2020-2021 Graduate/Doctorate Catalog w/ Sept Addendum [ARCHIVED CATALOG]
Course Descriptions
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Analytics |
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ANLY 500 - Analytics I: Principles and Applications
(3 semester hours)
Prerequisites: None
Description: The first course in analytics covers the core concepts and applications of analytics. The student is introduced to the main concepts and tools of analytics including descriptive, predictive, and prescriptive analytics. During the course, the student uses a variety of statistical and quantitative methods, computational tools, and predictive models to make data-driven decisions. By the end of the course, the student will apply the concepts to real work projects where, by asking some questions about an issue or situation, use analytical tools to respond to it, and present it to technical and layperson audiences.
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ANLY 502 - Analytical Methods I
(3 semester hours)
Prerequisites: None
Description: This course reviews the fundamental mathematics required to be successful in the analytics program. It is designed to strengthen the mathematical abilities while addressing the requirements for coding/scripting. It presents the mathematical topics as coding/scripting problems. This is intended to further strengthen the ability to develop the subroutines/codes/scripts that are also necessary in an analytics career.
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ANLY 505 - Modeling, Simulation and Game Theory
(3 semester hours)
Prerequisites: None
Description: This course covers the basic principles of mathematical modeling, Monte Carlo simulations, and gamification in modern enterprises. The course draws upon interdisciplinary source material, real-world case studies, and production game environments to identify effective analytical models, strategies, techniques, and metrics for the application of games to business. It also identifies a number of significant pitfalls to the successful implementation of gamification techniques, notably legal and ethical issues, the difficulty of making things fun, and the problems with implementing radical change in established firms. The course’s emphasis is on how Big Data can be used to support the analytical models, simulations and games.
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ANLY 506 - Exploratory Data Analysis
(3 semester hours)
Prerequisites: None
Description: Exploratory data analysis plays a crucial role in the initial stages of analytics. It comprises the pre-processing, cleaning, and preliminary examination of data. This course provides instruction in all aspects of exploratory data analysis. It reviews a wide variety of tools and techniques for pre-processing and cleaning data, including big data. It provides the student with practice in evaluating and plotting/graphing data to evaluate the content and integrity of a data set.
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ANLY 510 - Analytics II: Principles and Applications
(3 semester hours)
Prerequisites: ANLY 500 and ANLY 502
Description: This course takes an applied perspective and provides the statistical tools and analytic thinking techniques needed to: formulate a clear hypothesis, determine the most efficient method to obtain required data, determine and apply the proper statistical techniques to the resulting data, and effectively convey the results to both experts and laypersons. The course begins with a review of the descriptive analytics concepts (i.e., sampling, and statistical inferences) introduced in ANLY 500 as well as general conventions regarding experimentation and research. It then progresses to predictive and prescriptive analytics techniques such as regression and forecasting that can be used to predict future events. Later sessions focus on issues related to lack of experimental control (e.g., quasi-experimental design and analysis). The course culminates with a research project in which the student applies the concepts learned to their own research interests.
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ANLY 512 - Data Visualization
(3 semester hours)
Prerequisites: ANLY 500 or HCIN 500 or ISEM 542
Description: The visualization and communication of data is a core competency of analytics. This course takes advantage of the rapidly evolving tools and methods used to visualize and communicate data. Key design principles are used to reinforce skills in visual and graphical representation.
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ANLY 515 - Risk Modeling and Assessment
(3 semester hours)
Prerequisites: ANLY 500
Description: This course focuses on risk management models and tools and the measurement of risk using statistical and stochastic methods, hedging, and diversification. Examples of this are insurance risk, financial risk, and operational risk. Topics covered include estimating rare events, extreme value analysis, time series estimation of external events, axioms of risk measures, hedging using financial options, credit risk modeling, and various insurance risk models.
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ANLY 520 - Natural Language Processing
(3 semester hours)
Prerequisites: ANLY 500
Description: Web technologies based on text and Natural Language Processing (NLP) are becoming the backbone of analytic solutions for understanding language as text language processing has come to play a central role in the multilingual information society. This course provides a highly accessible introduction to the field of text analytics focusing on processing text, tokenization, entity recognition, classification, and sentiment analysis. The course is intensely practical, it uses R and Python programming languages to perform NLP tasks.
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ANLY 525 - Quantitative Decision-Making
(3 semester hours)
Prerequisites: ANLY 510
Description: Decision-making in business today requires the use of all resources, particularly information. Analytics supports decision-making quantitatively by applying information received from multiple sources. This course provides the foundation for quantitative decision-making using a rational, coherent approach and includes decision-making principles and how they are applied to business challenges today.
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ANLY 530 - Machine Learning I
(3 semester hours)
Prerequisites: ANLY 510
Description: This course introduces the student to machine learning. It provides the student with the cognitive, mathematical and analytical foundation required for machine learning. It also provides the student with a broad overview of machine learning, including topics from data mining, pattern recognition and supervised and unsupervised learning. This course prepares the student for the complex, higher-level topics in Machine Learning II.
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ANLY 535 - Machine Learning II
(3 semester hours)
Prerequisites: ANLY 530
Description: Machine Learning II considers complex, high-level topics in machine learning. It builds on the foundation provided by Machine Learning I to develop algorithms for supervised and unsupervised machine learning, to study and develop artificial neural networks, to study, develop and evaluate systems for pattern recognition and to consider trade-offs in models, for example, balancing complexity (e.g. volume, variety and velocity of big data) and performance.
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ANLY 540 - Language Modeling
(3 semester hours)
Prerequisites: ANLY 500
Description: This course is an introduction to computational methods in empirical linguistic analysis and natural language processing focusing on building models of human language. Topics include vector space and topics models, similarity, deep learning, and information theory network models. This course will explore how to apply statistical techniques to language with a focus on R and Python programming skills.
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ANLY 545 - Analytical Methods II
(3 semester hours)
Prerequisites: ANLY 502
Description: This course provides student with exposure to an expanded range of analytical methods. This includes additional functions, e.g. the logit function, additional distributions, e.g. Poisson distribution, and additional analysis techniques, e.g. those included in the study of discrete structures such as combinatorics. Particular attention is paid to analytics relevant to disciplines in the social sciences. Also included are survey design, development and (survey data) analysis.
