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BSAN 160: Business Analytics and Data Visualization
Credits: 4.0
Level: UG
The field of business analytics is concerned with managing and analyzing complex, high-dimensional data to support business decisions and improve firm performance. Data visualization is emerging as an essential complement to business analytics that greatly facilitates managerial understanding and decisions. This course introduces state-of-the-art techniques for data management, analysis and visualization in business contexts. It will emphasize practical challenges involving complex, real-world data and include several case studies and hands-on exercises with leading data analysis and visualization software.
BSAN 360: Programming for Data Analytics
Credits: 4.0
Level: UG
The mission of this course is to immerse students in the technical challenges associated with contemporary data analytics as applied to business processes and data-driven decision making. To achieve this mission, the course will introduce modules covering the state of the art in the areas of statistical programming as applied to data analysis for business problems.
BSAN 460: Business Analytics Senior Project
Credits: 4.0
Level: UG
The senior project serves as a capstone for business analytics majors. The course provides an opportunity for students to develop a project that draws on their skills in the areas of data management, mathematical modeling, and statistical analysis to support data driven decision-making processes. Students often choose a project in the area of a second major (marketing, finance, etc.) and thus the project provides deeper insight into organizational decision-making in a functional area of business.
BSAN 710: Business Analytics Capstone Project
Credits: 3.0
Level: GR
Contemporary business is embedded in a data-driven economy. Large datasets must be analyzed to understand the financial, economic, technological, societal, social, and environmental impacts of products, services, and policies. In this course, students execute a data-driven project in order to demonstrate their ability to meet this challenge. Projects draw on multiple quantitative skills: data management, mathematical modeling, and statistical analysis to support decision-making processes. Emphasis is also placed on ability to develop business insights from data and present findings to decision makers.
MIS 200: Management Information Systems
Credits: 4.0
Level: UG
Introductory course to Management of Information Systems, a core business function. The course examines how information systems (i.e., information technology, people, procedures, and data) help add value to an organization, and integrate the various functional areas of a business (e.g., accounting, marketing, etc.).
MIS 342: Systems Analysis and Design
Credits: 4.0
Level: UG
Introduces structured and object-oriented systems analysis and design methodologies in classroom and hands-on lab settings. Discusses system life-cycle concepts and techniques such as dataflow diagrams, structure charts, and E-R diagrams. Also covers object-oriented design, prototyping, and rapid application development approaches.
MIS 343: Database Design and Implementation
Credits: 4.0
Level: UG
Covers data and file structures, object-oriented database design, and the use of SQL for querying databases. Discusses logical and physical database design and offers hands-on experience with commercial database management systems (DBMSs).
MIS 346: Management Information Systems Strategy
Credits: 4.0
Level: UG
To discuss Management of Information Systems, and then to elaborate on its application to organizational change, especially to reengineering. This course will introduce the student to central aspects of MIS policy and strategy in the first part of the course and then use these concepts to understand reengineering in the latter part of the course.
MIS 347: Domestic and Global Outsourcing Management
Credits: 4.0
Level: UG
To introduce the student to issues in managing the outsourcing of Information Systems. This will be done in a mixture of lectures and student team presentations. The lectures will introduce the students to some of the central themes of outsourcing IS by summarizing current literature. Parallel to these lectures students will form study teams to investigate other important topics of IS outsourcing through a guided literature reading.
MIS 361: Information System Project Management
Credits: 4.0
Level: UG
The course is structured around the key phases of a project lifecycle â initiating a project, planning a project, executing a project, controlling a project, and closing out a project. It also pays specific attention to the nine knowledge areas of Project Management as defined by the Project Management Institute (PMI)âs Project Management Body of Knowledge (PMBOK): project scope, cost, time, integration, quality, communication, risk, human resources, and procurement management. Additionally, students will be introduced with choices in project management approaches (such as SAP Project Management and APM.
