This course examines the basic concepts necessary to understand the information provided by financial and managerial accounting systems. The focus is on interpretation of basic information, as students learn about internal and external financial reporting. Topics include: accrual accounting; revenue and expense recognition; accounting for assets, liabilities, and equities; accumulation and assignment of costs to products and services; and budgeting.

This course emphasizes the use of accounting information by managers for decision-making. It is designed to provide managers with the skills necessary to interpret analytical information supplied by the financial and managerial accounting systems. Financial accounting concepts based on profit, liquidity, solvency, and capital structure are used in the process of employing management accounting tools to decisions and evaluate organization performance and changes in cost, profit and investment centers.

This course brings together technical accounting and reporting concepts and theories with a focus on the financial accounting information that is required to be filed with regulatory agencies, the most predominant being the Securities and Exchange Commission. This course aims to provide an in-depth conceptual understanding of regulatory reporting requirements coupled with an appreciation of how these regulations affect the quality of information in publicly available corporate reports. Students will enhance their ability to analyze and understand unique and complex future accounting issues and possible solutions. The course is taught seminar style with students leading the discussions of cases and research.

This course covers concepts that are relevant in practice for both a public and private accounting and taxation setting. Drawing on and integrating complimentary law and tax topics, the course will consider issues such as: real estate used in a trade or business or held for the production of rental income, ownership of a principal residence, and indirect ownership of real-estate interests in the form of securities under federal law, including a REIT, as well as secured transactions and bankruptcy. Crosslisted with TAXN 6515.

The primary focus of this course is the study of International Financial Reporting Standards (IFRS). Particular emphasis will be placed on developing an understanding of significant differences between the current United States Generally Accepted Accounting Principles (GAAP) and IFRS standards. Students will also learn the pros and cons of U.S. GAAP and IFRS approaches for select technical accounting issues. Some other non-IFRS related topics include International Taxation, International Transfer pricing and the impact of culture on the development of accounting standards and practices throughout the world.

This course provides students with a foundation in the Law of Commercial Transactions. The course begins with a review of the principles of common law contracts which underpins many aspects of the Uniform Commercial Code. This course entails an advanced study of several provisions of the Uniform Commercial Code (hereinafter referred to as "UCC" or "the Code"). The sections of the Code to be studied include Article 2 Sales, Article 2A Leases of Goods, Articles 3 and 4 Negotiable Instruments and Bank Deposits and Collections, and Article 9 Secured Transactions. With an emphasis on case analyses and/or problem sets, students taking the course will have the opportunity to improve their critical thinking and written and oral communication skills, particularly as they relate to the legal settings associated with the UCC. Crosslisted with TAXN 6525.

This course examines the generally accepted accounting principles applicable to governmental entities (as issued by GASB) as well as accounting principles applicable to not-for-profit entities (as issued by FASB). The focus will be on the financial statements and reports prepared by state and local governments and financial reporting for the wide array of not-for-profit entities with an emphasis on the contrast of these entities with for-profit accounting.

This course addresses technological topics of current interest to the accounting profession. Topics such as accounting information systems, cybersecurity, enterprise resource planning systems, and business intelligence may be discussed, but the focus of the class will be development of computer skills for extraction, data visualization, and cleaning and analysis of accounting data.

This course covers internal audit from a broad perspective. Course topics cover three main areas: internal audit basics, risks, and metrics. During the course, students will develop critical thinking skills (particularly employing professional skepticism) and learn to effectively communicate their professional opinions.

This course will expose students to the global profession of auditing, with a primary focus on public company auditors. Topics will vary any given semester, but may include the following: the different international organizations that set auditing standards and enforce auditing standards; the impact of culture on auditing standards and practices throughout the world; the impact of International Financial Reporting Standards on international and U.S. auditing rules; the evaluation of audit evidence; auditor independence; materiality; internal controls; computer assisted audit tools and techniques; fraud detection and forensic accounting. The course is taught seminar style, with students leading the discussions of cases and current articles. Assignments are designed to develop students' written and oral communication skills, analytical skills, and critical thinking skills.

This course provides students with a foundation in investigative accounting. Topics covered include identifying, investigating and documenting fraud and providing litigation support for forensic engagements. With an emphasis on case analyses and/or independent research, students taking the course will have the opportunity to improve their critical thinking and written and oral communication skills, particularly as they relate to the legal settings associated with investigative accounting.

This course investigates ethical problems in contemporary accounting practice. The goal is to increase students' ethical perception so they are better able to identify, consider, and ultimately act on the ethical issues they may face in their professional accounting career, regardless of specialty area (e.g., audit, tax, and corporate accounting). The course is taught seminar style, with students leading the discussions of cases and current articles. Assignments are designed to develop students' written and oral communication skills, analytical skills, and critical thinking skills.

This course covers the application of data analytics techniques in assurance and tax. Students will gain hands-on experience applying contemporary data analysis techniques to collect, clean and analyze large sets of data in order to address discipline-specific problems. Students will also learn to use appropriate visualizations and reports to meaningfully present analysis results.

The course is designed to increase and extend the knowledge of the student in financial statement information and topics introduced in undergraduate courses in intermediate and advanced financial accounting through lecture, problem solving and case analysis. A critical examination of both objective and subjective aspects of financial reporting will be undertaken with both quantitative as well as qualitative assessments of financial information emphasized.

In this course, students will practice communicating effectively in accounting settings. Topics include considering the communication needs of accountants' diverse audiences, adapting communications to varying purposes, and writing and speaking clearly and concisely in both preparing accounting-specific documents and in presenting accounting-focused information. Crosslisted with TAXN 6585.

This course presents recent practitioner and academic literature in various areas of accounting, including guest speakers where appropriate. Topics change semester to semester, depending upon faculty and student interests.

This research seminar engages students in utilizing an analytical lens to develop and present recommendations for emerging issues, recent pronouncements of accounting rule-making bodies, and/or unresolved controversies in contemporary financial reporting and practice. Students apply AI-informed methods of data mining, information synthesis, forecasting, and evaluation to discipline-specific questions in accounting. Emphasis is placed on the systematic evaluation of AI-generated analyses and recommendations, including their accuracy, bias, and implications for organizational decision-making and performance. Alongside developing their analytical and critical thinking skills, students strengthen their professional communication skills.

This course builds on the in-class lessons covered during the student's graduate studies by providing the student with the opportunity to apply their academic knowledge to a professional accounting context. As such, it is an experiential learning activity. Successful completion of the practicum will entitle students to three credits that count as a graduate-level accounting elective. Enrollment by permission of the department chair or designee. This course may not be repeated for credit.

This course provides students with an opportunity to develop research skills while exploring a specific contemporary accounting issue with a full-time faculty member specializing in the area of the discipline. Students are expected to complete a significant research paper as the primary requirement of this course. Enrollment by permission from department chair or designee only.

Using spreadsheet software, this hands-on course teaches a variety of quantitative methods for analyzing data to help make decisions. Topics include: data presentation and communication, probability distributions, sampling, hypothesis testing and regression, and time series analysis. This course uses numerous case studies and examples from finance, marketing, operations, accounting, and other areas of business to illustrate the realistic use of statistical methods. Previously BUAN 5400.

This course introduces the fundamentals of programming with a focus on core computational concepts and AI-assisted coding techniques that apply across general-purpose programming languages. Students will begin by exploring program structure, control flow, and data manipulation, while learning to harness AI tools to enhance problem-solving, debugging, and code development. Through concise lessons, quizzes, and interactive coding challenges, the course demonstrates how programming languages such as Python and R, combined with AI-augmented development, are utilized in contemporary Business Analytics environments. The course concludes with a comprehensive project that showcases both programming competency and the effective integration of AI-driven coding support in real-world analytical workflows.