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ANLY 560 - Functional Programming Methods for Analytics
(3 semester hours)
Prerequisites: None
Description: This course provides the student with the required knowledge and skills to handle and analyze data using a variety of programming languages as well as a variety of programming tools and methods. Depending on current industry standards, the student will be provided with the opportunity to develop knowledge and skills in programming environments such as R, Octave, and Python. In addition, the student is introduced to current industry standard data analysis packages and tools such as those in Matlab, SAS or SPSS.
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ANLY 565 - Time Series and Forecasting
(3 semester hours)
Prerequisites: None
Description: This course covers key analytical techniques used in the analysis and forecasting of time series data. Specific topics include the role of forecasting in organizations, exponential smoothing methods, stationary and non-stationary time series, autocorrelation and partial autocorrelation functions, univariate autoregressive integrated moving average (ARIMA) models, seasonal models, Box-Jenkins methodology, regression models with ARIMA errors, transfer function modeling, intervention analysis, and multivariate time series analysis techniques such as Vector Autoregression (VAR), Cointegration and Vector Error Correction Model (VECM).
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ANLY 580 - Special Topics in Analytics
(3 semester hours)
Prerequisites: None
Description: This course explores a topic or collection of topics of special interest that is timely and in response to critical or emerging topics in the broad field of analytics.
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ANLY 585 - Research in Analytics
(3 semester hours)
Prerequisites: None
Description: This program cultivates and supports research partnerships between the student, faculty and other researchers. It provides the student with the opportunity to work on cutting-edge research. Research projects can be at any appropriate and approved level; introductory, participatory or expert. Each project requires an approved proposal, periodic status reports and a final written report with a presentation prepared by the student in collaboration with the research supervisor.
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ANLY 600 - Optimized Analytics
(3 semester hours)
Prerequisites: ANLY 510
Description: This course introduces the fundamental tool in prescriptive analytics. Optimization is the process of selecting values of decision variables that minimize or maximize some quantity of interest. Optimization models have been used extensively in operations and supply chains, finance, marketing, and other disciplines to help managers allocate resources more effectively and make lower cost or more profitable decisions.
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ANLY 610 - Analytical Methods III
(3 semester hours)
Prerequisites: ANLY 560 or ANLY 545
Description: This course provides the student with exposure to the theoretical background for advanced analytical topics and methods. Topics include unstructured data/information and big data. For example, the theoretical background required for the integration of data mining and text analytics or text mining are explored. Additional topics could include the implementation and use of data lakes and ontology evaluation.
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ANLY 699 - Applied Project in Analytics
(3 semester hours)
Prerequisites: GRAD 695 or permission of instructor
Description: This course allows the student to pursue an area of interest that is within the broad scope of analytics. A faculty member will supervise this study.
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ANLY 705 - Modeling for Data Science
(3 semester hours)
Prerequisites: Admission to the Data Sciences Doctoral Program
Description: This course provides a more in-depth presentation of the theory behind linear statistical models, segmentation models, and production level modeling. Further emphasis is placed on practical application of these methods when applied to massive data sources and appropriate and accurate reporting of results.
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ANLY 710 - Applied Experimental and Quasi-Experimental Design
(3 semester hours)
Prerequisites: Admission to the Data Sciences Doctoral Program
Description: Methods and approaches used for the construction and analysis of experiments and quasiexperiments are presented, including the concepts of the design and analysis of completely randomized, randomized complete block, incomplete block, Latin square, split-plot, repeated measures, factorial and fractional factorial designs will be covered along with methods for proper analysis and interpretation in quasi-experiments.
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ANLY 715 - Applied Multivariate Data Analysis
(3 semester hours)
Prerequisites: Admission to the Data Sciences Doctoral Program
Description: This course provides hands-on experience in understanding when and how to utilize the primary multivariate methods Data Reduction techniques, including Principal Components Analysis and Exploratory and Confirmatory Factor Analyses, ANOVA/MANOVA/MANCOVA, Cluster Analysis, Survival Analysis and Decision Trees.
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ANLY 720 - Data Science from an Ethical Perspective
(3 semester hours)
Prerequisites: None
Description: This course introduces the power and pitfalls of handling user information in an ethical manner. The student is offered a historical and current perspective and will gain an understanding of their role in assuring the ethical use of data.
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ANLY 725 - Research Seminar in Unstructured Data Analysis
(3 semester hours)
Prerequisites: Admission to the Data Sciences Doctoral Program
Description: This course follows a research seminar format. The student and faculty develop research proposals, analyses, and reporting in the domain of Unstructured Data. Topics of special interest in Unstructured Data analysis are presented by faculty and the student under faculty direction. Topics of special interest vary from semester to semester. Repeatable for additional content reasons. Cross-listed with ANLY 761 .
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ANLY 730 - Research Seminar in Forecasting
(3 semester hours)
Prerequisites: Admission to the Data Sciences Doctoral Program
Description: This course follows a research seminar format. The student and faculty develop research proposals, analyses, and reporting in the domain of Forecasting. Topics of special interest in Forecasting Data analysis are presented by faculty and the student under faculty direction. Topics of special interest vary from semester to semester. Repeatable for additional content reasons. Cross-listed with ANLY 762 .
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ANLY 735 - Research Seminar in Machine Learning
(3 semester hours)
Prerequisites: Admission to the Data Sciences Doctoral Program
Description: This course follows a research seminar format. The student and faculty develop research proposals, analyses, and reporting in the domain of Machine Learning. In addition, topics of special interest in Machine Learning are presented by faculty and the student under faculty direction. Topics of special interest vary from semester to semester. Repeatable for additional content reasons. Cross-listed with ANLY 763 .
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ANLY 740 - Graph Theory
(3 semester hours)
Prerequisites: Admission to the Data Sciences Doctoral Program
Description: This course introduces standard graph theory, algorithms, and theoretical terminology. Including graphs, trees, paths, cycles, isomorphisms, routing problems, independence, domination, centrality, and data structures for representing large graphs and corresponding algorithms for searching and optimization.
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ANLY 745 - Functional Programming Methods for Data Science
(3 semester hours)
Prerequisites: Admission to the Data Sciences Doctoral Program
Description: This course is designed to build on the Functional Programming Methods for Analytics course. The student works to extend programming skills to write the student’s own versions of popular statistical functions using a current programming language.
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ANLY 755 - Advanced Topics in Big Data
(3 semester hours)
Prerequisites: Admission to the Data Sciences Doctoral Program
Description: Topics include the design of advanced algorithms that are scalable to Big Data, high performance computing technologies, supercomputing, grid computing, cloud computing, and Parallel and Distributed Computing, and issues in data warehousing.
