
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Biomedical Engineering |
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BME 4422 - The Biophysics of Neural Computation
Credits: 3
Prerequisites: MAP 4484 - Mathematical Modeling in Biology I
Course Description:
This course will discuss the biophysics of neuronal computation for both biological and artificial neural networks. It will provide a detailed introduction to: i) the anatomy/physiology of excitable cells, ii) the major brain architectures and principles, and iii) the most relevant mathematical models for neural computation from single neurons to circuits. Therefore, this course will prepare the students to understand the main principles by means of which our brains work and computers recognize patterns, learn/plan actions, and interact with humans
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Business Law |
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BUL 2241 - Law, Public Policy, Negotiation and Business
Credits: 3
Prerequisites: ENC 1101 - English Composition 1: Expository and Argumentative Writing
Course Description: The Structure of the legal system; administrative law and government regulation of business; and the legal environment of business including constitutional law, torts, contracts, and product liability are presented.
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Chemistry |
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CHM 2045 - Chemistry 1
Credits: 3
Prerequisites: None
Co-requisite or Prerequisite: Passing grade in CHM 1025
Co-requisite: CHM 2045L - Chemistry 1 Laboratory
Course Description: This course introduces the principles of chemistry and their applications based upon the study of physical and chemical properties of the elements. Topics covered in this class includes: stoichiometry, atomic and molecular structure, the states of matter, chemical bonding, thermochemistry, and gas laws.
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CHM 2045L - Chemistry 1 Laboratory
Credits: 1
Prerequisites: None
Co-requisite: CHM 2045 - Chemistry 1
Course Description: Students will participate in laboratory experiments designed to reflect the topics presented in CHM 2045 - Chemistry 1 . This course meets communication/writing-intensive requirements (W).
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CHM 2046L - Chemistry 2 Laboratory
Credits: 1
Prerequisites: None
Co-requisite: CHM 2046 - Chemistry 2
Course Description: Students will participate in laboratory experiments designed to reflect the topics presented in CHM 2046 - Chemistry 2 . This course meets communication/writing-intensive requirements (W).
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CHM 3217L - Organic Chemistry Laboratory (One Semester)
Credits: 1
Prerequisites: None
Co-requisite: CHM 3217 - Organic Chemistry (One Semester)
Course Description:
Students perform basic organic lab techniques. Synthesis, recrystallization, separations, extraction, chromatography, introduction to Nuclear Magnetic Resonance (NMR) and Infrared (IR) Spectroscopy.
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Computer Applications |
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CAP 1167 - First Year Experience
Credits: 1
Prerequisites: None
Course Description: This course is a weekly seminar designed to support freshman students in their transition to college. We hold meetings in and out of class for students to bring up any personal, academic or administrative concerns they have during their first semester in college. For the more advanced students, this course offers mentorship for those who wish to work on complex problems and projects early in their academic careers.
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CAP 3774 - Data Warehousing
Credits: 3
Prerequisites: COP 3710 - Database 1
Course Description: Fundamentals of building and populating a data mart to support the planning, designing and building of business intelligence applications and data analytics are covered in this course.
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CAP 4034 - Computer Animation
Credits: 3
Prerequisites: Computer Engineering: COP 3530 - Data Structures & Algorithms
Computer Science: COP 4415 - Data Structures and COP 4531 - Algorithm Design & Analysis
Course Description: Computer Animation is an introductory animation course that combines traditional animation principles with software based tools. Students will be introduced to the history and background of computer animation, animating primitive objects using a variety of techniques and tools, including keyframing and automating processes using procedural techniques. Students will also be introduced to behavioral animation processes, such as dynamics and simulation.
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CAP 4052 - Game Design and Development 1
Credits: 3
Prerequisites: CAP 4730 - Computer Graphics
Course Description: This is a technical course introducing the major tools used in game development and programming. Topics include stages of game development; development methodologies; scripting; game engines; game loading; programming input devices; multi-player design; mobile games; distribution and publishing.
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CAP 4056 - Game Design and Development 2
Credits: 3
Prerequisites: CAP 4052 - Game Design and Development 1
Course Description: This course builds upon CAP 4052 - Game Design and Development 1 . It is a hands-on, group- and project-based course. Students will use several game design aspects, different game engines, and a variety of software development kits. The focus of this course will be mainly on the technical aspects of game development with non-trivial programming projects employing different computer interaction technologies and digital media sources.
