MS in Insurance Management

Corequisite: PM 100 lab - Explore modern project management from an enterprise- level, experiential view of the discipline focused on connecting projects to the organization's mission, vision, and values. The theme of the course is applying key project management tools and techniques through case-based group work, which will help you identify, analyze, and develop practical proposals to real-world issues. Your group will select, plan, report, and then present on your project's scope, schedule, cost, risk, quality, and communications elements using tools such as the WBS, network diagram, PERT estimate, Gantt chart (including the use of MS Project), risk register, and heat map. As you complete an AI workbook, you will gain familiarity with important new concepts in project management: Agile frameworks, actionable sustainability thinking, and Benefits Realization Management, all of which will be important for success not only in other graduate courses, but as you lead projects for organizations so as to provide lasting, triple-bottom-line value. The course is aligned with the latest edition of the PMBOK® Guide from the Project Management Institute.  [ 4 cr. ]

Introduction to the concepts, methods and problems of accounting and financial analysis. Includes accounting principles, measurement and disclosure issues, financial statement analysis, time value of money, cash flow projection and analysis, capital budgeting and project evaluation, bond and equity valuation, cost of capital and capital structure. 4 cr. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Critical Thinking.  [ 4 cr. ]

The purpose of this course is to help improve business problem solving and managerial decision-making through the use of quantitative and qualitative decision-making tools and techniques. This course will provide the student with an overview of how decisions are made to solve management problems in the business environment. It introduces the fundamental concepts and methodologies of the decision-making process, problem-solving, decision analysis, data collection, probability distribution, evaluation, and prediction methods. Students will learn how to apply different quantitative and qualitative analytical tools commonly used in business to provide a depth of understanding and support to various decision-making activities within each subject area of management. Through the use of case studies of decisions made by managers in various production and service industries and a business simulation package specifically prepared for this course, the scope and breadth of decision-making in business will be described.  [ 4 cr. ]

This overview course examines the management issues involved with assessing the security and risk environments in both the private and public sectors in order to assure continuous system-wide operations. The course studies the elements of operational and technological risk assessment and operational continuity using a project management framework and quantitative risk metrics. Students are exposed to the role of the firm in crisis response and management as well as the terms, systems, and interactions necessary to assure continuous operations. Topics include: the role and need for comprehensive assurance strategy and planning; information security; an overview of the system-wide structure; the social and emotional impact on the workforce as well as its effect on productivity; and the organizational infrastructure relating to national, regional, and international compliance.  [ 4 cr. ]

Prerequisite: MET AD 571 - The course offers an overview of the key current and emerging enterprise risk analytical approaches used by corporations and governmental institutions and is focused on understanding and implementing the enterprise risk management framework on how to leverage the opportunities around a firm to increase firm value. The major risk categories of the enterprise risk management such as financial risk, strategic risk, and operational risk will be discussed and risk analytics approaches for each of these risks will be covered. Students will learn how to use interlinked analytics models, business statistics, optimization techniques, simulation, and decision-support tools. An integrated enterprise risk analytics approach will be demonstrated with examples from different functional areas of the enterprise. Python and R software are used in this course.  [ 4 cr. ]

Prerequisite: MET PM 100 Lab or MET AD 515 - In this course, you will be introduced to macro and micro approaches to project cost estimation. Case studies of both pre-project and in-process estimating examine some of the more common perils of human irrationality associated with project estimation to help develop more sensible, achievable project outcomes. You will learn how to manage both project cost and schedule objectives using the Earned Value and Earned Schedule Measurement Systems. You will also study risk management through an examination of both individual and overall project risk and apply your learnings using advanced risk management software in an actual case study. Project quality management, procurement/contract management, and project ethics and professional conduct will be explored using case study scenarios.  [ 4 cr. ]

Corequisite: MET AD 100 Lab - This course presents fundamental knowledge and skills for applying business analytics to managerial decision-making in corporate environments. Topics include descriptive analytics (techniques for categorizing, characterizing, consolidating, and classifying data for conversion into useful information for the purposes of understanding and analyzing business performance), predictive analytics (techniques for detection of hidden patterns in large quantities of data to segment and group data into coherent sets in order to predict behavior and trends), prescriptive analytics (techniques for identification of best alternatives for maximizing or minimizing business objectives). You will learn how to use data effectively to drive rapid, precise, and profitable analytics-based decisions. The framework of using interlinked data inputs, analytics models, and decision-support tools will be applied within a proprietary business analytics shell and demonstrated with examples from different functional areas of the enterprise. R and Power BI software are used.  [ 4 cr. ]

Prerequisite: MET AD 571 - The course offers an overview of the key current and emerging enterprise risk analytical approaches used by corporations and governmental institutions and is focused on understanding and implementing the enterprise risk management framework on how to leverage the opportunities around a firm to increase firm value. The major risk categories of the enterprise risk management such as financial risk, strategic risk, and operational risk will be discussed and risk analytics approaches for each of these risks will be covered. Students will learn how to use interlinked analytics models, business statistics, optimization techniques, simulation, and decision-support tools. An integrated enterprise risk analytics approach will be demonstrated with examples from different functional areas of the enterprise. Python and R software are used in this course.  [ 4 cr. ]