MIS 364: Information Security Systems Management
Credits: 4.0
Level: UG
This course provides a foundation for the management of information security systems. It includes an overview of some key technology concepts that are pertinent for the management of information security systems, while also exploring core legal, governance, risk management, and compliance concepts. The course is taught as a combination of lectures, hands-on group activities, readings and videos.
MIS 612: Aligning Information Systems and Business Strategies
Credits: 3.0
Level: GR
In this course, we will examine a variety of IS issues which are important to organizations, including information systems strategy, impact of IT on organization and work processes, business process reengineering, systems architecture and project management.
MIS 615: Aligning Information Technologies and Operations
Credits: 3.0
Level: GR
Information Technology (IT) infrastructure must be aligned with an organization’s strategy and operations to ensure optimal benefits. This class uses the principles of DevOps to examine operational alignment for IT infrastructure. Students learn how different IT infrastructures are matched to different operational profiles to maximize effectiveness. Students will also be exposed to cross-domain alignment: the ways in which top-level IT and business strategies affect operations. This includes how IT strategy affects business operations and how business strategy guides IT operations and infrastructure. Finally, students learn how new modes of system delivery meet the needs of business operations in hypercompetitive environments.
MIS 624: Systems Analysis & Design
Credits: 3.0
Level: GR
Examines concepts of the information systems development lifecycle and methods for analyzing user information requirements. Focuses on structured techniques for designing a system, managing its development and testing, performing feasibility analyses, and ensuring both user satisfaction and achievement of functional requirements. Covers techniques such as rapid application development (RAD), prototyping, and joint analysis and design (JAD) in detail. Also covers techniques such as data flow diagramming, logical database design, and user interface design.
MIS 625: Management of Information Technology Operations
Credits: 3.0
Level: GR
Contemporary Information Technology (IT) ecosystems include multiple infrastructure components, applications, and performance monitoring tools, which may be located within or external to an organization. In this course, students learn how a firm’s IT assets are procured, deployed, integrated, and managed. This includes licensing and service level agreements (SLAs), cost center (shared services) and profit center approaches for IT infrastructure, approaches for identifying and remediating problems with IT operations, and best practices for securing IT assets. Machine learning for IT operations management is also covered.
MIS 632: Database Analysis and Design for Business
Credits: 3.0
Level: GR
Focuses on database analysis and design for a wide range of business functions. Stresses the fundamentals of sound logical database design using techniques such as entity/relationship modeling. Examines the relational database and the object-oriented approaches to database design and handles specific design methods, such as normalization. Also discusses physical database design and data storage methodologies such as raid and hierarchical storage management (HSM). Involves a hands-on orientation with the use of tools such as oracle, Access, and Visual Basic.
MIS 636: Python Programming for Business Applications
Credits: 3.0
Level: GR
This course focuses on the fundamentals of computer programming with Python and its applications in business practices. Specific emphasis will be placed on solving optimization problems and building predictive models widely used by industry (e.g., network optimization and churn models). Using the Python interpreted programming language, students will develop coding skills and be able to efficiently solve today’s relevant business problems.
MIS 641: MIS Policy and Strategy
Credits: 3.0
Level: GR
Ties together concepts from all areas of management and the economic, behavioral, functional, and technical aspects of MIS. Defines overall and context-specific information needs of organizations and focuses on the role of MIS in meeting these needs. Examines alternatives for matching MIS department structures and operations to the structures, strategies, and behaviors of organizations. Also investigates, proposes, and analyzes management policy issues relating to the management of the MIS function.
MIS 642: Emerging Information Technologies in Business
Credits: 3.0
Level: GR
This course explores the current and potential future impact of emerging technologies on organizations and their core business operations, namely, accounting, finance, management and operations. Students will gain insights into these technologies and examine the challenges and opportunities of integrating the technologies into the organization. Other topics covered include managing change and legal and privacy issues resulting from emerging technologies.