This course focuses on quantitative modeling and analyzing business problems using spreadsheet software, such as Excel and its add-ins. Topics include descriptive analytics, visualizing and exploring data, predictive modeling, regression analysis, time series analysis, portfolio decisions, risk management, and simulation. Business models relevant to finance, accounting, marketing, and operations management are set up and solved, with managerial interpretations and "what if" analyses to provide further insight into real business problems and solutions. Open to MS Management students only. Previously BUAN 5410.

This is an introductory level graduate course focusing on spreadsheet modeling to analyze and solve business problems. Topics include descriptive analytics, data visualization, predictive modeling, time series analysis, and data mining. Contemporary analytical models utilized in finance, marketing, accounting, and management are set up and solved through case studies. Previously ISOM 6500.

This course provides a broad overview to the analytics profession, with a focus on data driven leadership, hands-on skills in analytics and AI, and ethics. Starting with a foundation of analytical framing, the course moves on to more advanced topics like data visualization and summarization, descriptive and inferential statistics, spreadsheet modeling for prediction, linear regression, risk analysis using Monte-Carlo simulation, linear and nonlinear optimization, decision analysis, and data storytelling. The course includes an introduction to prompt engineering and other AI basics, as well as discussions on ethics in the profession. The course culminates with a group research project using curated big data datasets, as well as individual exercises in problem framing intending to be a component of a capstone experience. Previously BUAN 6500.

This course introduces fundamental techniques for collecting, preprocessing, and transforming data for business analytics and AI applications. Students develop hands-on skills in modern data processing and data mining, with an emphasis on feature engineering—both traditional and multimodal. The course explores how to convert raw data into meaningful, analyzable forms that power advanced analytical and machine learning models. Key topics include handling diverse data types, integrating information from multiple sources such as text, images, and videos, and applying methods for data acquisition and cleaning. By the end of the course, students will understand how effective data preparation and feature design serve as the foundation for predictive, descriptive, and AI-driven analytics introduced in more advanced courses.

This course introduces data design and management. Starting from the traditional relational data model and database fundamentals, the course offers a hands-on introduction to Structured Query Language (SQL) for defining, manipulating, accessing, and managing data, accompanied by the basics of data modeling including entity relationship modeling and diagrams. The course also offers an introduction to NoSQL and data warehousing approaches to handling Big Data. The course further touches upon how artificial intelligence is shaping the field, including query generation and database design from natural language questions. The course concludes with a comprehensive data warehousing project that gives each student the opportunity to integrate and apply the new knowledge and skills learned from this class. Previously BUAN 6510.

This course focuses on governance frameworks for AI systems, including transparency, accountability, data privacy, consent, fairness, and social impact. Students will critically analyze AI governance frameworks drawn from sources such as the EU AI framework, NIST, and explainable AI (XAI) literature. Topics include assessment of transparency and risk, and the formulation of policy and governance recommendations. The course includes weekly readings, quizzes, case studies, and hands-on workshops. The final project centers on designing an AI governance framework for a real or realistic AI deployment.

With the rise of analytics for cutting-edge business innovation, the industry needs business leaders who can solve an organization’s most important problems by asking and answering questions using data. These business consultants need to bridge both the data analytics and business fields. This class tries to provide a “real world” consulting experience through a project-centric experiential approach, in addition to case studies of analytics consulting and business problem solving using descriptive, predictive and prescriptive analytics. When possible, class projects will be client-driven using community partners. Students work in teams using analytics to answer the client’s current and important business questions using data. The students will approach these as business analytics consultants by using effective project management to gathering requirements, using continuous client engagement to deepen understanding of the problem, suggesting ways in which to explore the question and its possible solutions through data, running different data models to approach the solution, working with clients to come up with effective analytics strategies, making business presentations based on findings, incorporating the inevitable changes that come with real world projects, and recommending strategic solutions based on their findings.

This course introduces analytical techniques used for decision-making under uncertainty. Topics include time series and other forecasting techniques, such as Monte Carlo simulation, to assess the risk associated with managerial decisions. Specifically, we will cover data collection methods, time dependent models and analysis, advanced solver, time series techniques, exponential smoothing, moving averages, and Box-Jenkins (ARIMA) models. Application examples include financial models - stock prices, risk management - bond ratings, behavior models - customer attrition, customer likes/dislikes, buying patterns - propensity to buy, politics - identify swing voters, and sales. Previously BUAN 6530.

Sports analytics is transforming the way teams, leagues, players, coaches, referees, and fans perceive and appreciate their favorite pastimes and games, including major team sports such as baseball, basketball, football, soccer, cricket, and rugby, more individualized sports like tennis and golf, and brand-new innovations such as e-sports. In this course, students will gain experience in framing analytical questions in sports, discover and evaluate cutting-edge research and findings in sports analytics, develop hands-on skills in using and implementing sports analytics solutions, and learn how to communicate findings to a non-analytical audience in an impactful and actionable way. This course culminates in a scholarly sports analytics research paper.

Modernly, business intelligence has become far more interactive. This course provides an advanced application and overview of the new techniques for building interactive dashboards and tools now prevalent in this profession. Additionally, with data overload happening on every level, the importance of good data storytelling has soared. Using programming languages and environments such as Tableau and R, this course introduces students to the business intelligence profession and teaches the skills necessary to develop and deploy cloud-based interactive apps to assist in data and analytical storytelling, including insights into user interface design (UI) and user experience design (UX). The course concludes with a comprehensive project. Previously BUAN 6540.

This course provides an advanced understanding of the principles and practices of data science and AI engineering, with a special focus on business applications. It covers the design, development, deployment, and maintenance of AI-powered systems across their full lifecycle. Emphasizing practical relevance, the course applies AI and data-driven tools to business analytics. Programming tutorials and case studies introduce topics such as AI lifecycle management, foundation models, and AutoML. Students are expected to actively participate in the course deliverables through independent assignments, lab work, and group projects. The course culminates with a final project in predictive analytics, as well as individual exercises in modeling and interpretation intending to be a component of an analytics capstone experience.

This course introduces the fundamentals of Big Data management and its implementation in the public cloud. Topics include classic theories of data architecture, dimensional database design, data pipelines, and data governance, supplemented with the latest developments in the emerging field of DataOps. The theory is grounded with hands-on experience building databases and data pipelines with the Modern Data Stack.

Sports analytics is transforming the way teams, leagues, players, coaches, referees, and fans perceive and appreciate their favorite pastimes and games, including major team sports such as baseball, basketball, football, soccer, cricket, and rugby, more individualized sports like tennis and golf, and brand-new innovations such as e-sports. In this course, students will gain experience in framing analytical questions in sports, discover and evaluate cutting-edge research and findings in sports analytics, develop hands-on skills in using and implementing sports analytics solutions, and learn how to communicate findings to a non-analytical audience in an impactful and actionable way. This course culminates in a scholarly sports analytics research paper.

Artificial intelligence is becoming far more prevalent in the business and analytics worlds, yet many analytics professionals are excluded from participating in this new wave because they lack the strong coding foundations that are typically needed to implement this new technology from scratch. However, recent advances in AI/ML have coincided with desktop and cloud tools that can be deployed far more easily to generate new models without complicated coding requirements. This course will teach students how to discover, use, and daisy-chain such tools to solve real-world business problems in ways that would otherwise be impossible.

This course introduces students to autonomous AI applications including agents. Fundamental knowledge, such as the architectures of the deep neural networks, extraction of high-level features representing unstructured data, backpropagation, and stochastic gradient descent, are also reviewed. Additionally, students get hands-on experience building autonomous AI models. Topics covered in this class can include model building and optimization, image classification, natural language processing, generative models, and so forth. These topics cover the foundations and the latest developments in the field of autonomous AI.

This course draws from current literature and practice on information systems and/or operations management. The topics change from semester to semester, depending on student and faculty interest and may include: project management, e-business, management of science with spreadsheets, e-procurement, executive information systems, and other socioeconomic factors in the use of information technology. Previously ISOM 6900.