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CAP 4122 - Virtual Reality
Credits: 3
Prerequisites: CAP 4730 - Computer Graphics
Course Description: This course is to introduce students to the fundamentals of Virtual Reality (VR). The course topics include bird’s eye view, VR geometry, lights and optics, psychology of human vision, visual perception, visual rendering, motion, tracking, interaction, audio, and evaluation and experience.
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CAP 4410 - Computer Vision
Credits: 3
Prerequisites: (MAS 3114 - Computational Linear Algebra or MAS 3105 Linear Algebra ) and COP 3809C - Advanced Topics in Programming and
((COP 4415 - Data Structures and COP 4531 - Algorithm Design & Analysis ) or COP 3530 Data Structures & Algorithms )
Course Description: The course introduces how computers see and interpret the visual world and how this interpretation can be used to enhance game play experience. Topics covered: projections and coordinate systems, camera modeling, stereo vision, edge detection, filtering, segmentation, optical flow, motion vision, color vision, object representation, face recognition, object recognition.
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CAP 4453 - Robotics and Computer Vision
Credits: 3
Prerequisites: None
Course Description: This course discusses robotics, computer vision, image formation and analysis, rigid body and coordinate frame transformations, low level visions and edge detection, models for shading and illuminations, camera models, calibration, 3-D stereo reconstruction, epipolar geometry, fundamental matrices, object recognition and motion estimation.
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CAP 4612 - Machine Learning
Credits: 3
Prerequisites: COP 3530 - Data Structures & Algorithms , MAD 2104 - Discrete Mathematics
Course Description: An overview of computer system design, problem solving and procedural abstraction design of computer solutions, algorithm development using simple data types and control structures, implementation and testing of programmed problem solutions, design modularization using subprograms and structured and user-defined data types are presented in this course.
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CAP 4613 - Applied Deep Learning
Credits: 3
Prerequisites: COP 4415 - Data Structures and COP 4531 - Algorithm Design & Analysis
Course Description: The course introduces the students to the fundamentals of deep learning. Students will learn to extract layered high-level representations of data by applying deep learning algorithms.
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CAP 4630 - Artificial Intelligence
Credits: 3
Prerequisites: (STA 2023 - Statistics 1 or STA 3032 Probability and Statistics ) and (COP 3530 - Data Structures & Algorithms or COP 4415 - Data Structures ) and COP 4531 - Algorithm Design & Analysis .
Course Description: This course covers fundamental concepts such as search and knowledge representation and applied work in areas such as planning, game playing, and vision. Topics included: logical reasoning, constraint satisfaction problems, graph search algorithms, Bayes rule, Bayesian networks, multi-agent system, neural networks, decision trees, and natural language processing.
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CAP 4730 - Computer Graphics
Credits: 3
Prerequisites: Computer Engineering majors: COP 3530 - Data Structures & Algorithms
Computer Science and Data Analytics majors: COP 4415 Data Structures and COP 4531 Algorithm Design & Analysis
Course Description: The objective of this course is to establish a foundation in two- and three-dimensional computer rendering algorithms and display devices. Topics included: Geometric transformations, homogeneous coordinates, anti-aliasing, color vision, ray tracing, surface modeling, texture mapping, polyhedral representations, and reflectance models.
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CAP 4733 - Systems Acquisition, Integration, and Implementation
Credits: 3
Prerequisites: Senior-level status or Program Coordinator Permission.
Course Description: This advanced course covers critical thinking and problem solving techniques applied to information systems. Fundamentals and complexities associated with learning and applying the methods for creating a system development and implementation (S-DIP) plan are also covered. The course includes materials and exercises for students to develop skills in the domain of systems acquisition, integration, and implementation through applied system development and planning, and application lifecycle management.
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CAP 4763 - Time Series Modeling and Forecasting
Credits: 3
Prerequisites: QMB 3200 Advanced Quantitative Methods
Course Description: The course covers modeling stochastic time series, building and evaluating forecasts, including appropriate techniques for cross validation in a time series context. Applications to issues in business, economics, and healthcare will be used.
