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Semester Three
This course provides an introduction to the systems development life cycle (SDLC) emphasizing the recent adaptive approaches to SDLC, such as the unified process life cycle and agile methods. The course focuses on the disciplines of business modeling, requirements analysis, and logical design and utilizes the Unified Modeling Language (UML) for analysis, modeling, and design of business-oriented information systems. Information assurance issues of system controls and security are covered with respect to their impact on system requirements and design models.
Credits: 3
Semesters offered: Spring 2024
At least 6 credits from:
This is an introductory course in Digital Forensics where students will learn how to acquire, authenticate and analyze digital evidence. Technical and managerial topics will be explored, providing students with both theoretical and practical hands-on experience using forensic equipment and software. The additional topics of E-Discovery, Data Retention, Litigation, Internal Investigations, Regulatory Compliance and Incident Response will also be discussed within the context of Digital Forensics. EnCase, Access Data FTK and other open source forensic software programs are used in this course.
Credits: 3
Semesters offered: Spring 2024
Special topics course that covers the field of information systems. Topics vary by semester.
Credits: variable
Semesters offered: Fall 2023 | Spring 2024
Investment in government and business infrastructure has lead to the accumulation of vast amounts of data in recent years. This course will discuss how techniques from convex optimization can be used to extract useful knowledge and business value from the data collected. It introduces students to the theory of convex optimization of relevance to managerial decision making and machine learning. Topics include convex sets and functions, formulation of convex optimization problems, and convex optimization algorithms including gradient, sub-gradient, proximal and interior point methods. Numerous examples will be chosen from machine learning problems including classification, regression and clustering. Students will have hands on experience with the R programming language and optimization packages including MOSEK. We will examine real world examples and case studies from text mining, medical applications, fraud detection, finance, and social networks.
Credits: 3
Semesters offered: Spring 2024
The aim of the course is to provide students with an overview of measures, models and methods of analysis that can be used to study social networks to further business interests within organizations using data from internal and external IT data sources. The focus of the course will be on modeling methods and IT tools to analyze large volumes of data for predictive and descriptive analysis. Students will also learn the use of standard statistical software packages such as SAS and special network analysis software.
Credits: 3
Semesters offered: Spring 2024
The main objective of this course is to introduce students to the theory and practice of doing business via the Internet. Topics include: elements of the infrastructure of electronic commerce; technologies and applications in electronic commerce; using electronic commerce for the creation of competitive advantages; planning technology-based strategies to achieve business goals. The course will rely heavily on research and peer learning with the instructor serving as catalyst, facilitator, and evaluator in a collaborative environment.
Credits: 3
Semesters offered: Spring 2024
Capstone (1.5 credits)
Any domain elective (1.5 - 3 credits)
Total credits: 12
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The focus of this course will be on technology management and developing an Internet-based business or extension to an existing business. The course will integrate concepts from economics, organizational strategy, entrepreneur-ship, and Web design. Topics to be covered in the course include: aligning technology and strategy; models of diffusion and innovation; characteristics of information and digital goods; identifying potential Web-applications and information products for solving a problem and/or identifying a business opportunity; intellectual property rights; pricing issues related to information goods; developing a business plan for a venture capital proposal; launching the e-business; designing Web based applications for usability; and strategies for successfully implementing systems. Case studies, lectures, guest speakers, and an integrated E-business project will be used to understand the complexities of the current business environment.
Credits: 3
Semesters offered: Fall 2023 | Spring 2024
This course is designed to provide students with a basic understanding of database management systems (DBMS) and the skills needed to design and implement a relational database. Students will be introduced to data modeling concepts, modeling tools, the process of transforming conceptual models into relational database designs, and finally the steps needed to implement those designs. Emphasis is placed on Entity-Relationship diagramming, data normalization, database administration, and data definition, data manipulation and query development using Structured Query Language (SQL). Other topics covered include: object-oriented databases, database security and integrity, web/database integration, application development in a Client/Server environment, distributed databases, data warehousing, data mining and knowledge management via the Internet to support electronic commerce. Readings, lectures, interactive case assignments and a database design project reinforce the role of DBMS in supporting organizational systems, transaction processing and decision support applications.
Credits: 3
Semesters offered: Fall 2023
This course covers the fundamental concepts in statistics that are essential for business and data analytics. Probability Theory and Sampling Theory are the two foundations of both descriptive and predictive forms of analytics. Building from these foundations, students are introduced to the statistical concepts of data analysis. Topics covered include: descriptive statistics, probability theory, discrete and continuous probability distributions, sampling theory, estimation, hypothesis testing, distribution fitting using chi-square tests, simple and multiple linear regression, introduction to causal modeling and predictive data analytics. MS-Excel based data modeling will be used extensively throughout the exposition of the concepts.
Credits: 3
Semesters offered: Fall 2023 | Spring 2024
*MGQ 606 is only required for students without a business background.
• Three electives (3 credits each)
*Students without a business background are required to take MGG 503 and MGQ 606; students with a business background may be waived from these courses.
MGS 613 is a component of the Information Assurance certificate. If you take three of these elective courses, you will be eligible for the certificate in addition to the MS degree. See additional information on the Information Assurance certificate.