This course provides an opportunity for students to complete a project or perform research under the direction of an Information Systems and Operations Management (ISOM) faculty member who has expertise in the topic being investigated. Students are expected to complete a significant project or research paper as the primary requirement of this course. Enrollment by permission of the ISOM Department Chair only. Previously ISOM 6990.

This capstone course for the MS Business Analytics program is to be taken in the last term before graduation. The purpose is to apply and integrate knowledge and skills learned in the program (statistics, modeling, data management, data mining, etc.) to a live data analytics project. The course is project-based, with students collaborating on their work under the guidance of faculty members. Application areas and format of the projects may vary, depending on faculty, dataset, and budget availability. However, the work should be rich enough to demonstrate mastery of business modeling and technology, with each student making a unique, demonstrable contribution to completion of the work. Previously BUAN 6999.

This course provides students the analytical tools necessary to test theories of microeconomic behavior through the lens of mathematical and statistical methods. These econometric methods have practical applications for any field where there is data recording events. The course further considers useful techniques and limitations of econometric analysis as well as practical applications of methods useful for bypassing data problems in measuring quantitative economic relationships. Undergraduate equivalent: ECON 4380.

This course examines the fundamentals of economic analysis from both the micro and macro perspectives; from individual consumer behavior to the choices firms make, as well as framing the aggregate economy and indicators that measure global economic activity. The basics of supply and demand, market structures, international trade, fiscal, and monetary policy are covered. A case study approach is utilized to facilitate discussion of real world examples of economic decision making in action.

This course is designed to provide an introduction to the underlying concepts and theories of microeconomics. Students will learn ideas and methodologies that help them to understand how markets behave, and how they are impacted by local, national, and international events. They will explore the behavior of consumers and producers in markets for goods and services, as well as global economic structures. They will assess the outcomes of market activity, and explore alternative methods of allocating resources when markets are determined to have failed.

Statistics is defined as a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data. This course will teach students the fundamentals of statistical analysis with an eye towards economic and business applications. Students will also learn about the limitations of statistics, and how to properly apply principles and present analytical results.

This course will put mathematical, finance, and statistical tools to work to solve firm-level, applied microeconomic problems. The objective of the course is to build a set of strategies that help guide managerial decision-making through case studies and problem-solving exercises. The central point is that good business decisions depend on vast amounts of information that is provided to decision-makers by skilled professionals from a wide variety of disciplines. The tools developed in this course will help students to do this work in a sophisticated way.

This course endows current and future business leaders with the knowledge needed to understand the macroeconomic environment they inhabit. It first illustrates the indicators used to analyze trends in economic activity, inflation, and labor market dynamics. The topics discussed include the business cycle, monetary and fiscal policy, the future of long-run economic growth, and income differences across countries. Case studies focus on how to respond, as a business, in a changing macroeconomic environment.

This course is a practical introduction to Cost-Benefit Analysis (CBA), as a quantitative monetary assessment method that can aid in effective decision-making. The course introduces students to the principles and techniques of preparing a CBA, including microeconomic foundations, valuation methods, discounting, the impact of risk and uncertainty, and distributional consequences. The course includes applications of CBA to a wide range of business decisions and public policy issues (e.g. public, development, health, and environmental projects).

The objective of this course is to provide a graduate level analysis of modern international trade theory, trade policy, international finance and international economics. The course material is complemented with cases from the Harvard Business Review to link theory with real-world applications. Course topics cover concepts related to the benefits and consequences of international trade and globalization, the global economic and financial system, international capital flows, exchange rate determination, and foreign exchange markets.

This course will examine the proper role government has to play in today's economy and will provide the fundamental and technical skills necessary to understand public budgeting and finances. Topics include the reasons for government involvement in the economy (market failure and redistribution), budgeting techniques at all levels of government, and sources of tax revenue. There will be a strong emphasis on issues related to state/local governments. By the end of this course students should have a strong understanding of the budgetary process at all levels, but in particular at the state/local level of government. Crosslisted with PUAD 5410.

This course will examine both the role that nonprofits play in the U.S. economy and how charitable organizations are managed and financed, including the interplay between the government and the Third Sector. Particular attention will be paid to distinctions between successful (impactful) nonprofits and those that are less effective in pursuing societal needs. New forms of philanthropic organizations will be examined, including benefit corporations and social marketing enterprises. In addition, new ethical practices, including impact investing, will be surveyed. Case studies will be utilized to illuminate the economic circumstances that can impact Individual nonprofits. Crosslisted with PUAD 5430.

This course covers the management and financial aspects of healthcare provision in the United States. The transitions that are underway in the market that have influenced the manner in which healthcare is provided will be examined. The course will also discuss the rising cost of treatment and changes in the how services are provided. Students will examine the structure of management within various healthcare organizations, hospitals, networks, and small providers, and how that influences quality of care. The final part of the course will examine the financial nature of various organizational forms within the medical industry. Crosslisted with PUAD 5455.

This course examines financial markets in the context of their function in the economic system. The material deals with the complexity of the financial markets and the variety of financial institutions that have developed, stressing the dynamic nature of the financial world, which is continually evolving. Crosslisted with FNCE 6560.

This course begins by applying microeconomic theory to the health sector of the U.S. economy with a focus on financial incentives throughout the healthcare system. Topics include the demand for healthcare and health insurance, quality improvement, managed care and the role of government. The U.S. experience is compared to healthcare systems in other countries. Evidence-based skills include cost analysis and business plan and budget development. Crosslisted with NURS 7602.

This course provides students the tools necessary to apply statistical methods to data in order to test economic theories, to inform government and private policy makers, and to instruct businesses on consumer behavior, pricing considerations, and cost considerations. In this course we will learn how to measure relationships between economic variables using rigorous econometric techniques. This includes understanding the purpose and methods of ordinary least squares (OLS) regression for cross-sectional and panel data for continuous data, as well as learning models for discrete choice data. We will learn the basic theory and practical means of performing these regressions as well as the methods of statistical inference for hypothesis testing, and students will apply this knowledge to datasets throughout the semester.

This course provides students with the analytical tools necessary to analyze time series data in order to examine past trends and to forecast future ones. Students will learn practical time series forecasting techniques with particular emphasis on the ARIMA method and conditional volatility (ARCH) models and they will perform a variety of data analyses on the computer using R/Rstudio. These methods are extensively employed in economic, financial and business forecasts and can be used for various purposes such as stock market analysis, economic forecasting, risk assessment, budgetary analysis, Census analysis, yield projection, and sales forecasting. To gain a deeper understanding of how the methods work, their theoretical underpinnings will be discussed to some extent, however, the course is mainly concerned with the applications of these techniques in forecasting economic time series.

This course examines the fundamental principles of modern finance that are helpful in understanding corporate finance, investments, and financial markets. More specifically, the course examines the time value of money; the functioning of capital markets; valuation of stocks, bonds, and corporate investments; risk measurement; and risk management. Students learn to use sources of financial data and spreadsheets to solve financial problems.

This course examines business decision-making with the aim of creating and managing value for stakeholders. Accordingly, students learn how to lead and manage a business in a competitive environment. This involves the formulation of corporate objectives and strategies, operational planning, and integration of various business functions leading to greater stakeholder value. Topics include investment and strategic financial decision-making. A business simulation facilitates the learning process.

This course provides an exploration of theoretical and empirical literature on corporate financial policies and strategies. More specifically, the course deals with corporate investment decisions, capital budgeting under uncertainty, capital structure and the cost of capital, dividends and stock repurchases, mergers and acquisitions, equity carve-outs, spin-offs, and risk management.

This course examines the determinants of valuation for bonds, stocks, options, and futures, stressing the function of efficient capital markets in developing the risk-return trade-offs essential to the valuation process.