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ANLY 760 - Doctoral Research Seminar
(6 semester hours)
Prerequisites: Completion of doctoral coursework requirements; pass qualification examination
Description: This seminar provides support to the doctoral student within their specific domains of research. Led by the faculty advisor for that domain, the course is designed to provide a forum where faculty and the student can come together to discuss, support, and share the experiences of working in research.
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ANLY 761 - Research Seminar in Unstructured Data Analysis
(3 semester hours)
Prerequisites: Completion of doctoral coursework requirements; pass qualification examination
Description: This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Unstructured Data. Topics of special interest in Unstructured Data analysis are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester. Cross-listed with ANLY 725 .
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ANLY 762 - Research Seminar in Forecasting
(3 semester hours)
Prerequisites: Completion of doctoral coursework requirements; pass qualification examination
Description: This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Forecasting. Topics of special interest in Forecasting are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester. Cross-listed with ANLY 730 .
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ANLY 763 - Research Seminar in Machine Learning
(3 semester hours)
Prerequisites: Completion of doctoral coursework requirements; pass qualification examination
Description: This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Machine Learning. Topics of special interest in Machine Learning are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester. Cross-listed with ANLY 735 .
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ANLY 799 - Doctoral Studies
(6 semester hours)
Prerequisites: Completion of doctoral coursework requirements; pass qualification examination
Description: Advancement to candidacy is a prerequisite of this course. This is an individual study course for the doctoral student. Content to be determined by the student and the student’s Doctoral Committee. May be repeated for credit.
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Biotechnology |
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BTEC 502 - Biomaterials
(3 semester hours)
Prerequisites: None
Description: There is a constant need for new biomaterials in life sciences to support novel technologies. This course is designed to introduce the student to the various classes of biomaterials currently in use and their application in selected subspecialties of medicine/industrial processes. The student will learn about the concepts behind developing materials for use in medical or industrial biotechnology field. The student will gain an understanding of material properties, various biological responses to materials, and the clinical context of their use. Aspects of manufacturing processes, cost, sterilization, packaging, and regulatory issues will be addressed.
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BTEC 508 - Omics for Life Sciences
(3 semester hours)
Prerequisites: Bachelor of Science degree in Biotechnology, Life Science or other relevant field or by permission of instructor
Description: Studies on cataloging and characterization of genome and proteome are on the forefront of research. Recently, there has been a considerable amount of work happening with genome and proteome data for selective manipulation of metabolic pathways, the metabolomics. All three fields are aggressively used in several areas for innovation in diagnostics, biomanufacturing, biomarker studies, and drug discovery to name a few. This course covers the basics of these three “omics” fields from the standpoint of using the information for developing new biotechnologies, especially in personalized medicine. The significance of next generation sequencing will be covered.
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BTEC 522 - Graduate Biotechnology Seminar
(3 semester hours)
Prerequisites: None
Description: This course introduces the student to fundamental topics in innovation, regulatory practices and ethics for various biotechnology industries and communities. The intention is to allow the student to learn about these diverse but inter-related areas that coalesce science and business disciplines. With the help of industry experts, case studies, and current literature, the student explores the interrelationship of these areas for creating productive collaborations within biotechnology industry with respect to compliance, innovation, and ethical decision-making.
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BTEC 540 - Biostatistics
(3 semester hours)
Prerequisites: Undergraduate level Math or by permission of the instructor
Description: This course introduces statistical concepts and analytical methods as applied to data encountered in biotechnology and biomedical sciences. It emphasizes the basic concepts of experimental design, quantitative analysis of data, and statistical inferences. Topics include probability theory and distributions, population parameters and their sample estimates, descriptive statistics for central tendency and dispersion, hypothesis testing and confidence intervals for means, variances, and proportions, the chi-square statistic, categorical data analysis, linear correlation and regression model, and analysis of variance. The course provides the student a foundation to evaluate information critically to support research objectives and product claims and to gain better understanding of statistical design of experimental trials for biological products/devices.
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BTEC 550 - Instrumentation in Biotechnology Industry
(3 semester hours)
Prerequisites: Bachelor of Science degree in Biotechnology, Life Science or other relevant field or by permission of instructor
Description: Instrumentation and application of various equipment is central to research and commercial production in the biotechnology industry. This course will familiarize the student with which instruments are used for which biotechnology applications and their principles of operation and limitations. Different biomolecules require different and customized protocols for isolation, purification, and characterization. The course offers an overview of instruments such sonicator, ultracentrifuges, spectrophotometers, etc. The course also covers the significance of instrument validation and calibration.
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BTEC 560 - Design of Experiment
(3 semester hours)
Prerequisites: BTEC 540 or by permission of instructor
Description: This course allows the student to design an experiment and learn methodology for data analysis. Components such as major characteristics of a scientific experiment, running statistical analyses to perform various tests to check validity of the data would be covered. In a case-based manner, the student works on design of an experimental protocol for an assigned conceptual research project. Trouble-shooting strategies and analyzing data sets would be covered.
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BTEC 610 - Advanced Topics in Drug Discovery and Delivery
(3 semester hours)
Prerequisites: Bachelor of Science degree in Biotechnology, Life Science or other relevant field or by permission of instructor
Description: This course introduces the student to the planning and preparatory phase skills required to develop potential new drugs and biologics efficiently. The student gains a thorough appreciation of FDA regulations and guidelines. It is known that in the drug discovery sector, it is important to plan before the proceeding to the development phase. With emphasis on the process, the course focuses on the final analysis and report before developing the protocols. Other important aspects of drug development covered in the course are preclinical investigations; new drug application (NDA) or biologic license application (BLA) format and content; clinical development plans; product and assay development; the Investigational New Drug (IND) process; and trial design, implementation, and management. Lastly, the course provides an overview of trending concepts such as controlled and targeted drug delivery.
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BTEC 612 - Regulatory Affairs in Life Science Industries
(3 semester hours)
Prerequisites: Bachelor of Science degree in Biotechnology, Life Science or other relevant field or by permission of instructor
Description: Regulatory affairs (RA) are rules and regulations that oversee and govern product development and post-approval marketing in the life sciences. For US companies, Food and Drug Administration (FDA) establishes and oversees the applicable regulations under several statutes, partnerships with legislators, patients, and customers. The commercializable products for the Biotechnology sector can be food, drugs, biologics, or medical devices. Each type is regulated by a different center within the FDA. This course provides an overview of RA, and its effect on product development. The course covers RA history, various regulatory agencies, methods to access regulatory information, procedures for drug submissions, biologics submissions, and medical device submissions. It also addresses Good Laboratory Practices (GLP), Good Manufacturing Practices (GMP), and FDA inspections. The course includes guest lectures, actual case studies and real world scenarios. As a course project, the student creates a conceptual submission document for a hypothetical drug/biologic/medical device approval.
