Quantitative Analysis
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| Name | Title | Credits | School |
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| QANT 501 | Business Statistics | 1.5 | School of Management |
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This course introduces students to both descriptive and inferential statistics. Coverage includes applications to business and other disciplines and the use of technology as a decision support tool. |
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| QANT 510 | Production and Operations Management | 1.5 | School of Management |
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Addresses concepts and critical activities required in the manufacturing of goods and the delivery of services. Quantitative applications and the use of relevant computer software are an integral part of this course. |
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| QANT 520 | Management Science | 1.5 | School of Management |
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Quantitative techniques for managerial decision-making are covered. These techniques include linear and integer programming, nonlinear programming, decision analysis, queuing theory and simulation. Problems are modeled and then solved using computer software. Prerequisite Course(s): Prerequisite: QANT 501 or a waiver |
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| QANT 595 | Quantitative Methods I | 3 | School of Management |
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An introductory course in college algebra, probability theory, and statistics. Beginning with a review of exponents, functions, graphs, straight lines, and equation solving, a coverage of maximization/minimization methods will be provided. This course also covers probability distributions, sampling theory, estimation and hypothesis testing. Students with limited math backgrounds will be assigned supplemental work. |
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| QANT 601 | Quantitative Methods II | 3 | School of Management |
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Focus on managerial decision making using deterministic and probabilistic models. Topics to be covered include inventory models, linear programming, game theory, forecasting, waiting lines and simulation. Prerequisite Course(s): Prerequisite: MIST 595 and QANT 595 |
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| QANT 610 | Operations Management | 1.5 | School of Management |
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Contemporary issues in Operations Management and their relevancy are discussed, including: Operations Strategy, Production Planning and Control (PPC), Total Quality Management (TQM) and Green Initiatives. The impact of these areas on the business environment is discussed and the use of technology as a decision support tool is included. |
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| QANT 620 | Multiple Criteria Decision Models | 1.5 | School of Management |
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An introduction to decision sciences and the application of multi-criteria quantitative and behavioral modeling to those problems often requiring complex decisions of policy makers. Course content focuses on applications in the business environment and the use of technology as a decision support tool. Prerequisite Course(s): Prerequisite: QANT 501or a waiver |
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| QANT 630 | Operations & Supply Chain Management | 3 | School of Management |
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This course discusses a wide range of issues from how organizations successfully create and manage its operations and supply chain to how they control operations and supply chain. Using AI technologies, this course discusses key drivers and approaches organizations adopt to improve productivity and achieve competitive position. It also addresses major issues in operations and supply chain including inventory management, logistics management, facility location, total quality, material requirement planning (MRP), project management, and scheduling. The innovations and capabilities of these areas that are related to revenues and financial performance of the organizations are discussed. Prerequisite Course(s): Prerequisite: QANT 510 or a waiver |
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| QANT 705 | Seminar in Operations Research and Systems Analysis | 3 | School of Management |
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The concepts of rational decision making and planning will be discussed with emphasis on profit maximization. Topics will include linear programming, waiting-line theory, inventory and simulation models. Prerequisite Course(s): Prerequisites: QANT 601 or QANT 630 or (QANT 610 and QANT 620) |
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| QANT 710 | Value Chain Risk Management | 3 | School of Management |
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This course introduces the concept of global value chain for product and service oriented organizations. The design and management of resilient global value chain network is discussed to ensure cost-effective flow of goods and services. Theoretical and practical concepts such as risk prioritization, mitigation, and response measures are discussed with as real-world cases. Several risk mitigation methods such as Value at Risk (VaR), Utility theory, Decision Tree, Failure Mode and Effect Analysis (FMEA) are explained to manage value chain risks. The goal is to provide students with a solid understanding of value chain risk management using analytics and simulation tools. Prerequisite Course(s): Prerequisites: BUSI 620 |
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| QANT 720 | Statistics of Healthcare Management | 3 | School of Management |
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Medical and biological applications of statistical theory to the decision making process in the delivery of health care. The integration of biomedical statistical applications to the total management process. Prerequisite Course(s): Prerequisite: QANT 501 or a waiver |
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| QANT 737 | Human Capital Analytics | 3 | School of Management |
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Application of quantitative and qualitative research methodologies to human resources and labor relations problem- solving in organizations. Covers research tools for data analytics, visualization and building predictive models. The skills learned in this course will enable students to make evidence-based decisions using data collection, analysis and presentation. |
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| QANT 750 | Simulation Modeling | 3 | School of Management |
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This course emphasizes the role of simulation in evaluating the performance of complex systems in an organization. Simulation techniques such as Monte Carlo and Discrete Event will be studied in-depth. Students will be required to use state of the art simulation packages to develop and analyze simulation models. Topics to be covered in the course will include simulation of operations and supply chain, marketing, and financial models. A term project in the course will require students to develop their own simulation model for developing performance measures of a specific system and integrate it with AI for performing sensitivity analysis and report generation. Prerequisite Course(s): Prerequisite: QANT 501, QANT 510, or a waiver |
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| QANT 755 | Management Science Applications | 3 | School of Management |
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This course shall highlight the use of mathematical modeling and optimization techniques applicable to decision-making situations in an organization. The topics to be discussed will include: stochastic inventory models, specialized linear programming models, integer programming, dynamic programming, game theory, network models and waiting lines. As part of the term project, students will be required to identify an opportunity, collect data, specify a model, and use decision making tools to find the most effective solution, perform sensitivity analysis, and develop an implementation plan. Prerequisite Course(s): Prerequisite: QANT 630 or QANT 620 |
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| QANT 760 | Operations Management Applications | 3 | School of Management |
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This course applies advanced Operations Management (OM) concepts to real-world challenges, emphasizing the integration of AI tools like Generative AI to enhance decision-making and process optimization. Topics include Total Quality Management, Forecasting, Project Management, and ERP systems. Through case studies and an application-oriented project, students will combine traditional OM strategies with AI-driven approaches to analyze, evaluate, and recommend innovative solutions for competitive advantage. Prerequisite Course(s): Prerequisite: QANT 510 (or QANT 510 waiver) |
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| QANT 780 | Supply Chain Management | 3 | School of Management |
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Supply chain management (SCM) deals with the procurement of raw materials, management of operations, and distribution of final products. The explosive growth of AI in all aspects of today's environment has created an opportunity for any organization to reduce costs while improving quality and effectiveness. This course will focus on analyzing, evaluating, and recommending actions that will improve the supply chain performance for a common consumer product. A term project in which students attempt to go as far back or upstream as possible in the supply chain of the selected product and identify each member along with the value added by each member. Students will need to use AI tools to find or estimate missing elements (data) which will be essential to complete the project Prerequisite Course(s): Prerequisites: QANT 630 or BUSA 630 |
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