Students examine how individuals and firms allocate and finance their resources between risky and risk-free assets to maximize utility. Students use an overall model that provides the sense that the portfolio process is dynamic as well as adaptive. Topics include portfolio planning, investment analysis, and portfolio selection, evaluation, and revision.

The globalization of international financial markets presents international investors and multinational corporations with new challenges regarding opportunities and risks. This course examines the international financial environment of investments and corporate finance, evaluating the alternatives available to market participants in terms of risk and benefits. Topics include exchange rate determination, exchange rate exposure, basic financial equilibrium relationships, risk management including the use of currency options and futures, international capital budgeting and cost of capital, and short-term and international trade financing.

This course examines financial markets in the context of their function in the economic system. The material deals with the complexity of the financial markets and the variety of financial institutions that have developed, stressing the dynamic nature of the financial world, which is continually evolving.

This course offers in-depth coverage of financial derivative securities, such as options futures and swaps. The course focuses on the principles that govern the pricing of these securities as well as their uses in hedging, speculation, and arbitrage activities.

This course deals extensively with the analysis and management of fixed income securities, which constitute almost two-thirds of the market value of all outstanding securities. The course provides an analysis of treasury and agency securities, corporate bonds, international bonds, mortgage-backed securities, and related derivatives. More specifically, this course provides an in-depth analysis of fixed income investment characteristics, modern valuation, and portfolio strategies.

This course examines the decision methods employed in long-term asset investment and capital budgeting policy. The course includes a study of quantitative methods used in the capital budgeting process: simulation, mixed integer programming, and goal programming. Students use these techniques and supporting computer software to address questions raised in case studies.

This course focuses on the evaluation and management of corporate and portfolio risk. More specifically, this course examines the methods of evaluating and managing risk with the objective of contributing to value maximization. Risk assessment methodologies such as value-at-risk (VaR) and cash-flow-at-risk (CaR) are analyzed and used extensively.

This course, open to MS in Finance students only, deals extensively with applied research methods in finance, a highly empirical discipline with practical relevance in the models and theories used. The central role of risk distinguishes research methodology in finance from the methodology used in other social sciences, necessitating the creation of new methods of investigation that are adopted by the finance industry at an astonishingly fast rate. For example, methods of assessing stationarity and long-run equilibrium, as well as methods measuring uncertainty, found a home in the finance area. This course covers traditional and new research methods that are directly, and in most instances, solely applicable to finance problems.

This course presents recent practitioner and academic literature in various areas of finance, including guest speakers where appropriate. Topics vary each semester to fit the interests of the seminar participants.

This course, open to MS in Finance students only, provides participants with the opportunity to explore a financial topic of interest in depth, immersing students in detailed investigations requiring substantial research and analysis.

The sudden rise in the value of Bitcoin and other digital assets focused the world's attention on cryptocurrencies as a means of payment. Blockchain technology powers Bitcoin and has been hyped as the next new, transformative technology. This class will first discuss the technical underpinnings of blockchain and review key concepts such as decentralization and consensus algorithms. The class will then discuss practical applications of blockchain technology. It will then then examine blockchain as an asset and review the dynamics of the cryptocurrency markets. It will conclude with the discussion of the future of blockchain.

Decentralized finance (DeFi) allows parties to trade in a peer-to-peer, decentralized manner by replacing financial institutions and other intermediaries with blockchain-based smart contracts and by replacing traditional, physical currencies (e.g. U.S. dollars) with cryptocurrency (e.g. stable-coins pegged to a physical currency). This course will examine how FinTech companies are disrupting the traditional financial services industry and assess the pros and cons of these new technologies. Students in this course will also survey relevant aspects of banking and securities law, with a focus on current regulatory issues pertaining to DeFi and considerations of the future regulatory landscape.

This class introduces the necessary background knowledge and processes to design and implement algorithmic trading models including an introduction to financial markets, mechanics, participants, order types and execution, microstructure, and more. The course walks students through the process of generating trading strategies, quantifying the trading process, risk-based modeling concepts, back-testing and optimization techniques, technology and infrastructure, regulatory compliance, and key metrics of algorithmic trading model performance evaluation.

While FinTech provides the world of finance with exciting new opportunities and innovations, they come with a new set of ethical considerations and potential new regulations. Ethical issues include potential breach of privacy of the data obtained through social media and other means. Artificial intelligence and machine learning and the use of large datasets of proprietary data could unintentionally lead to discrimination and adverse effects on diversity and inclusion efforts. Since much of the FinTech applications are linked to the internet, avoiding cyberattacks poses a large risk to successful implementation of any models. Successful leaders in the field of FinTech must understand ethical considerations associated with FinTech. It is also crucial for the manager to understand current regulation of FinTech and anticipate possible new regulation. This course will consider these and other ethical and legal considerations associated with FinTech.

This course examines micro-level organizational behavior theories as applied to organizational settings. Topics include motivation, leadership, job design, interpersonal relations, group dynamics, communication processes, organizational politics, career development, and strategies for change at the individual and group levels. The course uses an experiential format to provide students with a simulated practical understanding of these processes in their respective organizations.

This course examines micro level organizational behavior theories as applied to organizational settings. Topics will include motivation, leadership, interpersonal relations, group dynamics, and strategies for organizational culture and change.

Effective leadership provides a competitive advantage for an organization in the marketplace. The goal of this course is to enhance students' ability to successfully lead in an innovative, dynamic, global environment, building their confidence level to successfully lead in the 21st century. Building from a best practice "real-world" approach students will be given the opportunity to increase their knowledge and skill level through self-assessments, case studies, assignments, and experiential learning. An impactful set of strategies and techniques will be presented, covering situational leadership theories and practices, leading in multiple geographies and cultures, navigating team/organizational dynamics, influencing and motivating meaningful change, shaping culture, and creating vision and strategic direction.

This course is designed to provide a solid basis in legal and ethical reasoning that can support effective decision-making about a wide range of complex business issues. Employing active learning methods, it efficiently provides students with the capacity to think independently in an informed, carefully reasoned way. Course content includes select legal topics, rules and concepts, models of legal reasoning and ethical analysis, and the relationship between the two.

This course helps students be more responsible and effective managers of the gray areas of business conduct that call for normative judgment and action. The course is designed to develop skills in logical reasoning, argument, and the incorporation of legal, social, and ethical considerations into decision-making. The course teaches the importance of legal and ethical business issues and enables students to make a difference in their organizations by engaging in reasoned consideration of the normative aspects of the firm. Using the case method, the course provides an overview of current topics, including the legal process, corporate governance, employee rights and responsibilities, intellectual property and technology, and the social responsibility of business to its various stakeholders.

This course focuses on effectively managing people in organizations by emphasizing the critical links between strategy, leadership, organizational change, and human resource management. Topics include the strategic importance of people, leading organizational change, corporate social responsibility, implementing successful mergers and acquisitions, and fundamentals of human resource practices. Discussions interweave management theory with real-world practice. Class sessions are a combination of case discussions, experiential exercises, and lectures.

Human Resource (HR) Strategy serves as the critical link between human resource management (HRM) and organizational success. Human capital, which is knowledge, skills and abilities (KSA) of people, is the strategic asset of the firm that acts on all other assets to make them productive. In today's fast-evolving business landscape, leveraging both Artificial Intelligence (AI) and Human Intelligence (HI) is essential for creating dynamic, HR strategies that enhance a firm’s competitive advantage. This course explores how HRM functions—including talent acquisition, training and development, performance management, compensation strategies, and employee relations—can be optimized using AI while maintaining the essential human judgment and ethical considerations necessary for effective people management.