MIS 643: Digital Platform Management
Credits: 3.0
Level: GR
Digital platforms exist in various forms, such as electronic markets where participants exchange products and services, or core IT products that bring communities of businesses and consumers together. Incumbents as well as start-ups can build digital platforms to enter new markets or launch digital innovations. This course introduces students to the various types of digital platforms and the opportunities they offer. By studying the dynamics in this arena, students learn about the various forms of coordination and competition that exist in digital ecosystems, and what strategies firms have employed to succeed there. Additionally, students gain understanding of the changes that take place in markets and industries when digital platforms emerge.
MIS 652: Business Agility and IT
Credits: 3.0
Level: GR
This introductory course will cover the core principles, practices, and frameworks of agile practices and how they can be utilized to drive successful delivery inside an organization. These concepts also tie into the idea of organizational change, and how an enterprise can use these practices and principles on a large scale to shift an organization to one that reacts to change and opportunity at speed to enable success. The course will also emphasize these learnings in the context of real-world Management Information Systems (MIS) projects. Additionally, some related emerging topics such as international/distributed project management and design thinking all in the context of agile methodologies will also be introduced.
MIS 653: Design Thinking for Digital Innovations
Credits: 3.0
Level: GR
Design Thinking is a human-centered, collaborative approach to designing new services and products that has become popular in the context of digital innovations. Design thinking can also be applied to strategies and roadmaps, organizational structures, and processes-related problems. This course teaches the core principles, practices, and frameworks of design thinking, and how they can be utilized to drive successful business outcomes. Topics discussed include: the philosophy, concepts of design thinking, the process (empathize, define, ideate, prototype, test, implement), customer and team collaboration, identification of customer needs, and value-driven product design.
OPM 200: Operations Management
Credits: 4.0
Level: UG
Provides students with an understanding of the transformation process, which converts inputs into outputs. This is the primary function of every manufacturing/service organization, and how it adds value to the outputs. Discusses the decision-making process and techniques for planning and controlling the operations function.
OPM 315: Service Operations Management
Credits: 4.0
Level: UG
Analyze service systems from the viewpoint of the operations manager to understand where and in what ways the body of knowledge developed in operations management, strategy, and marketing can be applied and where other approaches are necessary. Focus on understanding what customers want, designing systems and procedures delivering services, and controlling quality.
OPM 324: Operations Planning
Credits: 4.0
Level: UG
This course offers students who have completed an introductory study of operations management the opportunity to further their knowledge of the discipline. Building on students’ understanding of essential operational structures, the course focuses on concepts and tools that allow operations managers to allocate available resources effectively in view of demand from internal and external customers. Key topics studied include design of products, services, and facilities; forecasting demand; material requirements planning; and analysis of waiting lines and inventory systems.
OPM 341: Supply Chain Management
Credits: 4.0
Level: UG
Presents and explains the concepts, insights, practical tools and decision support systems that are important for the effective managements of supply chains. Long-term strategic design issues, shorter-term tactical and operational issues are closely examined. State-of-the-art concepts of globally optimal decision making, often across traditional organizational boundaries are emphasized.
OPM 342: Sustainable Supply Chain Management and Logistics
Credits: 4.0
Level: UG
This course is a survey of solutions and techniques to design, evaluate, and improve supply chain operations with the goal of promoting environmental, social, and economic sustainability. Topics include product and process design for sustainability, cradle-to-cradle design, âgreenâ sourcing and procurement, reverse logistics and closed-loop supply chains, supply chain coordination for sustainability, end-of-life management, facilities location and design, sustainable transportation and logistics solutions.
OPM 344: Revenue Management
Credits: 4.0
Level: UG
The course will convey to future business leaders innovative ways to boost profitability. It will explore how firms can improve the operational management of the demand for their products (goods or services) to more effectively align it with their supply through business analytics lenses. It will introduce quantitative methods to improve decision-making, with special emphasis on spreadsheet modeling and analysis.
OPR 320: Linear Models for Decision Making
Credits: 4.0
Level: UG
Applies modeling and mathematical techniques to complex decision problems in business, with a focus on deterministic systems. Covers linear programming, integer programming, goal programming and networks.