Prerequisite: MET AD 571. Become familiar with the foundations of modern marketing analytics and develop your ability to select, apply, and interpret readily available data on customer purchase behavior, new customer acquisition, current customer retention, and marketing mix optimization. This course explores techniques to support the managerial decision-making process and skills in using state-of-the-art statistical and analytics tools. Students will gain a basic understanding of how transaction and descriptive data are used to construct customer segmentation schemas, build and calibrate predictive machine learning models, and quantify the incremental impact of specific marketing actions. Python and Tableau are used in this course. No prior Python experience is required.  [ 4 cr. ]

Prerequisite: MET AD571 Work with large, complex datasets beyond traditional desktop analytics using Apache Spark (PySpark), DuckDB, Polars, SQL-based data warehousing, and AWS cloud services. You will explore SQL databases through AWS’ Relational Database Service (RDS), learn to build feature engineering pipelines, and scalable machine-learning workflows using Spark MLlib. Key topics include cloud computing parallel processing, management of massive data stores, cloud data architectures, web scraping, API-based data collection, text and web mining, and batch and streaming analytics. You will develop an end-to-end analytics solution from data ingestion and cleaning to modeling, evaluation, and communication, using Python (PySpark), SQL, Git, and GitHub in a cloud-ready AWS environment. By the end of the course, you will be able to design scalable analytics pipelines, manage cloud data environments, and apply distributed machine learning to real-world datasets. The course culminates in a term project implementing a complete big data and cloud analytics workflow.  [ 4 cr. ]

Prerequisites: AD571 Enterprises, organizations, and individuals are creating, collecting, and using a massive amount of structured and unstructured data with the goal of converting the information into knowledge, improving the quality and the efficiency of their decision-making process, and better positioning themselves in the highly competitive marketplace. Data mining is the process of finding, extracting, visualizing, and reporting useful information and insights from both small and large datasets with the help of sophisticated data analysis methods. It is part of business analytics, which refers to the process of leveraging different forms of analytical techniques to achieve desired business outcomes through requiring business relevancy, actionable insight, performance management, and value management. The students in this course will study the fundamental principles and techniques of data mining. They will learn how to apply advanced models and software applications for data mining. Finally, students will learn how to examine the overall business process of an organization or a project with the goal of understanding (i) the business context where hidden internal and external value is to be identified and captured, and (ii) exactly what the selected data mining method does. Python, R, SQL, and Power BI software are used in this course.  [ 4 cr. ]

Introduction to the concepts, methods, and problems of financial and managerial accounting. Includes data accumulation, accounting principles, financial statement analysis, measurement and disclosure issues, cost analysis, budgeting and control, production costs, and standard costs.  [ 4 cr. ]

Emphasizes issues in accounting, finance, and economics that are important for managers in various corporate finance settings. Stresses understanding financial statements, planning and control, cost and benefit evaluation, cash flow analysis, capital budgeting, risk/return tradeoff, efficient markets hypothesis, cost of capital, capital structure, and corporate governance. Connect corporate finance theory with real-world practice.  [ 4 cr. ]

Financial Regulation and Ethics is a course designed to thoroughly review the important topics of financial regulations, policies, and ethics. The course will explore an overview of the financial systems, their history, problems, and issues for the purpose of understanding the enactment of regulations as a method to protect the financial systems and investors. Also, regulators and their authority will be identified, both domestically and internationally. Ethics, an extremely important aspect of finance will be discussed and explored. Ethics is a difficult topic to define and can be impacted by social norms. During the ethics portion of the course, students will study where ethics have failed and caused major issues for the financial marketplace and individual companies.  [ 4 cr. ]

Prerequisite: MET AD 100 Lab. Finance is a highly competitive and dynamic industry that demands quantitative-oriented professionals. This course equips students with empirical techniques which are used in the analysis of financial markets, with a strong focus on financial applications using actual data. The goal of this course is to provide students with a number of econometric techniques which are used in the analysis of financial markets based on asset pricing and corporate finance models. In particular, the emphasis is on classical linear regression models, time series analysis, and limited dependent variable models applied to the following topics: predictability of asset returns; event study analysis; econometric tests of the CAPM and multifactor models; and volatility modeling.  [ 4 cr. ]

Prerequisite: MET PM 100 lab or MET AD 515; Corequisite: PM200 lab- This course focuses on the relationship among portfolios, programs, and projects, and the important strategic objectives of each endeavor. It is designed to help you develop a program management framework, policy, and organizational structure. You will gain skills and techniques for chartering constituent projects, directing and managing program execution, and managing the program team and stakeholders. In small groups, you will take a Program from concept to an implementation-ready plan. The global legal, economic, cultural, and political environments in which projects operate will be contrasted, and mechanisms for resolving conflicts will also be addressed.  [ 4 cr. ]