This course examines the intersection of sustainability, law, and finance to explore how policy, regulation, and private capital interact to shape the clean energy economy. With a focus on both U.S. and global frameworks, students will analyze the role of environmental law, administrative enforcement, and government incentives in supporting innovation and shaping market behavior. Students will explore how legal risk, ESG mandates, and regulatory complexity impact business strategy and investment decisions. Through readings, simulations, and real-world case studies - including venture financing of clean tech firms and ethical dilemmas in global supply chains - students will build practical fluency in how sustainability issues influence corporate governance, entrepreneurial ventures, and international dealmaking. Topics include environmental and administrative law, the Inflation Reduction Act, public-private partnerships, venture capital, ESG and CSR distinctions, international clean energy negotiations, and state-level green investment policies. Weekly instruction will incorporate current events and culminate in group-based legal and investment simulations.

This course uses the theories of negotiation and alternative dispute resolution, along with extensive experiential exercises, to build individual negotiation skills and to help students manage disputes from a business perspective. The course emphasizes ways of managing both internal and external disputes.

This course begins by presenting cutting-edge concepts and applications so that students understand the dynamics of innovation, the construction of a well-crafted innovation strategy, and the development of well-designed processes for implementing the innovation strategy. It then focuses on the building of an entrepreneurial organization as a critical core competency in the innovation process. Concurrent with this, it focuses on the development and support of the internal entrepreneur or "intrapreneur" as part of the process of developing organizational core competencies that build competitive comparative advantages that, in turn, allow the firm to strategically and tactically compete in the global marketplace. Topics explored include technology brokering, lead users, disruptive technologies and the use of chaos and complexity theory in the strategic planning process.

The purpose of this course is to aid the process of professional career development at the graduate level. Students will develop professional resumes, practice interviewing skills, and develop a robust LinkedIn social media presence for networking in the job market.

This course builds on the foundational evaluations and reward concepts covered in "Managing People for Competitive Advantage." Students explore in some depth the employee performance management, compensation, and reward systems in organizations. Topics may include 360 degree feedback programs, ESOPs, profit sharing, gain sharing, and the strategic use of employee benefits. The course focuses on how employee performance management, compensation, and reward systems can lead to a competitive advantage for firms.

This course covers entrepreneurship and small business management. The course focuses on the development of entrepreneurial start-up ventures from the point of view of the founding entrepreneur. The course explores characteristics and skills of successful entrepreneurs, the stages of growth of entrepreneurial businesses, the crises in start-up ventures, and issues confronting family and small business management. Students may create their own start-up business plan in conjunction with faculty as the primary course requirement.

This course is about understanding how entrepreneurial skills can be used to craft innovative responses to pressing social needs. These skills are opportunity recognition, assembling resources, launching a venture, scaling it and finally ensuring its sustainability. There will be an emphasis, throughout the course, on how exemplar for-profit enterprises have been able to successfully contribute to widespread economic well-being and social development while enjoying significant profitability. Students will appreciate that the pursuit of profit and poverty alleviation need not be mutually exclusive domains and the institutional requirements that are needed to ensure this outcome.

This graduate level course provides an overview of topics related to environmental and social sustainability with a focus on how strategy and innovation at the business level help to create sustainable societies and infrastructures. This course has three major objectives: to increase students’ knowledge of key global sustainability related trends; to teach students to think strategically on environmental and social challenges; to help students understand the promises and limits of modern business approaches to sustainability. The course combines talks, case studies, and class discussions on management theory, tools, and frameworks shaping the business-environment interface, and evolving requirements for business success. Several guest lecturers will discuss how they have addressed sustainability within their company.

This course develops a framework for distinguishing the various stages of cooperative relationships across national cultures, which have distinct characteristics and call for different modes of behavior. The stages of this framework include: identifying a cross-cultural win-win strategy; translating the strategy into viable action plans; executing the strategy and making cross-cultural collaboration happen; and assuring that emerging synergistic organizations become self-initiating entities. The course identifies and discusses in detail the necessary managerial skills for the support of each of these stages.

This course examines law and public policy issues relating to employee rights and obligations, including employment discrimination, OSHA, pension and benefit issues, minimum wage, and workers' compensation. The course provides a basic overview of the law and its relevance to human resource strategy and operations.

The dual aim of this course is to acquaint students with the dynamics of the labor-management relationship and to make them better negotiators and managers of workplace conflict. Toward these ends, this course examines the processes of bargaining and dispute resolution, primarily in the context of the unionized environment. Case studies, law cases, and experiential exercises are used to explore issues such as negotiations strategy, mediation, and arbitration. Successful models of cooperative relations between management and labor are also covered.

This course provides students an opportunity to explore career planning and development issues from two perspectives, as a job-seeking candidate and as an employer engaged in the hiring and development process of employees. The course will provide theoretical background on a number of career development topics, including: career development over the life span, career transitions, work-family balance, and post-retirement issues. Cases on individuals negotiating career issues such as new roles associated with promotion, managing technical or entrepreneurial careers, aspects of derailment and family issues will be presented. The second part of the course will be devoted to experiential activities that are designed to enhance one's career planning skills. Students take a self-assessment survey and participate in workshops on resume creation, mock interviewing, and social media applications associated with the job search.

This course considers the formulation of effective policy and accompanying strategy actions, and the management of such policies and actions. It examines the role of the general manager in this process and presents the diversified issues and problems the management of a business firm may be required to consider and solve in strategic planning. This course also examines the problems and tasks of strategy implementation and the general manager's function of achieving expected objectives and establishing new ones to assure the continuity of the business organization. Students are required to prepare a business plan as part of this course.

This course examines recent practitioner and academic literature in various areas of management. Topics vary each semester. Guest speakers may be invited as appropriate.

This course examines analytical and managerial techniques that apply to marketing functions with an emphasis on the development of a conceptual framework necessary to plan, organize, direct, and control the product and strategies needed for promotion, distribution, and pricing of a firm's products. The course also considers the relationship of marketing to other units within a firm.

This course will provide students with the understanding of the role of marketing in the modern corporation. The course integrates the fundamental concepts of marketing with the newest trends in digital and social media marketing. Emphasis will be placed on how firms execute marketing strategy utilizing the latest digital tools. Open to MS Management students only.

This course examines the concept of customer value with the goal of understanding how it can be used to achieve customer satisfaction, which in turn generates loyalty and, ultimately, a long-term customer-firm relationship. Topics include the nature of the costs and benefits associated with the notion of customer value, measuring and analyzing customer value and satisfaction, and understanding the associated concepts of customer loyalty and customer relationship management. The class consists of a mix of lectures, case analyses, in-depth qualitative research projects, and opportunities for practical applications, such as a marketing simulation.

Storytelling is an essential part of marketing communication. This course will allow students to understand the power of "the story" in a marketing context in how brands can effectively communicate, persuade, and influence key audiences. Students will learn to leverage brand stories in building long-term relationships with customers. Students will acquire the basics of storytelling drawing from its inter-disciplinary nature including the models and science behind it. They will also learn about the critical elements and techniques used in storytelling. Students will work on in-class assignments using established brands to identify good story ideas along with ways to capture target audiences’ attention. They will not only learn to understand and critique brand stories but also be able to craft and execute holistic brand-customer stories through various promotional mix platforms.

Storytelling with data is an essential part of marketing communication. This course will allow students to use data for effective brand-customer storytelling within an overarching brand strategy framework. Students will learn to apply the critical elements, techniques, including story angles and hooks in effectively communicating a brand-customer story backed with data. Students will work on an in-class project that entails research design, data collection and analysis to not only create a powerful data-oriented, brand-customer story but also to implement this story on at least one promotional mix platform.

This course offers an interdisciplinary approach to understanding the behavior of consumers in the marketplace, covering concepts from the fields of economics, psychology, social psychology, sociology, and psychoanalysis. Topics include motivation, perception, attitudes, consumer search, and post-transactional behavior.