OPR 330: Advanced Decision Making and Simulation
Credits: 4.0
Level: UG
Applies modeling and mathematical techniques to complex decision problems, with a focus on nonlinearity and uncertainty in the business environment. Covers nonlinear programming, dynamic programming, queuing theory, Markov Processes, decision analysis and simulation.
OPR 601: Managerial Decision Models and Simulation
Credits: 3.0
Level: GR
Introduces students to the basic modeling tools and techniques for making managerial decisions in a complex and dynamic business environment. Topics include linear, discrete, and nonlinear optimization, multicriteria decision making, decision analysis under uncertainty, and simulation.
OPR 620: Operations Research I
Credits: 3.0
Level: GR
Covers theory and applications of linear programming, including the simplex method, sensitivity analysis and duality, formulation and solution of transportation and network optimization problems. Extensions include game theory, quadratic programming, financial optimization, and emerging solution techniques such as interior-point methods.
OPR 622: Operations Research II
Credits: 3.0
Level: GR
This course covers modeling and solving optimization problems under uncertainty. Topics will include stochastic processes, queueing systems and dynamic programming.
OPR 624: Advanced Mathematical Program
Credits: 3.0
Level: GR
This course covers algorithms and software development for nonlinear programming, integer programming, and global optimization. Special emphasis is placed on solution methods for constrained and unconstrained nonlinear optimization, a survey of methods for integer linear and nonlinear optimization, and search techniques for global optimization.
OPR 626: System Simulation
Credits: 3.0
Level: GR
This course focuses on the application of simulation in analyzing complex systems. The corresponding theory is also covered.
OPR 922: Operations Research Methods I
Credits: 3.0
Level: GR
Covers theory and applications of linear programming, including the simplex method, sensitivity analysis and duality, formulation and solution of transportation, and network optimization problems. Extensions include integer programming, quadratic programming, and emerging solution techniques such as interior-point methods.
OPR 998: Dissertation Research in Operations Research
Credits: 1.0-12.0
Level: GR
Dissertation Research.
POM 510: Operations and Supply Chain Management
Credits: 2.0
Level: GR
This course is an introduction to some selected topics in the field of production and operations management. It covers process analysis, quality management, queueing and capacity management, lean operations, inventory management, aggregate planning and supply chain management.
POM 601: Operations Management
Credits: 3.0
Level: GR
This course is an introduction to the field of production and operations management (POM). Production and operations activities such as forecasting, capacity planning, inventory control, scheduling, and ensuring quality are discussed from the supply chain perspective. The philosophies and characteristics of lean operations and responsive manufacturing/service systems are highlighted.
STAT 201: Introduction to Business Statistics
Credits: 4.0
Level: UG
This introductory first course in business statistics focuses on applications of data analysis and statistics in business and economics. Topics covered include descriptive statistics and graphical presentation, probability, statistical inference, and simple regression analysis.
STAT 202: Business Statistics II
Credits: 4.0
Level: UG
This second course in business statistics focuses on widely used data analysis techniques in business and economics. Topics include two sample procedures, categorical data analysis, analysis of variance, regression analysis and other statistical applications as time permits. Applications are covered through practical data analysis examples.
STAT 205: Statistical Inference I
Credits: 4.0
Level: UG
Covers descriptive statistics, elementary probability theory, discrete and continuous random variables and probability distributions, joint distribution functions, expected values, statistical measures, sampling distributions, and point and interval estimation.
STAT 206: Statistical Inference II
Credits: 4.0
Level: UG
Topics include hypothesis testing, two sample procedures, analysis of variance models, regression analysis and the use of statistical software.
STAT 325: Six-Sigma Quality Implementation
Credits: 4.0
Level: UG
Focuses on current theory and practice in Six-Sigma implementation for quality monitoring and improvement. Topics include the dynamic nature of quality, Six-Sigma implementation, and the roles of management in planning and guiding quality efforts. The fundamentals of managerial and statistical methods for quality monitoring and improvements are covered.