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CAP 4764 - Advanced Topics 2: Data Center Design & Large Data Sets
Credits: 3
Prerequisites: CAP 4763 - Time Series Modeling and Forecasting
Course Description: Application of a variety of analytical tools from the curriculum used for solving real world problems, with the focus on identifying the problem, constructing an appropriate model, and finding the best available method to solve it. Topics include data center design, working with large data sets and mining information from text, including scalable supervised and unsupervised machine learning methods, visualization and extraction. Other topics covered include: Discovering heuristic rules from large data sets, handling special data types, creating methods, procedures and models for extracting and sorting large amounts of unstructured data.
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CAP 4786 - Topics in Big Data Analytics
Credits: 3
Prerequisites: COP 3710 - Database 1 and MAS 3114 - Computational Linear Algebra
Course Description: This course provides the fundamental knowledge to capture and analyze all sorts of large-scale data from a variety of fields, such as people behavior, sensors, biological signals, finance, and more. Platforms for data storage system and distributed processing of large data sets, Hadoop HDFS and MapReduce, Spark, and others, and different ways of handling analytics algorithms on different platforms will be introduced.
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CAP 4793 - Advanced Data Science
Credits: 3
Prerequisites: CAP 4770 - Data Mining & Text Mining and COP 3710 - Database 1
Course Description: This course introduces advanced concepts, methodologies and techniques in relation to data science including novel learning approaches, deep learning, reinforcement learning, novel data mining methodologies and emerging modes of data acquisition and aggregation. The course will develop students’ understanding of data mining concepts and their ability to carry out advanced data science projects.
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CAP 4830 - Modeling and Simulation
Credits: 3
Prerequisites: STA 2023 - Statistics 1 and COP 3809C - Advanced Topics in Programming
Course Description: The course will introduce the concepts of continuous and discrete event system simulation. The focus of the course will be discrete event simulation. In this course, the students will learn the basic definitions, Modeling and Simulation paradigms, design techniques, and applications.
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CAP 5320 - Data Wrangling and Exploratory Data Analysis
Credits: 3
Prerequisites: COP 5090 - Scientific Computation and Programming or an equivalent programming course
Course Description: Preprocessing tasks often consume a large fraction of time in computational projects, and all downstream analyses depend on them. In this course, students will develop practical skills for working with large datasets. Topics will include common methods for gathering, organizing, and reshaping structured and unstructured data. We will also cover methods of exploratory data analysis that are useful to guide more focused questions and models. These include principles of information display, simple model forms and data reduction, common visualization methods, and reporting tools.
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CAP 5410 - Advanced Computer Vision
Credits: 3
Prerequisites: Graduate Standing
Course Description: The course focuses on advance computer vision topics (image filtering, edge detection, interest point detectors, segmentation, optical flow, motion vision, color vision, object representation, face recognition, object recognition). This course will expose graduate students to innovative research.
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CAP 5431 - Medical Imaging Informatics
Credits: 3
Prerequisites: Graduate Standing
Course Description: This course provides a broad and practical introduction to the major techniques employed in medical image processing: 3D and 4D medical imaging modalities, dilation and erosion, segmentation and thresholding, denoising, direct space filter kernels, Fourier-based filters, matching and morphing, artificial neural networks, self-organizing maps, principal component analysis. The course will be useful for graduate students in biomedical computing who wish to learn state of the art data mining and image vision techniques. Medical imaging related policies (DICOM, HIPPA compliance, data sharing) are also discussed.
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CAP 5630 - Artificial Intelligence
Credits: 3
Prerequisites: Graduate Standing
Course Description: This course expands on fundamental concepts such as search and knowledge representation and applied work in areas such as planning, game playing, and vision. Topics included: logical reasoning, constraint satisfaction problems, graph search algorithms, Bayes rule, Bayesian networks, multi-agent system, neural networks, decision trees, and natural language processing.
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CAP 5734 - Systems Acquisition, Integration and Implementation
Credits: 3
Prerequisites: None
Course Description: This course provides a study of the software acquisition process. The following subjects are discussed: SDLC, CMM, Cost Estimation, Risk Assessment and Management, Quality, Testing, Traceability, ERP, and Business Process Modeling. The course emphasizes methods and techniques for analyzing alternative software solutions to meet organizational needs. This course is part of the COIT core for all graduate students.