Artificial Intelligence (AI) in Marketing provides a comprehensive overview of the theory and practical application of AI within the field of marketing. This course equips students with theoretical and practical expertise in core AI concepts like machine learning, LLM, NLP, and generative AI, and their direct application across vital marketing functions, including advanced audience targeting, data-driven personalization, modern SEO/AEO/GEO, media buying, content creation, social listening and predictive analytics. The curriculum emphasizes leveraging AI for strategic decision-making while adhering to ethical use and privacy regulations. Students will be prepared to drive socially responsible, strategic decision-making and gain a competitive advantage.

This course provides an overview of the risks associated with marketing decisions and emphasizes developing skills for conducting basic market research. Topics include problem formulation, research design, data collection instruments, sampling and field operations, validity, data analysis, and presentation of results.

Customer experience (CX) occurs every time a customer interacts with a company via any channel, at any time, for any purpose. Creating word-class, end-to-end customer experiences has become an innovative way to gain sustainable competitive advantage, but it requires a deep understanding of both the customers and how they interact, as well as a commitment to change business processes based on that understanding. In this course, students will learn how to create compelling customer experiences along the whole process of customer touch-points, from prospect to purchase. Topics to be covered include pre-purchase profiling and segmentation of prospects, persona development, experience mapping, and the role of technology platforms and artificial intelligence that drive advanced customer journeys. The class consists of a mix of lectures, case analyses, and a project that includes designing, planning, and executing a complete Customer Experience for a selected brand/organization.

Marketing analytics is the practice of leveraging data to drive marketing decision-making and strategy. This course introduces students to essential analytical techniques used to collect, analyze, visualize, and interpret marketing data including the ethical challenges with AI, machine learning and big data. Through a combination of theoretical concepts, hands-on exercises, and simulations, students will develop skills in data-driven marketing, optimization, and predictive modeling. By the end of the course, students will have gained hands-on experience with marketing data technologies and developed the ability to make data-driven marketing decisions.

This course focuses on the theory and conceptual tools used to develop and implement product and service branding strategies, as means for insuring brand awareness, acceptance, and success, or "equity," in the marketplace. The course highlights the importance and impact of the brand in the marketplace, identifies various decisions involved in creating successful brands, provides an overview of different means for measuring brand effectiveness, and explores the existence of customer-brand relationships. The course incorporates three general modules: Module 1: Identifying/Developing Brand Equity; Module 2: Measuring Brand Equity; Module 3: Managing Brand Equity.

This course provides a comprehensive overview of advertising and promotional processes, and develops strategies facilitating managerial decisions in the areas of advertising, public relations, sales promotion, and direct marketing. This course analyzes the importance and influence of advertising in the changing marketplace; provides students with an integrated approach for analyzing marketing communication opportunities; develops the capability for designing, implementing, and evaluating advertising campaigns; and promotes an understanding of the different methods of measuring advertising effectiveness.

The course will focus on the importance of an effective marketing strategy in order for businesses to make key marketing decisions and be successful. In this course, students will learn to strategically analyze and solve marketing problems from a decision maker’s perspective. Students will focus on solving four fundamental marketing problems known as the “First Principles of marketing strategy” that firms face when designing and implementing marketing strategies. In order to develop an effective marketing strategy, most critical decisions must address that 1) All customers differ; 2) All customers change; 3) All competitors react and 4) All resources are limited. Application of the principles will cover all elements of the marketing mix including topics such as research and development, demographic targeting and segmentation, pricing, product forecasting, marketing channels, sales force distribution and media allocation.

In this course, students will learn how retailers and manufacturers engage in a collaborative process to manage a product category at retail for the purpose of optimizing shopper satisfaction to increase revenues and profits. The course imparts students with hands on training on how to analyze retail POS (Point of Sales) data (syndicated scanner data). An emphasis will be given to data interpretation and implication on strategic and tactical decision making related to product assortment, shelf set, promotion, and pricing decisions for the purpose of generating store traffic, improving shopper loyalty, and ultimately increasing revenues and profitability. Students will learn how to leverage insights through a combination of case studies, workshops and by creating compelling, fact-based presentations.

This course develops a strategic and analytical understanding of digital marketing, artificial intelligence, and data‑driven decision making, and their implications for organizations and consumers in a dynamic business environment. Students apply contemporary digital marketing tools and analytics to design, integrate, and evaluate marketing strategies across digital channels, including web, search, social media, email, and mobile platforms. The course emphasizes the responsible integration of artificial intelligence—such as large language models, generative engine optimization (GEO), and agentic AI—into marketing planning, execution, and performance measurement, while addressing ethical, legal, and governance considerations essential to building and managing a sustainable digital marketing ecosystem.

MKTG 6575 focuses on the power of social media analytics and strategy in enabling businesses to make informed, ethically sound decisions. Students will explore key strategies and techniques, including social listening, social CRM, and social network analysis, to create effective campaigns and optimize resource allocation. The course offers hands-on experience with major social media platforms, where students will develop content, design paid campaigns, and analyze performance data, all while addressing legal and ethical considerations in data collection and management. By the end of the course, students will earn industry-recognized certifications and gain proficiency using AI. The course integrates lectures, case studies, interactive discussions, and an experiential learning project, helping students transform data into actionable insights to elevate social media marketing strategies.

With unprecedented growth in data availability, companies are increasingly focusing on data driven decision making in marketing. As most of this data is multivariate, an understanding of statistical techniques used to analyze it gains paramount importance. The objective of this course is to develop skills with a range of procedures for multivariate data analysis involving dimension reduction, pattern recognition, classification, and prediction. Students will engage in experiential exercises that require utilizing statistical software to organize and analyze data, interpreting the results, and presenting actionable conclusions for decision making.

Pricing is the only element of the marketing mix that generates revenue. The other marketing instruments typically drive cost in the process of creating customer value, which is recaptured through effective pricing. This course establishes a foundation for effective pricing decisions by teaching key economic, analytical, and behavioral concepts associated with costs, customer behavior, and competition; introduces students to advanced pricing techniques that aim to create additional value, including dynamic pricing, segmented pricing, pricing structures, and promotions; and highlights practical applications of these approaches within specific industry contexts. Through a mixture of lectures, case studies, and analytical techniques, the course will prepare students to address strategic and tactical issues related to pricing.

Experiments are widely used in marketing research to investigate cause and effect relationships, such as the impact of a price change on sales, or the impact of a new promotional campaign on brand awareness, or the impact of a change in the ingredients of a product on brand acceptance. This course is intended to acquaint the student with the basic topics of experimental design and analysis. It is intended to provide an understanding of the components of an experiment, the various types of experimental designs, the analysis of experimental results, and when experiments are indicated as a research option.

This course examines recent practitioner and academic literature in various areas of marketing, incorporating guest speakers as appropriate. Topics vary each semester to fit the interests of the seminar participants.

In this course, students demonstrate how the knowledge and skills learned in the MS in Marketing Strategy and Analytics program can be applied to business problems. Students will demonstrate this competence by providing socially responsible strategic recommendations to a local organization to solve a business problem in order to gain competitive advantage. Students undertake a major research project as a central activity in this course, drawing on the expertise and research methodologies they have developed in the program.

This course explores the requirements gathering, system analysis, software design methods and prototyping of software application following the software processes required for the production of high quality software. Techniques for creating documentation and using software development tools will be presented. Students will gain experience in software project management; requirements, analysis, and design; procedural maturity; social, ethical, cultural, and safety issues in software development; interpersonal skills for management and team membership; and the software engineering discernment of systems architecture. Undergraduate equivalent: SWEG 3301.

This course is designed to introduce fundamental concepts of object orientation techniques.Through the use of case studies and project work that has the student gradually building a large design specification, students will achieve an understanding of how complex applications are designed and built. Undergraduate equivalent: SWEG 3302.