STAT 331: Introduction to Data Mining for Business
Credits: 4.0
Level: UG
This course introduces students to the fundamental ideas of data mining methods, including dimension reduction, cluster, classification and regression trees, and logistic regression. The emphasis is understanding the application of methods rather than on mathematical and computational foundations. All applications are business-oriented.
STAT 601: Business Statistics
Credits: 3.0
Level: GR
This course covers the basic principles and implementation techniques of descriptive statistics, sampling, statistical inference, analysis of variance, and regression analysis. An understanding of how these tools can support managerial decision making is emphasized.
STAT 610: Statistics for Business Analytics
Credits: 3.0
Level: GR
This course covers the basic principles and implementation techniques of analysis of variance, simple and multiple regression analysis. An understanding of how these tools can support business analytics is emphasized. The course covers not just methods, but theory, too.
STAT 628: Applied Regression Analysis
Credits: 3.0
Level: GR
Covers techniques used in simple and multiple regression analysis, including residual analysis, assumption violations, variable selection techniques, correlated independent variables, qualitative independent and dependent variables, polynomial and non-linear regression, regression with time-series data and forecasting. Applications related to business decision-making will be emphasized.
STAT 630: Multivariate Analysis
Credits: 3.0
Level: GR
An introduction to multivariate statistics that focuses on the use of statistical methods for exploring and discovering information in large business datasets. Topics will be drawn from clustering and discriminate analysis for classification, principle components analysis for data exploration and variable reduction, factor analysis for indentifying latent variables, and other traditional multivariate topics.
STAT 632: Datamining for Managers
Credits: 3.0
Level: GR
Datamining focuses on extracting knowledge from large datasets. This course introduces the student to several key datamining concepts including classification, prediction, data reduction, model comparison and data exploration. Software and datasets are employed to illustrate the concepts.
STAT 634: Quality & Six-Sigma
Credits: 3.0
Level: GR
This course covers the current theory and practice in quality, with a focus on Six-Sigma Implementation. Topics will include the dynamic nature of quality, the roles of management in planning and guiding quality efforts, as well as the fundamentals of statistical methods for quality monitoring and improvement.
STAT 636: Experimental Design
Credits: 3.0
Level: GR
Introduces design of experiments. Covers topics including scientific approach to experimentation, completely randomized designs, randomized complete block designs, Latin square designs, factorial designs, two-factorial designs, fractional factorials, nested and split plot designs, response surfaces designs, and Taguchi methods.
STAT 642: Data Mining for Business Analytics
Credits: 3.0
Level: GR
This course introduces students to the methods of data mining and how to apply them to business problems. Included are logistic regression, trees, neural networks, support vector machines, and marketbasket analysis. Data preparation, visualization, and feature selection also are addressed, as are boosting and random forests.
STAT 924: Multivariate Analysis I
Credits: 3.0
Level: GR
An introduction to multivariate statistics with topics that may include but are not limited to Matrix Algebra, the Multivariate Normal Distribution, Multivariate Analysis of Variance, Tests on Covariance Matrices, Discriminant Analysis, Multivariate Regression, Canonical Correlation, Principle Component Analysis, factor Analysis, and Cluster Analysis.
STAT 925: Multivariate Analysis II
Credits: 3.0
Level: GR
This course is the sequel of STAT 924. STAT 924 discussed linear regression, PCA, EFA, CFA, cluster analysis, ANOVA, discriminant analysis, logit, canonical correlation, and MDS Using SAS. This course builds on that baseline by continuing into GLM models and then exploratory regression models.
STAT 931: Statistics for Economics
Credits: 3.0
Level: GR
This course will cover the traditional introductory statistics topics; descriptive statistics, probability theory, random variables, discrete and continuous probability distribution, sampling distributions, estimation, and hypothesis testing. Then weâll move on to a more advanced topic: regression analysis.