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CAP 5735 - Data Visualization and Reproducible Research
Credits: 3
Prerequisites: None
Course Description: A project-centered introduction to the visual display of quantitative information for both knowledge discovery and the communication of results. Fundamentals of reproducible research with attention to best practices and modern frameworks for data science project collaborations.
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CAP 5765 - Computational Data Analysis
Credits: 3
Prerequisites: STA 2023 - Statistics 1
Course Description: This course explores advanced topics in statistical data analysis and computability. It prepares students to perform big data analysis and organization using machine learning and data mining techniques and algorithms. Topics include: multivariate statistical methods, computational statistics, classification, clustering, prediction, regression analysis, and principal components analysis.
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CAP 5770 - Data Mining & Text Mining
Credits: 3
Course Description: This advanced course addresses the knowledge discovery process and the use of data mining concepts and tools as part of that process. In depth analysis of processes for automatically extracting valid, useful, and previously unknown information from data sources and using the information to make decisions is also covered.
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CAP 5771 - Data Mining & Text Mining
Credits: 3
Prerequisites: None
Course Description: This advanced course addresses the knowledge discovery process and the use of data mining concepts and tools as part of that process. In depth analysis of processes for automatically extracting valid, useful, and previously unknown information from data sources and using the information to make decisions is also covered.
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CAP 5774 - Data Warehousing
Credits: 3
Prerequisites: None
Course Description: Advanced techniques for building and populating a data mart to support the planning, designing and building of business intelligence applications and data analytics are covered in this course, along with data governance, stewardship, and security.
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CAP 5781 - Complex Modeling, Forecasting Techniques and Web Analytics
Credits: 3
Prerequisites: COP 3729C - Database 2 and CAP 4770 - Data Mining & Text Mining and CAP 3774 - Data Warehousing and MAD 2104 - Discrete Mathematics
Course Description: Applying Big Data analysis techniques to the complexities of emergent and predictive analytics such as marketing issues, financial reporting, and stock market trading schemes. Advanced application of knowledge discovery, CRM systems, and modeling for trends and predictions are covered. Emphasis on mathematical methods such as deterministic and probabilistic operations research models for decision problems. Complex applications of web analytics are also covered.
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CAP 5830 - Modeling and Simulation
Credits: 3
Prerequisites: Graduate Standing. Strong Programming (e.g. Programming 2) and Statistics 1 background.
Course Description: The course will introduce the concepts of continuous and discrete event system simulation. The focus of the course will be discrete event simulation. In this course, the students will learn the basic definitions, Modeling and Simulation paradigms, design techniques, and applications. The students will read and critique state of the art research papers about the covered topics.
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Computer Design/Architecture |
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CDA 2108 - Introduction to Computer Systems
Credits: 3
Prerequisites: COP 3337C - Object Oriented Programming
Course Description: This course provides an introduction to logic design and the basic building blocks of digital computers. The course will cover logic gates, some minimization techniques, arithmetic circuits, flip-flops, synthesis of sequential circuits, finite state machines, counters, registers, Random Access Memory (RAM), and Arithmetic Logic Unit (ALU).
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CDA 3100 - Computer Architecture
Credits: 3
Prerequisites: COP 3337C - Object Oriented Programming and STA 2023 - Statistics 1
Course Description: This course discusses the background and principles of computer architecture and assembly language including the following: computer organization, computer performance, assembly language and machine code, computer arithmetic, multi-processor and distributed architectures, ALU design, datapath and control, pipelining, memory hierarchy, I/O devices, graphics, mobile and multi-core processors.
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CDA 3631C - Embedded Operating Systems
Credits: 3
Prerequisites: CDA 3100 - Computer Architecture or EEL 4768C - Computer Architecture and Organization
Course Description: Embedded Operating Systems or Real time operating systems are operating systems are designed to be compact, efficient, and reliable. Topics discussed include embedded architectures, interaction with devices, concurrency, real-time principles, implementation trade-offs, profiling and code optimization, and embedded software.
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CDA 4210 - VLSI Design
Credits: 3
Prerequisites: EEL 4768C - Computer Architecture and Organization and EEE 3310 - Digital Electronics
Course Description: Topics discussed in this course include CMOS technology, MOSFET timing analysis, dynamic clocked logic, and layout design rules. Digital VLSI chip design is introduced. Computer-aided design software tools and elementary circuit design will be used. Cell library construction.