This course introduces the student to developing browser applications for use on the web. Students learn client side concepts including the display of static information. The course topics include designing and authoring web pages, usability, search engine optimization, markup languages, style sheets, the client side document object model, and making web pages dynamic on the client side. Undergraduate equivalent: CPSC 2304.

This project-oriented course examines the fundamental aspects of mobile computing, application architecture, and mobile application design and development. Students will learn application development on the Android platform. Students will complete a hands-on project building a prototype mobile application. Topics include user interface design and building, input and data handling, and network techniques and GPS and motion sensing. Students are expected to work on a project that produces a professional-quality mobile application. Projects will be deployed in real-world applications. Undergraduate equivalent: CPSC 4305.

In this course, students apply in-depth techniques and experience various roles incorporated into one of the main approaches to software development which is agile methodology. It uses detailed knowledge about each of the major traditional software engineering phases to explore a more iterative approach for development of faster and more adaptable software. Proficiency in programming is expected of the students entering this course. Undergraduate equivalent: SWEG 4312.

This course is designed to benefit computational and experimental biologists to understand the principles of analyzing biological data, building models and testing hypotheses using computer science paradigms. Students will learn how to build computational tools that are used to analyze DNA content, identify protein binding patterns, compare sequences, and discover variation within genomes. Undergraduate equivalent: CPSC 4315.

This course will provide a practical introduction to analysis of biological and biomedical data. Basic statistical and machine learning techniques will be covered, including descriptive statistics, linear regression, non-linear regression, classification/prediction, and biomedical data visualization. Emphasis will be on how to choose appropriate data analysis models and how to assess statistical significance. This course will benefit data scientists to apply data science techniques to analyze biomedical data or clinical data. In addition, this course is also designed to benefit computational and experimental biologists to understand the principles of analyzing biological data, building models and testing hypotheses using computer science paradigms. To visualize data and carry out data analysis, students will learn R or Python, and other programming languages for statistical computing and graphics. The class will be a combination of lecture and computer lab. Undergraduate equivalent: CPSC 4317.

This course will cover in-depth methods for software testing, reliability and maintenance of software. Students will learn the principles of software testing and how to apply software testing techniques to the development of quality software and how to deploy software systems, maintain, enhance and reuse software systems. Undergraduate equivalent: SWEG 4320.

This course explores software project activities from conception to completion based on best practices. Topics include software systems engineering, personal/team software process management and control, and project planning and management. Through group and individual activities, students apply project management tools and techniques, and address typical problems that occur during the life cycle of the software project. Undergraduate equivalent: SWEG 4321.

In this course, students investigate visual analytics tools and techniques used to synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data and to communicate the findings effectively for decision-making. Extensive use of case studies based on real-world events will be used to illustrate course concepts. Students will apply visual analytics techniques toward a focused research problem in a real-world application or a domain of interest. Undergraduate equivalent: CPSC 4322.

In this course, students will be given an extensive overview of the various components of cybersecurity including software development, operating systems, databases, and networks. They will learn cybersecurity concepts, issues, and tools that are critical in solving problems in the computing security domain. The course will use lectures, reading assignments, and interactive lab exercises to re-enforce the concepts that are introduced. Undergraduate equivalent: CPSC 3333.

In this course students will be given the basic notions and theory of digital forensics. For file systems and operating systems, the class covers investigative techniques and legal and technical considerations that the examiner should make. They will learn concepts, challenges, and tools in applying digital forensics examinations. The course includes, but not limited to, topics in the suggested curriculum of CDFE certification. The course will use lectures, reading assignments, and interactive lab exercises to reinforce the concepts that are introduced. Undergraduate equivalent: CPSC 4335.

This course will introduce the foundations of cloud computing, and familiarize students with the core concepts needed to build, deploy and manage applications in a cloud. Besides the theoretical underpinnings, emphasis will be put on practical experience of using cloud resources and services. Concepts like microservices and containers will be discussed in depth, as well as best practices for building successful cloud native applications and implications for development and operational processes. The course will be a combination of lectures and hands-on experience of a public cloud. Undergraduate equivalent: CPSC 3349.

This course offers a thorough introduction to data science, focusing on both practical skills and theoretical knowledge across a wide range of topics. It guides students through the entire data science lifecycle, starting from the basics of data wrangling, exploratory data analysis, and visualization, to more advanced topics such as statistical inference, machine learning, and natural language processing. Python with web-based interactive computing platforms, such as JupyterLab, are utilized as the primary tools for hands-on learning. Through a combination of lectures, hands-on projects, and assessments, students will learn to derive insights from data and make informed decisions based on data analysis.

This course, which examines computational and theoretical accounts of human intelligence, includes knowledge representation, commonsense reasoning, planning, natural language understanding, machine learning, and deep learning. Undergraduate equivalent: CPSC 4355.

This course focuses on the steps required to build and maintain relational database infrastructure for modern n-tiered applications. It covers logical and physical design, implementation of the database, the use of the database to meet the informational needs of a software system, and the installation, operation and maintenance of the software. Specific topics include database design, SQL, interacting with the DBMS, and backup and recovery of data security. Students perform a number of hands-on exercises using the Oracle Database Server running on the Microsoft Windows platform. Undergraduate equivalent: CPSC 4357.

This course will provide a practical introduction to machine learning applications such as face recognition, clinical diagnosis, speech recognition, natural language processing, or image classification. Topics such as regression, classification, neural networks, deep learning, and ensemble methods will be discussed. Emphasis will be on how to choose appropriate machine learning and deep learning models and how to evaluate their performance. The class will be a combination of lecture and computer lab. Undergraduate equivalent: CPSC 4360.

This course offers a comprehensive introduction to the fundamental principles, theories, and practical aspects of deep learning. Lectures will begin with the basics of shallow neural networks before progressing to complex deep neural network structures. The topics include an overview of key neural network architectures, such as convolutional, recurrent, and autoencoder networks, and their applications in computer vision for tasks like image classification and segmentation, as well as in natural language processing for text classification and machine translation. Additionally, students will learn to craft deep learning architectures using Python on open-source machine learning platforms. Through a combination of lectures, practical exercises, and final projects, students will acquire the ability to implement deep learning models in real-world situations. Undergraduate Equivalent: CPSC 4366.

This course offers students an in-depth overview of quantum computing, emphasizing both theoretical foundations and applied techniques. Students will explore the principles of quantum mechanics relevant to computation, including qubits, superposition, entanglement, and measurement. Key topics include quantum gates and circuits, quantum algorithms and applications. The course integrates hands-on experience with quantum programming, enabling students to simulate and implement quantum algorithms on real and emulated quantum hardware.

This course is a study of object oriented software component design. This course introduces object oriented programming and its use in problem solving with abstract data types such as lists, linked lists, stacks, queues, graphs, and trees.

This course will introduce the foundations of security program management and familiarize students with the core concepts needed to build, deploy, and manage security controls and policy to protect against today's cyber threats and regulations. Besides the theoretical underpinnings, emphasis will be put on practical experience of using security governance resources. Concepts like security policy/standards, governance, risk management, and program management will be key to ensuring effective security program management. The course will be a combination of lectures and hands-on collaborative working experience in building a security program.

This course will introduce the core concepts of detective and preventative security and the venues that threat agents use to compromise and breach systems. Students will learn to evaluate their environment for potential attacker entry points physical, virtual, and electronic, and come up with solutions to deploy to prevent intrusions. Emphasis will be placed on theoretical occurrences, but will also include practical experience of using prevention applications. Additionally, research on methodologies used by attackers will be required from outside resources (internet) which will be shared with the class as a whole. The course provides a current status of what is prevalent in the evolving cybersecurity domain.

This course introduces the internal operations of modern operating systems. Students will learn how to program on non-Windows OS platforms. The topics cover a brief history of operating systems, the major components of modern operating systems, and the object-oriented methodology on UNIX-like platforms. Various UNIX tools will be used in the course and students will study examples using object-oriented programs as well as large system integration by object-oriented methodology.

