MS in Financial Management, Artificial Intelligence Applications Concentration

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. ]

Prerequisite: MET AD 504. Emphasizes issues of accounting, finance, and economics that are important in most management contexts. Stresses understanding of financial statements, corporate planning and control, cost and benefit evaluation, cash flow analysis, and capital budgeting.  [ 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. ]

Finance is a highly competitive and dynamic industry that demands quantitative oriented professionals. This course will equip students with the 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 will be 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; volatility modelling, etc.  [ 4 cr. ]

This course presents financial algorithms used in applications of computer science in financial decision analysis, risk management, data mining and market analysis, and other modern business processes. The course covers theoretical background on probabilistic methods used for financial decision making and their application in number of fields such as financial modeling, venture capital decision making, operational risk measurement and investment science. Number of financial applications and algorithms are being presented for portfolio risk analysis, modeling real options, venture capital decision making, etc. The course concludes with algorithms for financial risk assessment and presents the security concepts and challenges of financial information systems.  [ 4 cr. ]

Prerequisite: MET AD571 Generative AI is transforming industries by automating tasks, generating content, and assisting in decision-making. This hands-on course explores how business professionals, analysts, and managers can apply Generative AI to solve real-world business challenges. The course emphasizes practical AI applications, covering topics like prompt engineering, AI-driven workflow automation, AI-powered data analytics, and responsible AI practices. Students will gain experience using Jupyter Notebooks, VS Code, SQL, and GitHub to integrate AI into their workflows.  [ 4 cr. ]

Prerequisites: MET CS 248 and MET CS 342. - Study of the ideas and techniques that enable computers to behave intelligently. Search, constraint propagations, and reasoning. Knowledge representation, natural language, learning, question answering, inference, visual perception, and/or problem solving. Laboratory course.  [ 4 cr. ]

Cryptocurrencies and the underlying distributed ledger technology (blockchain), have exploded into public consciousness over the last few years, with many industry practitioners arguing that the blockchain technology has the potential to disrupt business and financial services in the way the Internet disrupted off-line commerce. This course covers digital currencies, blockchains, and related topics in the FinTech area using the analytical tools provided by economics, investments and corporate finance.  [ 4 cr. ]

Prerequisites: MET AD 717 or consent of instructor.- A comprehensive investments course introducing important aspects of investing, including environmental, social, and governance issues, and their role in corporate risk management, financial markets, and investments, presented from the viewpoint of market participants and corporate leadership. The course incorporates the mechanics of investing sustainably, with long-term planning on a micro and macro level. Topics will include an introduction and understanding of the ESG market, defining the environmental, social, and governance factors important for investment decision-making, and the importance of corporate engagement and stewardship. The course will also cover ESG analysis, valuation, and integration in portfolio management.  [ 4 cr. ]

The goal of the course is to give the student a clear, practical understanding of significant pieces of the energy "puzzle" as a guide to understanding how energy is produced and consumed -- as market forces dictate - both in the United States and abroad. Students considering this course can have various backgrounds/knowledge of energy, but most importantly, an interest in understanding the transitions needed to achieve climate-related goals. The student will be challenged to explore energy transition opportunities and decarbonization's imperative through finance, policy, markets, and regulation.  [ 4 cr. ]

This course expands upon the foundations of finance theory with interdisciplinary approaches from statistical physics and machine learning. The course equips the students with the Python tools to tackle a broad range of problems in quantitative financial analysis and combines the study of relevant financial concepts with computational implementations. Students will learn to use packages like Numpy, Pandas, Statsmodels and Scikit, which are commonly used in research and in the industry. Prerequisites: MET AD 685 or PY 355 or equivalent or consent by the instructor.  [ 4 cr. ]

Prerequisites: AD100 - Python is a modern, high-level programming language. One of the most popular programming languages, its use has steadily increased across a large number of industries. This course introduces students to the Python environment and teaches a solid foundation in the basic syntax and structure. Structured Query Language (SQL) is the most common language globally for interacting with relational databases. Employers have indicated that knowledge of SQL is one of the most important skills for new graduates entering the workforce. Even with advances in database technologies and languages for handling heterogeneous data types, SQL remains the core skill for interacting with data. This course introduces both languages to equip students pursuing an analytics education with the skills necessary to succeed in the analytics and data visualization field. The outcome of this course will be a focused survey of Python and SQL topics designed to equip analytics professionals rather than a deep focus on technical programming topics.  [ 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 data inputs, 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, R, SQL, and Power BI software are used in this course.  [ 4 cr. ]

This course equips students with the skills and knowledge to drive social change through innovative entrepreneurial ventures. It explores the fundamental frameworks and theories of social innovation while teaching students how to identify and tackle social needs effectively. Participants will learn to create sustainable business models that are both economically viable and scalable, ensuring a long-term impact. The curriculum includes strategies for scaling social ventures, measuring their impact, and engaging with diverse stakeholders to promote collaborative change. Students will examine various funding options available to social entrepreneurs, such as impact investing and crowdfunding, and discover how to influence policy and advocate for supportive environments.  [ 4 cr. ]