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CDA 4910 - Directed Research
Credits: 3
Prerequisites: Senior-Level Status or Program Coordinator Permission
Course Description: Students will conduct intensive research and produce significant written documentation of an experiment, research exploration, or special interest project in technology. This course meets communication/writing-intensive requirements (W).
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CDA 5653 - Advanced Concepts in Virtualization 2
Credits: 3
Prerequisites: Permission from VP of Academic Affairs or Designee
Course Description: This is an applied course in the principles, methods, and technologies of Cloud Computing. Upon completion of this course students should be able to create, configure, build, deploy and manage a variety of cloud based solutions.
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Computer General Studies |
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CGS 1100C - Applications for Business
Credits: 2
Prerequisites: None
Course Description: Using technology to improve global business performance as well as the creation of value for organizations by using business process innovations and technology are discussed. Hardware concepts, operating systems, word processing, spread sheets, database, project management, networks, internet, World Wide Web, multimedia presentations, information systems, and other microcomputer hardware and software applications that are typically used in the work place are presented.
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CGS 5096 - Advanced Legal, Ethical and Management Issues in Technology
Credits: 3
Prerequisites: None
Course Description: This course covers legal, ethical and regulatory issues present in the domain of applied technology. Students will learn to apply SWOT, PEST, and SEEC methods to analyze situations based on social, political, environmental, cultural and economic criteria. This course is designed to emphasize moral and ethical training for students to be well prepared for the business environment.
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CGS 5367 - Advanced Applied Information
Credits: 3
Prerequisites: Knowledge of database fundamentals, Statistics and modeling, computer programming.
Course Description: This course covers historical and modern approaches of knowledge management (KM), knowledge discovery (KD), data analytics (DA), and information retrieval (IR). This is an advanced level course and is focused on direct application of the principles and methods of information science theories and model building. Students are exposed to a variety of commercial data analytic tools for visualization and analysis techniques.
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Computer Networks |
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CNT 3004C - Introduction to Computer Networks
Credits: 3
Prerequisites: COP 3337C - Object Oriented Programming and (STA 2023 - Statistics 1 or STA 3032 Probability and Statistics )
Course Description: This course provides an introduction to fundamental concepts in computer networks, including their design and implementation. Topics covered include all seven layers of OSI Reference Model, network protocols (providing reliability and congestion control), routing, and link access. Special attention is also paid to wireless networks and security.
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CNT 3200 - Distributed Information Systems
Credits: 3
Prerequisites: COP 3710 - Database 1
Course Description: This course discusses server based operating systems which are deployed, administered and managed via remote locations. Emphasis is placed upon the hardware required for interconnecting digital devices for the purpose of enabling data communication through a network. Bus architectures, ports, network cards, cabling, routers, switches are also covered. Ensuring network reliability and optimizing network performance are presented.
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CNT 3502 - Data Communication
Credits: 3
Prerequisites: CIS 1000 - Introduction to Innovation and Technology
Course Description: Fundamentals of data communication, including network architectures, communication protocols, transmission standards, internet, and distributed computing are covered. Basic concepts of network management systems, including fundamentals of standards, models, languages, network management systems architectures and protocols as well as SNMP based protocols that manage TCP/IP networks are also discussed. Broadband network management systems and Web-based network management systems tools and applications are presented.
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CNT 4403 - Data Security
Credits: 3
Prerequisites: (COP 3530 - Data Structures & Algorithms or COP 4415 Data Structures ) and COP 4531 Algorithm Design & Analysis .
Course Description: Access control systems, telecommunications and network security, security management practices, application and systems development security, cryptography, disaster recovery planning, legal and ethical issues, and physical security are covered in this course. Special topics include Network Security, Cryptography, Access Control, Security Architecture and Models, Applications and Systems Development, and Vulnerability Assessment.
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CNT 4409 - Network Security
Credits: 3
Prerequisites: CIS 4362 - Applied Cryptography and CNT 3004C - Introduction to Computer Networks
Course Description: The course introduces networks security tools and techniques. Topics covered are: hardware and software network security tools, firewalls, attacks and mitigation at the network level, authentication, intrusion detection, network vulnerability analysis, threat and risk assessment.