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BTEC 615 - Biomedical Devices and Prototyping
(3 semester hours)
Prerequisites: Bachelor of Science degree in Biotechnology, Life Science or any other relevant field or by permission of instructor
Description: This course familiarizes the student with basic principles of biosensors design and applications. Biomedical devices such as Biosensors are one of the most innovative, complex, and fastest growing area of biotechnology today; the interface between biotechnology, nanotechnology and micro-electronics industries. The course covers a variety of biosensors based on whole cells, nucleic acids, proteins, antibodies and enzymes as well as new and emerging technologies related to designing, fabricating, and applying multi-array biochips and micro-fluidic systems (lab-on-the-chip). Practical applications of this technology in health care, environment, medical diagnostics, defense and other areas are explored.
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BTEC 618 - Principles of Bioprocessing
(3 semester hours)
Prerequisites: Bachelor of Science degree in Biotechnology, Life Science or any other relevant field or by permission of instructor
Description: Bioprocessing deals with the isolation, purification, and characterization of industrial bio-products. This course prepares the student with skills needed in bioprocessing procedures used in industry. Fundamental scientific principles underlying the recovery, purification and formulation of biomolecules, especially proteins, or other industrial bio-products are covered. Identification or delineation of key chemical and physical properties of biomolecules that impact downstream processing and formulation development are emphasized. Introduction to analytical and small-scale purification procedures exposes the student to key scientific principles and small-scale unit operations.
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BTEC 620 - Emerging Trends in Diagnostics
(3 semester hours)
Prerequisites: None
Description: This course provides an overview of the fundamental principles of molecular diagnostics and explores the use of molecular techniques in the diagnosis of disease/infection/contaminants. Diagnostics has impacted several fields such as human health, environment, and food and agriculture. Development of novel diagnostics technologies have depended on discovery of biomarkers for multiple applications in fields such as drug discovery, drug delivery, and diagnostics in general. Topics covered in this course include: biomarkers, protein and nucleic acid structure-function, identification and amplification techniques used in infectious disease diagnosis, components of a molecular diagnostics, companion diagnostics, and evaluation of controls to validate results obtained. This course allows innovative use of current literature and technology with an entrepreneurship element. The student has an opportunity to use course material and available technology to design a conceptual assay/device for a chosen target and integrate it into a conceptual course project assignment.
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BTEC 622 - Principles of Accounting and Finance
(3 semester hours)
Prerequisites: None
Description: This course is offered to expose the student to a basic introduction to principles of accounting and finance for the life science industry. Accounting and finance take different shades when one compares revenues for giants like Target with that for a pharma company. The student studies life science companies and their accounting procedures. Impact of significant adjustments and estimates on revenue counting, health insurance, managed care, and governmental contracts is covered. Also covered are accounting practices related to multi‐round private financing and IPO timing for start-ups. The student is taught the basics of money management, the language and vocabulary of finance, how to communicate scientific concepts to potential investors, and how to generate fiscal plans/milestones. Course activities enable the student to create and analyze financial documents such as a term sheet, a contract and a balance sheet. The student is also presented the concepts of financial risk and the time value of money. This course will use real company scenarios and case studies from life sciences companies.
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BTEC 625 - Pharmacogenomics
(3 semester hours)
Prerequisites: Bachelor of Science degree in Biotechnology, Life Science or other relevant field or by permission of instructor
Description: The genetic basis of variability in drug response can contribute to drug efficacy and toxicity, adverse drug reactions and drug-drug interactions. Healthcare professionals need an understanding of the genetic component of patient variability to deliver effective individualized pharmaceutical care. This course offers an introduction to the evolution of pharmacogenetics/pharmacogenomics, the human genome and modern applications of DNA information related to diagnostics, drugs and therapeutics. Emphasis is placed on concepts and methodologies for using an individual’s genetic make-up to determine that individual’s predisposition towards diseases and ability to respond to drugs. Understanding of the basics of pharmacogenomics enables the student to better understand and manage the new genomics based tools and make best treatment choices.
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BTEC 630 - Cancer Biotechnology
(3 semester hours)
Prerequisites: Bachelor of Science degree in Biotechnology, Life Science or other relevant field or by permission of instructor
Description: Cancer has a huge impact on our society and is one of the major factors driving biomedical research related to various areas such as imaging, diagnosis, and therapy. This course provides a comprehensive overview of the molecular biology and genetic basis of cancer. Biotechnological research on the molecular mechanisms of cancer has resulted in more effective treatments, sensitive diagnostic procedures and strategies for prevention. The course covers topics such as mutations leading to deregulation of programmed cell death, their impact on cell proliferation, and cell differentiation. Cancer and medical intervention is also reviewed. It allows the student to study traditional treatment methods and new treatment protocols for cancer therapies. The challenges of early diagnostics are also covered.
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BTEC 634 - Healthcare Economics: Fundamentals for Providers and Biotech Professionals
(3 semester hours)
Prerequisites: None
Description: Patients, healthcare providers and biotech industry professionals all have an interest in the best possible medical care, but healthcare services and products come at a cost. This course explores economics of topics that impact the cost of healthcare as we know it today, and how the healthcare technologies of the future will be funded. Additional questions, such as who pays and who gets access when healthcare is in limited supply, are discussed. Among the factors explored are market dynamics, public policy, technology, reimbursements and workforce and patient choices. Case studies, course papers, and group discussions are used to offer the course content in an engaging and interactive mode. This course requires no previous study of finance or economics.
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BTEC 635 - Clinical Pharmacology
(3 semester hours)
Prerequisites: Bachelor of Science degree in Biotechnology, Life Science or other relevant field or by permission of instructor
Description: Clinical pharmacology deals with drug development and drug utilization in therapeutics. This course covers the advancements regarding drug action and efficacy. Concepts of pharmacokinetics, drug metabolism and transport, pharmacogenetics, assessment of drug effects, and drug therapy in special populations are explored. Expert knowledge is shared about drug development and content specialization needed to stay competitive and build opportunity for career options.
