Intelligence and Security Analysis Certificate

This course provides a thorough major's level introduction to genetics and heredity. It will cover the fundamentals of genetic information, its transmission from parents to offspring [heredity], its phenotypic and molecular expression in cells and organisms, replication and repair of genetic material within a cell, and its population impacts. Also included are the modern techniques of genetics including: gene mapping, cloning, genome manipulation and mutation. Knowledge of species' genomes, their genes, their inheritance, and how genes impact individuals and/or populations has rapidly become an integral part of almost every aspect of biology. From public health to ecology - genetics touches all.

Full course description for Principles of Genetics

This course covers the taxonomy, structure, function and ecology of microbes including bacteria, viruses, fungi and protista. Additional topics include microbial pathogenesis, the response of the mammalian immune system to microbial infection, microbial metabolic diversity and microbial biotechnology. Labs include use of microscope, survey of types of microbes, isolation of microbes from the environment, identification of microbes, staining of bacteria, action of antibiotics and disinfectants, counting of bacteria in food and water and use of microbes in food and beverage production. Intended for Biology, Biochemistry, Environmental Science or Life Science Teaching majors.

Full course description for Advanced Microbiology

In this course, students continue not only to learn how to identify and collect digital evidence through forensics search tools, but also to study the emerging data mining techniques. The topics include how to design a plan for a computer crime investigation; how to select a computer software tool to perform the investigation; how to articulate the laws applying to the appropriation of computers for forensics analysis; how to verify the integrity of the evidence being obtained; how to prepare the evidence collected for the use in the court; and how to present the evidence as an expert eyewitness in court. Some hypothetical and real cases are also discussed in class.

Full course description for Digital Evidence Analysis

This course provides a comprehensive, hands-on exploration of mobile device security and forensic investigation techniques, equipping students with both foundational principles and advanced practical skills. Through the use of modern forensic tools and methodologies, students will learn to conduct structured investigations that adhere to forensically sound procedures, ensuring the integrity and admissibility of digital evidence in legal proceedings. The course covers the entire mobile forensic process for Android and iOS devices, including data acquisition, extraction (including recovery of deleted data), analysis, and forensic reporting. Students will gain proficiency in identifying and examining digital artifacts, utilizing command-line and graphical open-source forensic tools, and assessing mobile application security to uncover vulnerabilities. Additionally, the course emphasizes the ethical and legal considerations essential for forensic investigators and teaches students how to…

Full course description for Mobile Device Security and Forensics

Medicinal chemistry allows the advanced chemistry student to explore the considerations of drug design and development as well as case studies on how different classes of therapeutic agents act in the human body. Topics include drug targets, drug sources, structure-activity relationships, pharmacokinetics, pharmacodynamics, and the modern drug discovery pipeline. This class is suggested for those students intending to continue in health sciences.

Full course description for Medicinal Chemistry

This course is intended for Chemistry majors and minors; this course contributes to Category 2 electives for the Chemistry major. Topics include instrumental methods of analysis including spectrochemical, kinetic and chromatographic methods. Includes 3 credits dedicated to lecture and 2 credits to lab.

Full course description for Instrumental Analysis

This course focuses on theories, concepts, narratives, and myths of crime and delinquent behavior. Contemporary issues and controversies within the criminal justice field are explored in social, political, and economic contexts. Special emphasis is placed on the roles of race, class, gender, and culture in relation to the etiology, prevention, control, and treatment of crime and delinquency. This course is committed to general theoretical debate, examination of the interrelation between criminological theory and research, and empirical analyses of policy and practice.

Full course description for Criminology and Public Policy

This course provides students with international perspectives on criminal justice. Through a comprehensive review of cross-national research data, students examine the features, successes and failures of various distinct criminal justice systems around the globe and use that information to evaluate the American criminal justice system. By exploring justice institutions in other parts of the world, students learn that criminal justice systems are shaped by the values, norms, customs or standards of behavior characteristic of the society in which they are found.

Full course description for Comparative Criminal Justice

This course presents an overview of white collar crime. Students explore theories of white collar crime and corporate criminal liability. The investigation, prosecution and sentencing of white-collar offenders are examined. "Crime in the suites" is compared to "crime in the streets." Issues related to diversity are explored.