Prereq: METAD 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 approaches and techniques to support the managerial decision-making process and skills in using state-of-the-art statistical and analytics tools. Students will have an opportunity to gain a basic understanding of how transaction and descriptive data are used to construct customer segmentation schemas, build and calibrate predictive models, and quantify the incremental impact of specific marketing actions. Python, R, SQL, and Power BI software are used in this course.  [ 4 cr. ]

Prerequisites: AD571. - Explore web analytics, text mining, web mining, and practical application domains. The web analytics part of the course studies the metrics of websites, their content, user behavior, and reporting. The Google Analytics tool is used for the collection of website data and doing the analysis. The text mining module covers the analysis of text including content extraction, string matching, clustering, classification, and recommendation systems. The web mining module presents how web crawlers process and index the content of websites, how search works, and how results are ranked. Application areas mining the social web and game metrics will be extensively investigated. R, SQL, and Power BI software are used in this course.  [ 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. ]

Undergraduate Prerequisites: METAD 630, METAD 731 - Prereq: METAD 630, METAD 731. This course involves utilizing analytical methods for financial forecasting, cost of capital calculation, rate of return analysis, use of derivatives instruments, business growth management, discounted cash flow analysis, corporate valuation, mergers & acquisitions, and evaluation of bankruptcy proceedings.  [ 4 cr. ]

Undergraduate Prerequisites: METAD 630, METAD 731 - Prereq: METAD 630, METAD 731. Provides an overview of operation, mechanics, and structure of the derivative markets and covers in-depth quantitative valuation of derivative instruments, such as options, futures, and swaps. The course involves risk analysis including risk arbitrage, and risk management. Emphasizes the theory and practice of derivatives-based trading strategies including hedging opportunities for risk mitigation.  [ 4 cr. ]

Prereq: METAD 630, METAD 731. This course covers the nature and analysis of fixed income securities and an in-depth examination of some of the particular features of some major classes of fixed income instruments, valuation, sensitivity to risks, and management of fixed income portfolios.  [ 4 cr. ]

Provides analysis of the economics of innovation and the means by which firms secure the necessary capital to begin or expand operations. The students learn different procedures for raising venture capital through investment institutions and individuals.  [ 4 cr. ]

Graduate Prerequisites: (METAD 731) - Prereqs: AD 630, AD 731. Applies the concepts of corporate finance and risk mitigation to the problems of multinational financial management. Major topics include foreign exchange risk, and construction of hedging strategies using derivative instruments such as forwards, futures, and swaps to reduce multinational corporate risk. Addresses international financial flows and their impact on foreign exchange rates, capital flows, speculation, analysis of alternative foreign investments, analysis of sources and uses of corporate funds abroad, multinational tax and profit.  [ 4 cr. ]

Prerequisites: MET AD 599 and MET AD 571. - This course focuses on applying deep learning techniques to solve practical problems in business analytics. Students will explore foundational concepts of deep learning, including MLPs (Multi-Layer Perceptrons), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and advanced architectures like Generative Adversarial Networks (GANs), Graph Neural Networks (GNNs), and Transformers. Through lectures, hands-on projects, and real-world datasets, students will develop the skills to design, train, and optimize deep learning models to extract insights and drive decision-making in business contexts.  [ 4 cr. ]

CAPSTONE IN FM  [ 4 cr. ]

This course covers the concepts of the object-oriented approach to software design and development using Python. It includes a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, arrays and strings, and proceeding to advanced topics such as inheritance and polymorphism, creating user interfaces, exceptions and streams. Upon completion of this course students will be able to apply software engineering principles to design and implement Python applications that can be used in with analytics and big data. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking. Prerequisite: Programming experience in any language. Or Instructor's consent.  [ 4 cr. ]

Exploration of principles and practices essential for planning cities that are both sustainable and resilient to climate change. Strategies to create environmentally responsible, economically viable, and socially equitable urban futures will be examined. Key topics include climate adaptation and mitigation, green infrastructure, urban resilience, sustainable transportation, and energy-efficient urban design. Through case studies, policy analysis, and hands-on projects, students will develop the skills to design and implement innovative solutions that address the impacts of climate change while promoting sustainable urban development.  [ 4 cr. ]

Sustainability generally refers to the ability of "systems" to be maintained such that they remain viable over long periods of time. As much as achieving the perfect sustainable equilibrium may be the ideal, it is important to recognize that there will be competing and conflicting interests, especially within complex hierarchical social, economic and ecological systems, particularly in light of ongoing climatic change. This field intensive course draws on the practices and theories of sustainability and climate change to understand what sustainability can mean in different contexts, and, more important, how nuanced, sustainable solutions can be achieved under varying conditions and in different systems. With an emphasis on the urban environment, the course will consist of projects in which students will identify, analyze, and develop practical proposals to real world issues. This course is intended for a wider audience from a range of disciplines.  [ 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. ]