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BTEC 640 - Trends in Regenerative Medicine
(3 semester hours)
Prerequisites: Bachelor of Science degree in Biotechnology, Life Science or other relevant field or by permission of instructor
Description: Tissue engineering (TE) and regenerative medicine (RM) are geared towards developing biological substitutes that restore, maintain, or improve damaged tissue and organ functionality. While tissue engineering and regenerative medicine have hinted at much promise in the last several decades, significant research is still required to provide exciting alternative materials to finally solve the numerous problems associated with traditional implants. This course covers relevant biological, engineering, clinical, legal, regulatory and ethical principles and perspectives to understand the basics of RM. This course also introduces the student to the current state of the RM field, global market trends and opportunities and challenges in process development, manufacturing, and commercialization.
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BTEC 650 - Fermentation Technologies
(3 semester hours)
Prerequisites: Bachelor of Science degree in Biotechnology, Life Science or other relevant field or by permission of instructor
Description: Fermentation technology focusses on use of recombinant microorganisms for several industrial processes, i.e. biomanufacturing. This course requires the student to conceptually design a process for biomanufacturing a target product. This includes the basics of strain selection, development, and process optimization. Application of strain morphology, physiology and DNA sequence- based methods are analyzed for industrial processes. The student studies microbial metabolism and its significance to the manufacturing process. Fundamentals of microbial growth, growth stoichiometry, types of growth media (defined, semi-defined, complex) and media optimization are covered. The course provides an overview of fermenter design concepts and operational principles for a fermentation process using bioreactors.
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BTEC 655 - Industrial Enzymes and Proteins
(3 semester hours)
Prerequisites: Bachelor of Science degree in Biotechnology, Life Science or other relevant field or by permission of instructor
Description: There is significant commercial activity in the biomanufacturing sector. Key products include vaccines, antibiotics, or various industrial enzymes. The basics of recombinant DNA (rDNA) principles in modification, selection, and application of recombinant microbial strains for industrial enzyme and protein production are studied. Theoretical foundations of microbial production and detection of recombinant protein products such as enzymes, hormones, and antibiotics are covered. The course provides an overview of basic methodologies involved in genetic manipulation of microbes to produce recombinant peptides and proteins. This would focus on use of plasmids, role of promoters and its use in control of gene expression with the end goal of generating enzymes and whole cells for industrial catalytic processes.
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BTEC 672 - Legal Affairs and Policies for Life Science Industry
(3 semester hours)
Prerequisites: None
Description: This course provides the student an overview of key legal concepts and policies that govern research, development and commercial activities within the biotech industry. The course is structured from a company’s perspective and introduces the student to topics and strategies critical to management while considering new topics and products. Selected cases, videos of speeches, and assigned readings illustrate how the laws that provide protection of society and promotion of social goals operate. Procedures that allow navigating the middle ground while dealing with competition in the biotech and pharma industry would be covered as well. This course requires no previous legal study.
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BTEC 675 - Innovation and Improvisation in Research and Development
(3 semester hours)
Prerequisites: None
Description: This course prepares the student for the research and development sector. The student develops creative problem-solving abilities and other skills necessary for innovative approaches in managing research and development units. The resolution of conflicts between Research and Development, manufacturing, and marketing in a high technology firm are studied. The student explores various coping strategies, ways to maintaining entrepreneurial spirit and encourage innovation as the company develops into a formal administrative organization, identify R &D issues and strategies to resolve them. Mass production techniques such as Just-In-Time, On-Job Training and Total Quality Management to the real world of high technology Research & Development (R&D) are studied. As a team project for the course, the student identifies and develops solutions to practical problems or market needs for a hypothetical scenario.
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BTEC 698 - Biotechnology Graduate Internship
(3 semester hours)
Prerequisites: Completion of 6 credits in the BTMS program
Description: This graduate internship course provides the student an opportunity to serve as a graduate intern to learn the skills of a certain job in real world situation. It is the student’s responsibility to identify an industry or an organization from the field of interest and work on a mutually relevant topic under direct supervision of an employee from that company.
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BTEC 699 - Applied Project in Biotechnology
(3 semester hours)
Prerequisites: GRAD 695 or permission of instructor
Description: This course allows the student to pursue an area of interest that is within the broad scope of Biotechnology. A faculty member will supervise this study.
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Computer Information Sciences |
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CISC 504 - Principles of Programming Languages
(3 semester hours)
Prerequisites: A Baccalaureate degree in computer science or a related technical field (e.g., electrical and computer engineering, information science, operations research) or permission of CISC grad committee (This course is designed for the student that does not have a CS background)
Description: This course explores a topic of collection of topics of special interest that is timely and in response to critical or emerging topics in the broad field of computer information sciences. The student with prior math or engineering education may have a foundation for the statistical concepts they encounter in a computer science graduate program, but not enough programming experience to keep up with the analysis, modeling and creating their own computational solutions. This course is intended to give the student the programming capability and experience required to succeed in their graduate study of master computer information sciences. The course is an application-driven and solution strategies with Python. Furthermore, integration between Python and other languages is also covered. Topics include programming paradigms, functional programming scripting languages, objects, algorithm design and analysis, trees, graphs, sorting and searching. The focus is on how these concepts relate to computational tasks in science and engineering.
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CISC 510 - Object-Oriented Software
(3 semester hours)
Prerequisites: Baccalaureate degree in Computer and Information Sciences with a concentration in Software Engineering and Systems Analysis or the equivalent.
Description: This course develops fluency in object-oriented design. The student studies semantics of object-oriented languages, strengths and limitations of the object-oriented approach, processes that can lead to good design outcomes, graphical and textual representations for design including UML, common problems and some of the patterns that can solve them, and refactoring utilizing modern IDEs. The student develops an ability to read and critique designs, and to clearly present and advocate design ideas.
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CISC 520 - Data Engineering and Mining
(3 semester hours)
Prerequisites: Baccalaureate degree in Computer and Information Sciences with a concentration in Software Engineering and Systems Analysis or the equivalent.
Description: This course addresses the emerging issues in designing, building, managing, and evaluating advanced data-intensive systems and applications. Data engineering is concerned with the role of data in the design, development, management, and utilization of complex computing/information systems. Areas of interest include database design; meta knowledge of the data and its processing; languages to describe data, define access, and manipulate databases; and strategies and mechanisms for data access, security, and integrity control. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these data repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.