Full course description for White Collar Crime

This course examines the fundamental principles and practices of emergency management including how it functions within the homeland security enterprise. Mass shootings, acts of terror, infrastructure collapse, and natural disasters all are examples of emergencies examined in this course. This course also explores the human and economic costs of emergencies and the intended and unintended consequences of intervention.

Full course description for Emergency Management for Criminal Justice

This course examines the growth of technology in modern society and how the use of that technology is affecting law enforcement practices in the United States. The course further examines the types of technology, its impact on policing practices, and the impact on the use of technology on civil rights including the public perception of the violation of individual privacy.

Full course description for Technology and Modern Policing

This course provides an in-depth exploration of fundamental concepts in Artificial Intelligence (AI) and Machine Learning (ML), with a focus on their applications in cybersecurity. Students will analyze AI principles, classical algorithms, and modern ML techniques, evaluating their role in enhancing security protocols and predicting cyber threats. The course emphasizes ethical considerations, governance frameworks, and the responsible use of AI technologies. Through case studies and hands-on applications, students will apply AI and ML tools to solve complex cybersecurity challenges, developing critical skills for securing digital environments.

Full course description for Fundamentals of AI and ML in Cybersecurity

This course provides students with the practical skills and theoretical knowledge required to proactively hunt for cyber threats and conduct advanced intelligence analysis. Students will explore methods to synthesize complex data into actionable intelligence, leveraging tools for data mining, information gathering, and threat management. Emphasis is placed on understanding and applying Indicators of Compromises (IOCs), adversary Tactics, Techniques, and Procedures (TTPs), and integrating cyber threat intelligence into security operations. Through hands-on exercises, case studies, and projects, participants will gain proficiency in detecting, investigating, and mitigating advanced threats. The course also addresses the ethical and legal dimensions of cyber intelligence, equipping students with the expertise to conduct thorough and effective intelligence operations. This curriculum prepares students to utilize advanced methodologies and technologies to enhance the accuracy and…

Full course description for Cyber Threat Hunting and Intelligence Analysis

As medical devices become increasingly interconnected and reliant on digital technologies, they introduce new cybersecurity risks that can impact patient safety and healthcare operations. This course provides an in-depth examination of the security challenges, regulatory requirements, and risk management strategies associated with medical device cybersecurity. Students will explore the evolving landscape of medical device threats and vulnerabilities while analyzing relevant cybersecurity regulations, standards, and best practices. Through case studies, technical labs, and real-world scenarios, students will develop the foundational knowledge necessary to secure medical devices throughout their lifecycle. Topics include threat modeling, security controls, patching strategies, incident response, and emerging technologies such as artificial intelligence (AI), mobile health applications, and wearables. By the end of the course, students will be equipped with practical skills to assess,…

Full course description for Cybersecurity for Medical Devices

The medical device industry faces unique cybersecurity challenges due to the direct impact of security threats on patient health and safety. To address these risks, cybersecurity professionals leverage established control frameworks and risk management methodologies to assess and mitigate potential threats.This course provides an in-depth exploration of cybersecurity risk management in the medical device sector, emphasizing the application of industry-recognized control frameworks such as those developed by the National Institute of Standards and Technology (NIST) and the International Organization for Standardization (ISO). Students will gain hands-on experience in identifying, evaluating, and mitigating cybersecurity risks through threat modeling, security assessments, and the implementation of appropriate security controls. Key topics include cybersecurity risk assessment methodologies, security-by-design principles, technical solutions for securing connected medical devices, and…

Full course description for Risk and Security Controls for Medical Devices

The integration of Internet of Things (IoT) technologies with critical infrastructure has revolutionized essential services such as energy, water, transportation, and healthcare, including medical devices. However, this growing connectivity also introduces significant cybersecurity risks. This course examines the security challenges unique to IoT systems in critical infrastructure and explores effective strategies for protecting these environments from cyber threats. Students will analyze IoT-specific vulnerabilities, assess security risks, and evaluate potential attack vectors targeting critical infrastructure systems. The course covers essential cybersecurity principles, risk assessment methodologies, threat modeling techniques, security protocols, and incident response strategies tailored for IoT environments. Through hands-on labs, case studies, and applied projects, students will develop the technical expertise necessary to design, implement, and manage security measures that…