STAT 932: Statistics for Behavioral Science
Credits: 3.0
Level: GR
This course provides a non-theoretical coverage of common statistics topics for students in the behavioral sciences. These may include, but are not limited to descriptive statistics, probability theory, random variables, discrete and continuous probability distributions, sampling distributions, estimation, hypothesis testing, analysis of variance, & regression. Emphasis is put on and examples are of behavioral topics.
BSAN 601: Business Analytics for Managers
Credits: 3.0
Level: GR
Business Analytics is an interactive process of analyzing and exploring enterprise data, in order to understand the past, make predictions about the future, and guide decision-making. In this course, students will learn how to use analytics in their decision-making. Analytics through the methodology of problem framing, model building, analysis, and communicating insights is explored.
BSAN 615: Data Visualization & Analytics
Credits: 3.0
Level: GR
Graphical methods enable data and information to be presented in an easily consumable, insightful, and actionable manner. Descriptive, diagnostic, predictive, and prescriptive analytics all benefit from data visualization, through the use of infographics, interactive visualizations, dashboards, and more recently, augmented analytics. This course will focus on three main facets of data visualization: strategy and implementation, Interpretation and insights, and communication and storytelling.
STAT 645: Time Series Forecasting
Credits: 3.0
Level: GR
This course provides a comprehensive introduction to the latest time series forecasting methods. Topics such as autocorrelation, forecast accuracy, seasonality, stationarity, decomposition, time series linear models, exponential smoothing, and ARIMA models are discussed. The course provides a practical skillset to students interested in more accurately forecasting future energy usage, retail sales, crime, economic indicators, user engagement, or any data which is repeatedly measured over time. Knowledge of a statistical programming language is prerequisite.
BSAN 605: Data Visualization for Managers
Credits: 3.0
Level: GR
For contemporary firms, data analytics are often an essential complement to human expertise and intuition in the decision-making process. To be successful, managers need to understand how data, models, and advanced computing can help meet the challenges of stronger competition, more sophisticated customers, and globalization. In this course students learn how firms can integrate data and tools to take advantage of business opportunities, improve organizational functions, and reduce risks in a data-oriented way. The course places particular emphasis on data visualization tools and methods that can be used to achieve better understanding of patterns, trends, and outliers in data. These often lead to valuable business insights. Managerial and leadership perspectives are highlighted throughout the course.
MIS 368: Enterprise Cloud Systems Management
Credits: 4.0
Level: UG
Cloud computing offers organizations elastic, fine-grained, on-demand, pay-for-use computing resources, such as CPU power, memory, storage, or data transfer. The flexibility of the cloud allows customers to scale system capacities rapidly up or down, which entails lower computing expenses than with fixed, internally maintained resources. Enterprise cloud systems also foster resilience by enabling quicker deployment of new systems to support rapid changes in organizational processes and missions. This course focuses on the business and IT-related aspects of cloud computing and includes direct experience with the cloud services of one or more leading providers.
POM 922: Inventory Models Seminar
Credits: 3.0
Level: GR
Major managerial and economic issues involving inventory management in manufacturing and services firms are examined in detail. The focus is on the development and application of operations research and applied probability based mathematical modeling approaches towards inventory decisions.
POM 925: Supply Chain Management Seminar
Credits: 3.0
Level: GR
Supply chain management encompasses all the physical and information flows that play a role in satisfying customer demand for goods and services. This course emphasizes mathematical approaches towards the development and implementation of solutions in the various strategic, tactical and operational aspects of integrated supply chains.
POM 990: Service Operations Management
Credits: 3.0
Level: GR
This course focuses on services management in general and service operations in particular. It explores the elements that unite services that differentiate service processes from manufacturing processes, and that differentiate various types of services from each other. It covers strategic and tactical issues associated with designing and managing service operations, and it provides tools to help assess, design and improve processes, and establish systems to help ensure an excellent customer experience.
STAT I499: Independent Study in Business Statistics
Credits: 0.0-12.0
Level: UG
Self-directed within the area of study requiring intermittent consultation with a designated instructor.