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CISC 525 - Big Data Architectures
(3 semester hours)
Prerequisites: Baccalaureate degree in Computer Information Systems, Computer Sciences, or related field.
Description: Government, academia and industry have spent a great deal of time, effort, and money dealing with increases in the volume, variety, and velocity of collected data. Collection methods, storage facilities, search capabilities, and analytical tools have all needed to adapt to the masses of data now available. Google paved the way for a new paradigm in Big Data, with two seminal white papers describing the Google File System, a distributed file system for massive storage, and MapReduce, a distributed programing framework designed to work on data stored in the distributed file system. This course introduces the student to the concepts of Big Data and describes the usage of distributed file systems and MapReduce programming framework to provide skills applicable to developers and the data scientist in any facet of industry.
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CISC 530 - Computing Systems Architecture
(3 semester hours)
Prerequisites: Baccalaureate degree in Computer and Information Sciences with a concentration in Software Engineering and Systems Analysis or the equivalent.
Description: Modern computer information systems are ever-increasing in complexity and sophistication. As a result, software engineers must be able to make effective decisions regarding the strategic selection, specification, design, and deployment of information systems. Therefore, this course addresses the topics of architectural design that can significantly improve the performance of computer information systems. The course introduces key architectural concepts, techniques, and guidance to software engineers to enable them to make more effective architectural decisions.
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CISC 535 - Cloud Security
(3 semester hours)
Prerequisites: Graduate Standing
Description: This course provides guidelines for CSC data security utilizing cloud computing by determining the boundaries of the cloud service provider (CSP) responsible for ensuring that customer data is properly secured. Depending on the cloud services (i.e. IaaS, PaaS, SaaS), the security of the data is the responsibility of the CSC themselves. For example, in some cases the CSP may be responsible for restricting access to the data, while the CSC remains responsible for deciding which cloud service users (CSUs) should have access to it, and the behavior of any scripts or applications with which the CSU processes the data. This course identifies the security controls protecting CSC data that can be used in the different stages of the full data lifecycle. These security controls can differ when the security level of the CSC data changes.
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CISC 540 - Agile Software Development
(3 semester hours)
Prerequisites: Bachelor of Science in Computer Information Systems, Computer Sciences, or related field.
Description: This course addresses what agile methods are, how they are implemented, and their impact on software engineering. A variety of agile methods are described, including but not limited to: Scrum, Extreme Programming, and Crystal Clear. The concerns associated with planning and controlling agile projects, along with the implications of agile development on the customer-developer dynamic are analyzed.
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CISC 550 - Software Engineering in Mobile Computing
(3 semester hours)
Prerequisites: CISC 510 and CISC 520
Description: Recent years have witnessed the advent of wireless mobile and sensor technologies and the proliferation of application scenarios whereby large numbers of pervasive computing devices are connected to a wireless networking infrastructure in an ad hoc manner. The student is shown how to design, implement, and deploy location/context-aware applications that interact with Service Oriented Architecture (SOA) solutions. Topics to be covered include: basic user interfaces, application design, concurrency, and location-aware and other context-aware programming.
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CISC 560 - Secure Computer Systems
(3 semester hours)
Prerequisites: Bachelor of Science in Computer and Information Sciences with a concentration in Software Engineering and Systems Analysis or the equivalent.
Description: This course focuses on the design principles for secure computer systems. Topics regarding authentication, access control and authorization, discretionary and mandatory security policies, secure kernel design, secure operating systems, and secure databases are covered from a systems architecture perspective. Emphasis is on the design of security measures for critical information infrastructures. Upon completion of this course, the student is able to design, implement, and manage secure computer systems through the design of a security awareness program.
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CISC 570 - Advanced Database Security
(3 semester hours)
Prerequisites: CISC 560
Description: This course focuses on topics related to the design and implementation of secure data stores. Emphasis is placed on multi-level security in database systems, covert channels, and security measures for relational and object-oriented database systems. This course teaches how to recognize the insecurities present within common database systems and how these flaws can leave a database wide open to attack. The course covers how hackers discover and exploit vulnerabilities to gain access to a data store.
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CISC 580 - Advanced Network Security
(3 semester hours)
Prerequisites: CISC 560
Description: This course covers fundamental concepts, principles, and practical networking and inter-networking topics relevant to the design, analysis, and implementation of enterprise-level trusted networked information systems. Topics include networking and security architectures, techniques, and protocols at the various layers of the internet model. Security problems in distributed application environments are analyzed and solutions discussed and implemented.
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CISC 585 - Principles of Software Architectural Patterns
(3 semester hours)
Prerequisites: A baccalaureate degree in computer science or a related technical field
Description: This course will serve as a catalog of commonly used design patterns, prominent and dominant software patterns, and their applications. This course is divided into three modules. First, Software Architecture Patterns covers the various architectural patterns of object-oriented, component-based, client server, and cloud architecture. The need for software patterns is described. The various architectural patterns are listed and explained in detail in order to convey the what, where, why and how of architectural patterns. Second, Enterprise Integration Patterns covers enterprise application integration patterns and how they are designed. Patterns of service-oriented architecture (SOA), event driven architecture (EDA), resource-oriented architecture (ROA), big data analysis architecture, and microservice architecture (MSA) will be carefully studied. Finally, Patterns for Containerized and Highly Reliable Applications covers advanced topics such as Docker containers, high-performance, and reliable application architectures. Key takeaways include understanding what architectures are, why they are used, and how and where architecture design and integration patterns are being leveraged to build bigger and better systems. Cross-listed with NGEN 585 .
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CISC 590 - Information Security Project
(3 semester hours)
Prerequisites: CISC 560
Description: This project course serves as a capstone for the specialization in Information Security. The class focuses on techniques for protecting critical information infrastructures through case studies, application development, and systems assessment, while the project’s activities encompass research, development and analysis/synthesis for a particular problem or opportunity.
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CISC 592 - Software Architecture and Microservice
(3 semester hours)
Prerequisites: Bachelor of Science in Computer Science or a related technical field (e.g., Electrical and Computer Engineering, Information Science, Operations Research) or permission of CISC grad committee.