Full course description for IoT and Critical Infrastructure Security

To properly secure an organization¿s information infrastructure and assets, periodic assessments of security posture at multiple levels are critical. This course provides a comprehensive approach to penetration testing and vulnerability assessment, covering key phases from reconnaissance to exploitation and post-exploitation techniques. Students will apply industry-standard penetration testing methodologies, use advanced tools such as Nmap, Wireshark, Metasploit, and Snort, and conduct controlled cybersecurity testing to simulate real-world attacks. Emphasis is placed on reporting and remediation, ensuring students can document findings and recommend actionable security improvements. This course aligns with CompTIA PenTest+ certification objectives, equipping students with essential offensive security skills needed for penetration testing, ethical hacking, and vulnerability analysis.

Full course description for Vulnerability Assessment and Penetration Testing

This course focuses on applying Artificial Intelligence (AI) in defensive cybersecurity operations and security assessments. Students will explore AI-driven tools and techniques to enhance threat detection, strengthen defenses, and conduct comprehensive security assessments in a secure and ethical manner. Emphasis is placed on practical applications, critical evaluation of AI methods, and the ethical implications of utilizing AI to protect systems. Through hands-on exercises and case studies, students will develop expertise in employing AI technologies to address modern defensive cybersecurity challenges.

Full course description for Applied AI in Cyber Defense Operations

In today's rapidly evolving digital landscape, cryptography is fundamental to securing sensitive information, protecting communications, and maintaining data integrity. This course provides a comprehensive foundation in applied cryptography, covering the historical evolution, mathematical principles, encryption techniques, and real-world implementations of cryptographic security. Students will explore symmetric and asymmetric encryption methods, including Feistel networks, S-Box design, RSA, Diffie-Hellman, and Elliptic Curve Cryptography, while also applying cryptographic principles to security technologies such as SSL/TLS, Virtual Private Networks (VPNs), and military-grade encryption. The course delves into cryptanalysis techniques to assess cryptographic vulnerabilities and adversarial attack methods used to bypass encryption defenses. Additionally, students will evaluate the future of cryptography, including quantum-resistant encryption, cloud-based cryptographic applications,…

Full course description for Cryptography for Cybersecurity Practitioners

In today's digital landscape, safeguarding information as a vital asset is paramount. This course delves into the operational aspects of managing and securing corporate network infrastructures and business information systems. Students will develop situational awareness of organizational networks and learn to manage and maintain a defensive infrastructure comprising log servers, network firewalls, and intrusion detection systems. Key topics include information security operations, access control, risk management, business continuity planning, disaster recovery, and ethics. Additionally, the course addresses the challenges of securing cloud assets, aligning with select objectives from the CompTIA Cloud+ certification, to equip students with strategies to enhance cloud security and address threats such as misconfigurations, unauthorized access, and data breaches. Overlap: ICS 484.

Full course description for Cyber Operations

In the face of escalating cyber breaches and intrusions, organizations seek professionals adept at identifying and responding to security incidents proactively. This course offers an in-depth exploration of Digital Forensics and Incident Response (DFIR) methodologies, emphasizing frameworks such as NIST and US-CERT. Students will learn to effectively detect, analyze, contain, eradicate, and recover from cyber attacks within enterprise networks. Throughout the course, students will develop expertise in identifying threat actors and security breaches, analyzing artifacts and logs, conducting post-mortem analyses, and implementing and refining mitigation strategies. The curriculum aligns with the CompTIA CySA+ objectives, ensuring students are equipped with the competencies required for effective cybersecurity analysis and incident response. By the end of the course, students will be proficient in using industry-standard forensic tools, assessing cyber attack stages, and developing…

Full course description for Cyber Incident Response and Handling

This course explores specialized and emerging topics in cybersecurity, addressing cutting-edge threats, technological advancements, and evolving best practices not covered elsewhere in the Cybersecurity program. Designed to keep pace with the rapidly changing cybersecurity landscape, this course provides students with opportunities to analyze and evaluate recent developments, apply advanced tools and methodologies, and synthesize scholarly and professional literature to solve real-world cybersecurity challenges. Emphasis is placed on ethical decision-making, professional responsibility, and adherence to legal and industry standards in addressing contemporary cybersecurity issues. The specific topic of study varies by semester, ensuring alignment with current industry trends and demands.