Prerequisite: AD515 and instructor permission - This course will introduce students to Project Value - a unique perspective on classical Project Management that is focused on assuring that a project delivers value that is aligned with the mission, vision, and values of an organization. This increasingly popular framework with which to view projects and project management is derived from current research, standards, and thought leadership from PMI and other international bodies, which now formally recognize project management as a 'system for value delivery'. Students will learn how to create value in projects even in today's disruptive, turbulent environment. They will learn how to apply the conceptual framework of benefits realization management (BRM) and to use tools to manage a portfolio of programs and projects strategically with an aim of sustainable project value. Value in Agile projects will be discussed. The element of the Triple Bottom Line and a focus on sustainability is part of the subject matter. Case studies and real life experiences, illustrated with guest lectures, will be intertwined with current research in project management..  [ 4 cr. ]

Prerequisite: MET PM 100 Lab - This course is highly valuable for project and program managers, analysts, consultants, educators, and managers in government, nonprofit, and private institutions, offering a comprehensive survey of the methods of use in monitoring, evaluating, and overseeing projects and programs. You will learn to identify and understand enterprise-wide project interdependencies and gain experience with the tools that determine what pace best complements appropriate planning, scheduling, executing, monitoring, and controlling of the projects within a program in the future. You will develop the ability to assess program results and identify ways to improve program performance, as well as learn to assess factors linking projects under one program and provide guidance on the best allotment of resources between them. Ultimately, you will leave this course with the understanding necessary to shape accountable and responsible organizations with well-defined roles that are based on transparency, resource allocation, decision-making, and enterprise project management.  [ 4 cr. ]

Prerequisite: MET PM 100 Lab - In this course, you will gain an understanding of how new Agile principles and practices are changing the landscape of project management and be provided a fresh insight into how to successfully blend Agile and traditional project management principles and practices in the right proportions to fit any business and project situation. You’ll also gain a deep understanding of Agile project management principles and practices in order to see them as complementary rather than competitive to traditional project management. Topics include: Agile fundamentals, principles, and practices; roots of Agile in TQM and Lean Manufacturing; adapting an Agile approach to fit a business environment; planning and managing an enterprise-level Agile transformation; integrating AI into Agile; and scaling Agile to an enterprise level using Agile frameworks and Agile project management tools.  [ 4 cr. ]

This course will provide students with the analytical tools to analyze, manage, and improve manufacturing, service, and business processes. Coverage includes various options to lower operational costs and improve responsiveness to customers' needs, including operating system design, product & service design, capacity analysis & buffering, waiting line optimization, and process quality analysis using statistical approaches. Quantitative methods include application of stochastic simulation, analysis of random outcomes, statistical analysis routines (confidence intervals, hypothesis testing, machine learning), system reliability analysis, and statistical process control. The Deming philosophy of management, Lean operations principles, and Six Sigma process improvement methodologies form the underlying foundation of the course coverage.  [ 4 cr. ]

This course covers the quantitative analysis tools to support operations management for a supply chain that is geographically dispersed and culturally diverse. The tools necessary to assure that the products/services are delivered/provided in the quality and timely manner include demand forecasting, inventory and capacity buffer optimization, delayed differentiation, statistical risk pooling, and stochastic inventory optimization. These tools are applied to decisions such as offshoring, multi-country outsourcing, push-pull, reverse supply chains, and risk mitigation. Particular attention is given to sustainability, information technology and digitalization, and creating resiliency.  [ 4 cr. ]

This course covers quantitative approaches to logistics management. It teaches network optimization techniques and center gravity models for location analysis, mathematical programming for selecting the optimal transportation modality, statistical distributions for modeling the statistical uncertainty around the arrivals of trucks to a warehouse or a store, and inventory modeling for optimizing distribution centers. The course introduces mathematical models for warehouse layout decisions, learning curve models, and logistics network design in the context of today's increasingly digitalized supply networks.  [ 4 cr. ]

Course participants will be exposed to the fundamental principles involved in the analysis and management of quality for enterprises and their supply chain. Quality is defined in the broadest sense, encompassing all performance components that drive customer satisfaction. The course focuses on management principles, statistical modeling and analysis, and their application in a variety of industrial, service, healthcare, and educational environments. Topics include the Deming philosophy of management, Six Sigma and the DMAIC project framework, quality certification systems, statistical data analysis & presentation, statistical modeling using control charts, and statistical analysis of process capability. Students will earn a Six Sigma Green Belt based on satisfactory performance on the final examination.  [ 4 cr. ]

This course provides extensive insight into international trade practices and corporate decision-making criteria attendant to global import/export and other market entry strategies, and management of international logistics operations including global sourcing, global transportation, facility network design, intermediaries, and trade documentation. Topics include operations, government agencies, import/export channel networks, and the evaluation of international opportunities with the help of a business simulation package specifically prepared for this course. It is designed to provide students with the skills and tools necessary for international trade and international logistics management.   [ 4 cr. ]

Prerequisites: Six completed program courses. An extensive research project culminating in a written paper and oral defense. Research is conducted under intensive faculty supervision. Requires department approval and thesis supervisor from full-time faculty.  [ Var cr. ]

Prerequisites: MET AD 800. The second course of an extensive research project culminating in a written paper and oral defense. Research is conducted under intensive faculty supervision. Requires department approval and thesis supervisor from full-time faculty.  [ Var cr. ]