Description: This course explores a collection of topics in Software Architecture and Microservices and introduces concepts and best practices of software architecture. It deals with; high-level building blocks that represent the underlying software system, how a software system is structured, and how that system’s elements are meant to interact. Fundamentals of software architecture, its principles, elements, components, configurations and architectural structures and styles will also be discussed. Special focus will be given to the interaction between quality attributes and software architecture. Societal and ethical implications of software architecture and microservices will also be discussed
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CISC 593 - Software Verification and Validation
(3 semester hours)
Prerequisites: CISC 592
Description: This course will introduce various software testing techniques such as; unit testing, integration testing, system testing, acceptance testing, and regression testing, types of software errors, reporting and analyzing software errors, problem tracking systems, test planning, test case design, and verification & validation. The course also explores functional (black box) methods for testing software systems, reporting problems effectively and planning testing projects. The student will apply testing techniques that they have learned, throughout the course, to a sample application.
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CISC 594 - Software Testing Principles and Techniques
(3 semester hours)
Prerequisites: CISC 593
Description: This course explores a collection of topics in Software Testing Principles and Techniques. It introduces testing techniques, software quality fundamentals, and focuses on software quality assurance for the entire software development lifecycle. It covers topics such as; Quality factors, Software Quality Requirements, Reviews, Software Audits, Software Configuration Management, Policies, Processes, and Procedures, Measurement, Risk Management, Software Quality Assurance Plan, Software Quality Models, Test Automation, Testing Tools, Black Box and White Box testing techniques. The Pareto Principle Applied to Software Quality Assurance, and Software Testing Strategies will also be discussed.
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CISC 595 - Software Architectural Patterns Design and Implementation
(3 semester hours)
Prerequisites: CISC 585 or NGEN 585
Description: This course will serve as a catalog of commonly used open source software in the design and implementation of software solutions. The student will be exposed to open source project structure, work on an open source project, and be expected to make a significant contribution through their own custom design projects.
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CISC 600 - Scientific Computing I
(3 semester hours)
Prerequisites: A baccalaureate degree in computer science or a related technical field (e.g. electrical and computer engineering, information science or operations research).
Description: This course provides an overview of scientific computing and covers: Solution of Linear Algebraic Equations, Interpolation and Extrapolation, Integration and Evaluation of Functions, Random Numbers, and Sorting. The course uses C++ programming language as the base language to solve the problem sets. The student may choose to use another programming language as well. The course is conceived as an introduction to the thriving field of numerical simulation for computer scientists, mathematicians, engineers, or natural scientists without an already strong background in numerical methods.
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CISC 601 - Scientific Computing II
(3 semester hours)
Prerequisites: CISC 600
Description: Scientific Computing II covers: root finding and nonlinear sets of equations, minimization or maximization of functions, eigensystems, fast Fourier transform, Fourier and spectral applications, statistical description of data, and modeling of data. The course uses C++ programming language as a base language to solve the problem sets, or a student can choose another programming language. The course is intensely practical with fully worked examples and graded exercises.
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CISC 603 - Theory of Computation
(3 semester hours)
Prerequisites: CISC 530 and CISC 610
Description: This course contains abstract models of computation and computability theory including formal languages, finite automata, regular expressions, context-free grammars, pushdown automata, Turing machines, primitive recursive and recursive functions, and decidability and un-decidability of computational problems.
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CISC 610 - Data Structures and Algorithms
(3 semester hours)
Prerequisites: CISC 504
Description: This course emphasizes fundamental algorithms and advanced methods of algorithmic design, analysis and implementation. This class overs techniques used to analyze problems and algorithms (including asymptotic, upper/lower bounds, best/average/worst case analysis, amortized analysis, complexity), basic techniques used to design algorithms (including divide and conquer/greedy/dynamic programming/heuristics, choosing appropriate data structures) and important classical algorithms (including sorting, string, matrix, and graph algorithms) and data structures.
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CISC 611 - Network Operating Systems
(3 semester hours)
Prerequisites: CISC 530 and CISC 610
Description: This course introduces the principles and implementations of operating systems and networking. The operating system manages hardware resources and provides a simplified interface for programs to use these resources. Networking allows different computers to communicate and potentially act as a larger virtual system. These topics are closely related; networking is often managed by the operating system (and always requires use of the hardware it manages) and the operating system uses the network to provide services like the file system. C++ language is needed to facilitate out study to these topics which provides low-level access to the hardware and is often used in operating systems and networking.
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CISC 612 - Elements of Computing Systems
(3 semester hours)
Prerequisites: CISC 611
Description: This course is an integration process of key notions from algorithms, computer architecture, operating systems, compilers, and software engineering into one unified framework. This is done constructively, by building a general-purpose computer system from the ground up. In the process, many ideas and techniques are used in the design of modern hardware and software systems, and discuss major trade-offs and future trends. This is a hands-on course, evolving around building the full set of HW and SW modules including the chip set of simple computers using a simulator, developing the assembler, building part of the virtual machine translator and a simple compiler all the way to a simple programming language and a simple game.
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CISC 614 - Computer Simulation
(3 semester hours)
Prerequisites: CISC 601
Description: This course is about the use of simulation to make better business decisions in application domains from healthcare to mining, heavy manufacturing to supply chains, and everything in between. It is written to help both technical and non-technical users better understand the concepts and usefulness of simulation. The student can use the programming languages of their choice or use an off-the-shelf software to implement their projects.
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CISC 620 - Principles of Machine Learning
(3 semester hours)
Prerequisites: CISC 530 , CISC 600 , and CISC 610
Description: This course introduces the basic idea of machine learning and the application to data from real world problems. Topics include: Classification as a Problem-Solving Tool, Similarity Measures and Clustering. The Classification Process, Classification for Sentiment Analysis, Advanced Recommendations, FFT Classifiers, Computer Vision & Pattern Recognition, Dimensionality Reduction, and Big Data & Machine Learning.
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CISC 621 - Statistical Pattern Recognition
(3 semester hours)
Prerequisites: CISC 610 , equivalent, or permission of the instructor
Description: Statistical pattern recognition techniques are used to design automated systems that improve their own performance through experience. This course covers the methodologies, technologies, and algorithms of statistical pattern recognition from a variety of perspectives. The objective is to provide a reasonable answer for all possible data and to classify input data in to objects or classes based on certain features. After taking the course, the student should have: a clear understanding of the design and construction and a pattern recognition system; major approaches in statistical and syntactic pattern recognition; some exposure to the theoretical issues involved in pattern recognition system design such as the curse of dimensionality and clear working knowledge of implementing pattern recognition techniques.