Full course description for Special Topics in Cybersecurity

Statistical machine learning (often referred to simply as statistical learning) has arisen as a recent subfield of statistics. It emphasizes the interpretability, precision, and uncertainty of machine learning models. This course assesses the accuracy of several supervised and unsupervised machine learning models for both regression and classification. Topics include the bias-variance trade-off, training and test datasets, resampling methods, shrinkage and dimension reduction methods, non-linear modeling techniques such as regression splines and generalized additive models, and decision tree-based methods. Applications include examples from medicine, biology, marketing, finance, insurance, and sports.

Full course description for Applied Machine Learning

The course focuses on how to design and build process, object and event models that are translatable into project specifications and design. Topics include an overview of systems analysis and design; a framework for systems architecture; design and development using data modeling; object modeling, entities, relationships, attributes, scope rules and influences; and event models, messaging and application activation.

Full course description for Software Design Models

Interaction design is an interdisciplinary field integrating theories and methodologies across several disciplines such as computer science, cognitive psychology, technical communication, user experience, human factors, information technology and engineering design. In this course, students are introduced to the theoretical knowledge of and practical experience with concepts of interaction design, design theory and techniques, and implementation and evaluation of interfaces. Topics covered include: interaction design, human-computer interaction, prototyping, usability evaluation, universal design, multimodal interfaces, and virtual reality. In addition to lectures, students will work on individual assignments and team projects to design, implement, and evaluate various interactive systems and user interfaces.

Full course description for Interaction Design for User Experience

This course teaches students full stack Web application development using the Model View Controller (MVC) design pattern. Students will learn using a template engine for rendering front end, using a Web Framework that supports MVC and Web security, and database persistence using Object Relational Mapping (ORM) and SQL statements. Students will build a medium size database-driven web application that supports user management. Students should have some experience with object-oriented programming concepts including inheritance, and data structures such as lists and maps.

Full course description for Model View Controller Architecture-based Web Application

Covers design and development of parallel and distributed algorithms and their implementation. Topics include multiprocessor and multicore architectures, parallel algorithm design patterns and performance issues, threads, shared objects and shared memory, forms of synchronization, concurrency on data structures, parallel sorting, distributed system models, fundamental distributed problems and algorithms such as mutual exclusion, consensus, and elections, and distributed programming paradigms. Programming intensive.

Full course description for Parallel and Distributed Algorithms

Computer networking is the core of our today's lives and is rapidly advancing. Hence, computer scientists must be familiar with the fundamentals of computer networking. This course introduces computer networking, including network topologies, network protocols, algorithms, layering concepts (for example, ISO/OSI, TCP/IP reference models). After covering the principles of computer networking, the course introduces network security fundamentals, including the weaknesses of networks and networked systems and their countermeasures.

Full course description for Networks and Security

Principles, techniques, and algorithms for the design and implementation of modern operating systems. Topics include operating system structures, process and thread scheduling, memory management including virtual memory, file system implementation, input output systems, mass storage structures, protection, and security. Students will implement process, memory, and file management algorithms.

Full course description for Operating Systems

The need to solve systems of linear equations frequently arises in mathematics, the physical sciences, engineering and economics. In this course we study these systems from an algebraic and geometric viewpoint. Topics include systems of linear equations, matrix algebra, Euclidean vector spaces, linear transformations, linear independence, dimension, eigenvalues and eigenvectors.

Full course description for Linear Algebra and Applications

This is a calculus-based probability course. It covers the following topics. (1) General Probability: set notation and basic elements of probability, combinatorial probability, conditional probability and independent events, and Bayes Theorem. (2) Single-Variable Probability: binomial, geometric, hypergeometric, Poisson, uniform, exponential, gamma and normal distributions, cumulative distribution functions, mean, variance and standard deviation, moments and moment-generating functions, and Chebysheff Theorem. (3) Multi-Variable Probability: joint probability functions and joint density functions, joint cumulative distribution functions, central limit theorem, conditional and marginal probability, moments and moment-generating functions, variance, covariance and correlation, and transformations. (4) Application to problems in medical testing, insurance, political survey, social inequity, gaming, and other fields of interest.