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Computer Programming |
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COP 2034 - Introduction to Programming Using Python
Credits: 3
Prerequisites: MAC 2311 - Analytic Geometry and Calculus 1
Course Description: This course is an introduction to computational thinking and the art of computer programming using Python. Students will learn fundamental programming concepts and systematic design techniques. They will use them to write programs that computationally solve and reduce problems. At the end of the course, students will be able to use a programming language without focusing on the language specifics. No prior programming background is required and a working knowledge of high school level algebra is expected.
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COP 2073 - Introduction to Data Science
Credits: 3
Prerequisites: None
Course Description: Students will learn how to work through data science problems within a modern statistical programming language. The course covers the complete analytical process, from gathering the data, to applying appropriate exploratory and statistical analysis, and communicating the results. Important topics in data science projects workflows, version control, and efficient programming are integrated throughout the course. Fundamentals of analytics, data visualization, and management of data are presented.
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COP 2271C - Introduction to Computation and Programming
Credits: 3
Prerequisites: MAC 2311 - Analytic Geometry and Calculus 1 or IDS 1380 - Introduction to STEM
Course Description: This course is an introduction to computational thinking and the art of computer programming using the C programming language. Students will learn fundamental programming concepts and systematic design techniques. They will use them to write programs that computationally solve and reduce problems. At the end of the course, students will be able to use a programming language without focusing on the language specifics. No prior programming background is required and a working knowledge of high school level algebra is expected.
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COP 2272C - Computer Programming 1
Credits: 3
Prerequisites: COP 2271C - Introduction to Computation and Programming
Course Description: This is an intermediate programming course designed for students with prior programming experience in any language. It revises the fundamental programming concepts focusing on best practices in designing and writing efficient code. It also covers basic user-defined data types and the use of essential built-in data structures. After completing the course, students will have a solid command of computer programming and will be able to write medium-sized computer code.
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COP 3330C - Computer Programming 2
Credits: 3
Prerequisites: COP 3337C - Object Oriented Programming
Course Description: This course is an advanced level computer programming course. It introduces advanced programming concepts: Object-Oriented design principals, data abstraction, classes, polymorphism, inheritance, and basic algorithms. Students will acquire skills to solve larger projects and algorithmic problems with more efficient code.
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COP 3337C - Object Oriented Programming
Credits: 3
Prerequisites: COP 2271C - Introduction to Computation and Programming
Course Description: This is an intermediate programming course designed for students with prior programming experience. This course focuses on object-oriented programming concepts and techniques using C++. The covered topics will include: streams, classes, recursion, template classes, file handling, and exception handling.
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COP 3338 - Advanced Computer Programming
Credits: 3
Prerequisites: COP 3530 - Data Structures & Algorithms
Course Description: The course includes advanced programming topics: multithreading, libraries, exception handling, GUI, networks, memory allocation, database connection, cross-platform development issues.
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COP 3530 - Data Structures & Algorithms
Credits: 3
Prerequisites: COP 3337C - Object Oriented Programming and MAD 2104 - Discrete Mathematics
Course Description: The course introduces program run-time analysis and algorithm design and analysis. Topics include: data abstraction principals, serial and parallel data structures, linked lists, graphs, trees, divide and conquer algorithms, greedy algorithms, and linear programming.
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COP 3729C - Database 2
Credits: 3
Prerequisites: COP 3710 - Database 1
Course Description: Datacenter infrastructure and management including technologies such as: virtualization, networking, server consolidation, green IT computing, and network storage configurations are discussed. The utilization of virtualized platforms, networking and infrastructure configurations as well as the deployment, analysis and management of applications are also presented.
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COP 3809C - Advanced Topics in Programming
Credits: 3
Prerequisites: COP 3337C - Object Oriented Programming
Course Description: This course is an advanced level computer programming course. It reinforces the object-oriented programming concepts and techniques using Java. The course will cover topics include interfaces, exception handling, advanced GUI design, graphics and Java 2D, regular expressions, object serialization, collections, concurrency, accessing databases, and networking.
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COP 3834C - Web Application Development
Credits: 3
Prerequisites: COP 2271C - Introduction to Computation and Programming
Course Description: Topics include: Client-side programming, distributed transactions, remote procedure calls, component objects, server side programming and network load balancing. Methods such as HTML5, CSS, JavaScript, XML, and PHP are introduced.
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