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CISC 625 - Digital Image Processing
(3 semester hours)
Prerequisites: CISC 621 , equivalent, or permission of the instructor
Description: This course focuses on explaining and demonstrating the limitations and tradeoffs of various digital image representations, such as computed 3-D images, grayscale versus color, and tools such as wavelet transforms and image compression techniques. Additionally, displaying the ability to manipulate both binary and grayscale digital images using morphological filters and operators to achieve a desired result; showing how higher-level image concepts such as edge detection, segmentation, representation, and object recognition can be implemented and used.
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CISC 661 - Principles of Cybersecurity & Cyber Warfare
(3 semester hours)
Prerequisites: Bachelor of Science degree in Computer and Information Sciences
Description: The course introduces the student to the interdisciplinary field of cybersecurity. Topics include the evolution of information security into cybersecurity and exploring the relationship of cybersecurity to organizations and society. The analyses of the threats and risks to/in these environments are examined. The ultimate goal of this course is for the student to acquire the advanced knowledge required to develop the skills needed to integrate knowledge from this course into a workplace environment.
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CISC 662 - Ethical Hacking Development Lab
(3 semester hours)
Prerequisites: CISC 661
Description: This course integrates cyber risk management into day-to-day operations. Additionally, it enables an enterprise to be prepared to respond to the inevitable cyber incident, restore normal operations and ensure that the enterprise assets and the enterprise’s reputation are protected. This course focuses the student on a broad range of topics relative to risk-based planning for enterprise cybersecurity. The intent is to focus on creating risk assessment and modeling approaches to solve cybersecurity issues, so organizations can build security framework and sustain a healthy security posture. This course analyzes external and internal security threats, failed systems development and system processes and explores their respective risk mitigation solutions through policies, best practices, operational procedures, and government regulations.
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CISC 663 - Cyber Risk Assessment and Management
(3 semester hours)
Prerequisites: CISC 661
Description: This course integrates knowledge accumulated from the prerequisites and serves as a capstone for the concentration in Computer Security. Attention is focused on the techniques for protecting critical information infrastructures and the process of identifying the risk to data and information using case studies, application development, and systems assessment.
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CISC 664 - Advanced Digital Forensics
(3 semester hours)
Prerequisites: CISC 662
Description: Digital Forensics is “the application of computer science and investigative procedures for a legal purpose involving the analysis of digital evidence.” Digital forensics encompasses much more than just laptop and desktop computers. Mobile devices, networks, and “cloud” systems are very much within the scope of the discipline. It also includes the analysis of images, videos, and audio (in both analog and digital format). The goal is to provide digital evidence that are obtained (both in direct and indirect ways) from digital media. The course focuses on the analysis of authenticity, comparison, and enhancement as the main vehicle to obtain digital evidences (both in direct and indirect ways) from digital media.
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CISC 665 - Biometric Security Systems
(3 semester hours)
Prerequisites: CISC 662
Description: Biometric security systems is a rapidly evolving field with applications ranging from accessing one’s computer to gaining entry into a country. Biometric systems rely on the use of physical or behavioral traits, such as fingerprints, face, voice, and hand geometry, to establish the identity of an individual. The deployment of large-scale biometric security systems in both commercial and government applications increases the public’s awareness of this technology. This rapid growth also highlights the challenges associated with designing and deploying such systems. The core computational component of biometric systems is biometric identification (or recognition), and it is indeed a grand challenge in its own right. The purpose of this course is to expose the student to current biometric identification techniques and systems, teach them to coin their own biometric security applications through capturing human biometric traits, creating unique identifications for them, build classification systems that can identify individuals, and make decisions to maintain security parameters.
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CISC 680 - Special Topics in Computer Information Sciences
(3 semester hours)
Prerequisites: None
Description: This course explores a topic or collection of topics of special interest that is timely and in response to critical or emerging topics in the broad field of computer information sciences.
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CISC 681 - Special Topics in Scientific Computing
(3 semester hours)
Prerequisites: CISC 614 or permission of instructor
Description: This course explores a topic or collection of topics of special interest that is timely and in response to critical or emerging topics in the broad field of scientific computing in computer information sciences.
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CISC 682 - Special Topics in Software Engineering and Software Testing
(3 semester hours)
Prerequisites: CISC 593 or permission of instructor
Description: This course explores a topic or collection of topics of special interest that is timely and in response to critical or emerging topics in the broad field of software engineering and software testing in computer information sciences.
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CISC 683 - Special Topics in Cyber Security
(3 semester hours)
Prerequisites: CISC 663 or permission of instructor
Description: This course explores a topic or collection of topics of special interest that is timely and in response to critical or emerging topics in the broad field of cyber security in computer information sciences.
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CISC 690 - Current Topics in Computer Information Sciences
(3 semester hours)
Prerequisites: None
Description: This course explores a topic or collection of current topics that are timely and in response to critical or emerging topics in the broad field of computer information sciences.
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CISC 691 - Current Topics in Scientific Computing
(3 semester hours)
Prerequisites: CISC 614 or permission of instructor
Description: This course explores a topic or collection of current topics that are timely and in response to critical or emerging topics in the broad field of scientific computing computer information sciences.
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CISC 692 - Current Topics in Software Engineering and Software Testing
(3 semester hours)
Prerequisites: CISC 593 or permission of instructor
Description: This course explores a topic or collection of current topics that are timely and in response to critical or emerging topics in the broad field of software engineering and software testing in computer information sciences.
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CISC 693 - Current Topics in Cyber Security
(3 semester hours)
Prerequisites: CISC 663 or permission of instructor
Description: This course explores a topic or collection of current topics that are timely and in response to critical or emerging topics in the broad field of cyber security in computer information sciences.
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CISC 699 - Applied Project in Computer Information Sciences
(3 semester hours)
Prerequisites: GRAD 695 or permission of instructor
Description: This course allows the student to pursue an area of interest that is within the broad scope of Computer Information Sciences. A faculty member will supervise this study.
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Consumer Behavior and Decision Sciences |
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CBDS 520 - Judgement and Decision Making
(3 semester hours)
Prerequisites: None
Description: Human Behavior is the result of complex interactions between physiological and psychological processes. This is an accelerated course designed to give the student a firm understanding of these processes, as well as insight into how this knowledge can be used to garner unique insights which can be leveraged to influence behavior. Foundational topics such as perception, learning and memory, emotion, and cognitive biases and attempt to exploit them via nudging are covered through lectures, discussion or current applied research, and a team project developing an applied behavioral research plan.
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