Full course description for Probability

This course provides a practical introduction to computational mathematics with a strong emphasis on the development of programming skills for mathematical problem solving. The course introduces essential tools for numerical modeling, data handling, and simulation. Selected numerical analysis topics such as root-finding, Gaussian elimination, interpolation, and error analysis are included. The course also delves into stochastic models, including Markov chains, Poisson processes, and queueing theory, and implementation of Monte Carlo simulations to analyze real-world systems. Throughout the course, students gain experience writing code and interpreting analytical and computational results in business and industry contexts.

Full course description for Computational Mathematics

This course provides students with significant problem-solving experience through investigating complex, open-ended problems arising in real-world settings. Working in teams, students apply mathematical modeling processes to translate problems presented to them into problems that can be investigated using the mathematical, statistical, and computational knowledge and thinking they have gained from previous coursework. Significant emphasis is placed on justifying approaches used to investigate problems, coordinating the work of team members, and communicating analyses and findings to technical and non-technical audiences.

Full course description for Advanced Mathematical Modeling

Business Intelligence is the user-centered process of exploring data, data relationships and trends - thus helping to improve overall decision making for enterprises. This course addresses the iterative processes of accessing data (ideally stored in the enterprise data warehouse) and analyzing data in order to derive insights and communicate findings. Moreover, the course also addresses the use of software tools for analysis and visualization of data, especially report design along with the use of dashboards.

Full course description for Business Intelligence and Analytics

This course is designed to present the elements of an integrated security compliance platform from a technical and legal perspective. Issues such as provide risk assessment, legal compliance, identity management, provisioning, access management, and monitoring and audit activities will be discussed.

Full course description for MIS Auditing and Security Controls

This course examines the role of information systems applications involved in supporting supply chain and logistics operations. Topics covered include electronic purchasing/RFQ, Warehouse management systems, Warehouse Technology, Bar coding / RFID, mobile solutions for distribution and field force automation, MRP/ERP, Enterprise Asset Management and the Internet of Things, Transportation systems. Special emphasis is placed on building analytical skills for the detailed assessment of vendor software solutions in the supply chain arena.

Full course description for Supply Chain Information Systems

Managers need to know how to manage the diverse distributed computing environments in which they work, and leverage the opportunities these architectures provide. Integration of data and users, graphics and telephony are illustrated through emphases on client/server and N-Tier architectures, Internet, intranet/extranet, groupware, mobile, cloud and other technologies. This elective course reviews state-of-the-art technologies in each of the basic software and hardware arenas, while emphasizing management models and higher-level analysis, including the relationship with general database strategy and data warehousing. Practical projects are assigned, giving students real-world opportunities to use the tools to enhance their work and build productivity. Theory and models are taught with a management perspective as opposed to platform-specific training. Participants are asked to complete a comprehensive and applied class project and final exam.

Full course description for Management of Distributed Computing

This course explores the range of available network and telecommunications technologies and how they can be used to facilitate information access and dissemination at all levels of an organization and through the Internet. Trends of telecommunications services are analyzed. Telecommunications trends in the United States and Europe are addressed in detail. A range of emerging telecommunications services is explored as well as how such services radically alter the ways that organizations gather information for decision making. The widespread use of mobile technologies, the cloud and the World Wide Web has required many changes both in architecture and concept. The student learns how to manage these new environments.

Full course description for Telecommunications and Internet Management

A time series is a sequence of observations on a variable measured at successive points in time or over successive periods of time. This course provides an introduction to both standard and advanced time series analysis and forecasting methods. Graphical techniques and numerical summaries are used to identify data patterns such as seasonal and cyclical trends. Forecasting methods covered include: Moving averages, weighted moving averages, exponential smoothing, state-space models, simple linear regression, multiple regression, classification and regression trees, and neural networks. Measures of forecast accuracy are used to determine which method to use for obtaining forecasts for future time periods.

Full course description for Time Series Analysis and Forecasting