This course will introduce the foundations of vulnerability program management and familiarize students with the core concepts needed to build, deploy, and manage vulnerability management controls that help identify risk and help prioritize remediation and determine risk to protect against today’s cyber threats. Besides the theoretical underpinnings, emphasis will be put on practical vulnerability management experience. Concepts like vulnerability discovery, reporting and assessing risk, threat modeling, and security testing are key to managing a vulnerability management program's risk posture. The course will be a combination of lectures and hands-on a collaborative working experience in building a vulnerability management program.

Topics covered include graphics programming, information visualization general principles, visualization techniques for one-dimensional, two-dimensional, and N-dimensional information, graph visualization, information visualization lifecycle: representation, presentation, interaction, perception, and interpretation, as well as theories behind information visualization, and focus+context techniques. This course also includes the implementation of techniques presented in lecture. Students are encouraged to devise new techniques, implement them, and determine their effectiveness. Students will be required to complete in-depth assignments, read, summarize, and present recent journal papers from the information visualization literature, and prepare term papers with regard to an information visualization research topic. Students will also be required to specify, design, implement, and document a semester-long software project related to information visualization. Undergraduate equivalent: CPSC 4521.

This course introduces students to the foundations of Human Computer Interaction and how it applies in software engineering and research settings. Students will learn how to design user interfaces based on the capabilities of computer technology and the needs of human factors. They will design user interfaces and learn how to implement a prototype from a list of informal requirements. It will also introduce students to issues related to human subject research as well as ethical implications of human computer interaction.

This course gives students a fundamental understanding of current social engineering methods in the information security arena. Deception and human behavior is exploited to gain valuable information, which is very relevant to today's growing security concerns. This course is another key class in the information security track within the software engineering program, and builds upon the weaknesses in the human factor. Areas of discussion will be methods, current trends, and most of all countermeasures. Instruction includes lectures and discussion assignments which involve analyzing current work places and social gatherings coupled with scenarios of exploitation.

This course provides an in-depth study of selected topics in software engineering of particular interest to the students and instructor. The course is counted as a major elective/specialization course. The topics and prerequisites will be announced when this course is offered.

This course is an individualized study under the supervision of the faculty member. The course emphasizes individual creativity. Students work with a faculty mentor in studying and investigating topics of current interest in software engineering. Enrollment by permission only.

This course is intended for individuals who need an understanding of the client-server environment, with any emphasis on network security. The OSI Model, network concepts and network architecture are discussed. The components that make up a network, including cabling, wiring hubs, file servers, bridges, switches, routers, network interface cards, network operating systems, and network software and hardware configurations are discussed. Network architectural concepts, wide area networks, remote access, and segmentation are discussed. Operating systems will be discussed and demonstrated. Featured is the seven-layer OSI model, the foundation of today's communication protocols. Students will work with various security protocols and configure routers and switches with security methods.

This course covers advanced topic of Java programming. Topic covers multithreading, networking, nested references, design patterns, JDBC, persistence, I/O and advanced GUI such as swing. Data structure concepts such as linked list, tree and basic searching and sorting algorithms will be covered. Lab component included.

Advanced server-side Java technologies. Coverage includes state-of-the-art explorations into server-side technologies such as JDBC, Google Web Toolkit, Enterprise JavaBeans (EJB), Android, XML, etc., as time permits. Lab component included.

This course teaches software engineers how to produce robust, scalable, data driven JavaScript web applications. JavaScript is currently the most popular general-purpose programming language for web development. In this course students learn a wide range of JavaScript concepts. Topics covered in the course include web application architecture and organization; information management across distributed computing systems; connectivity to parallel and distributed database / web service systems; custom and dynamic web controls; web forms; and best practices. Towards the end of the course, students will engage in a major project that will require application of acquired course knowledge and skill. Students will also be able to articulate the complexities involved in creating and publishing an interactive JavaScript based web site. Programming will be part of this course. Finally, students will be able to implement best principles and practices for securing their web application.

This course provides students with a comprehensive foundation in building and deploying modern generative artificial intelligence (Gen AI) systems. The course examines the evolution of foundation models, large language models (LLMs), diffusion models, and multimodal systems, with emphasis on practical integration into scalable systems. Students explore several applied techniques, starting from deep learning models and all the way to agentic systems, along with evaluation methodologies, security, reliability, and secure deployment practices. The course builds foundations for responsible AI engineering, regulatory compliance, ethical implications, and risk management. Through hands-on projects and case studies, students design, evaluate, and deploy generative AI solutions within professional and interdisciplinary contexts.

This is a course designed to provide the student with the tools necessary to manage Windows servers. The topics include user management, installation and configuration of web servers, mail servers, FTP servers, LDAP and backup, and other routine systems and network administration.

This course introduces the student to the techniques used and capabilities of modern pattern recognition systems with an emphasis on those that can learn and improve their performance as they are used. After a short review of some necessary mathematical concepts (probability, stochastic processes, and vector spaces), the student is introduced to the problem of representing real-world problems to a system. Selected real world applications are used to show examples of some valid representations (e.g. speech and handwriting) to provide insight and experience in the application of recognition systems. Several important recognition engines are then described and analyzed for their effectiveness in recognition/synthesis/learning systems. The use of additional knowledge bases dealing with the problem environment is then introduced to increase system performance and overall recognition system structures are discussed.

This course explores the development and evaluation of algorithms. This class covers classic algorithms, algorithm analysis, searching and sorting algorithms, dynamic programming, heuristics, and graphic algorithms. Algorithm efficiency and performance is a focus as the student gains experiences through problems and programming projects.

This course covers topics in database implementation designed to provide software engineers with a wide variety of server-side problem solving techniques. Topics include cursors, query and index optimization, advanced SQL programming, distributed databases, object-oriented databases, clustering, partitioning, and working with XML and other unstructured data. While Microsoft SQL Server is primarily used for demonstration, the topics covered are applicable to any database platform, and the different approaches of the major database vendors are frequently contrasted. The format consists of lecture and lab components.

This course examines the tools, techniques, and processes used in the design and development of data warehouses. As such we will examine how to successfully gather structure, analyze, and understand the data to be stored in the data warehouse, discuss techniques for modeling the data in the data warehouse, discuss the ETL process, and describe techniques for presenting and analyzing the data in the warehouse. We will also discuss capacity planning and performance monitoring. Microsoft Analysis Services and Sybase ASIQ will be examined as approaches for implementing a data warehouse.

This course teaches site developers how to create robust, scalable, data-driven ASP.NET Web. Students learn how to create ASP.NET applications using a text editor and the command-line tools, as well as using Visual Studio. Topics include the .NET framework, web forms, validation controls, database connectivity, web services, component development, user controls, custom server controls, and best practices, etc. At the end of the course, students will be able to describe the issues involved in creating an enterprise web site, creating and publishing a web site, creating interactive content for a website, adding server scripting to a web page using ASP.NET, implementing security in a website, and reading and writing information to a database from ASP.NET.

This course is an introduction to the PHP programming language. Topics include installation and configuration with the Apache HTTP server, variables and data types, language syntax, control structures, functions, strategies and tools for handling input and generating output, error handling, sending email, manipulating dates and times, string manipulation and regular expressions, SQL and MySQL database access. The course also covers advanced topics such as MVC model-based web application development using framework and packages from the PHP Extension and Application Repository (PEAR). At the conclusion of the course, students will be able to design and implement scalable data-driven web applications.

This course examines business intelligence concepts, methods and processes used to improve data-centric business decision support solutions with a particular focus on data mining techniques. Students will first examine the principles and practices of gathering and retrieving large volumes of data for analysis and synthesis. Next, students will examine analytical techniques for extracting information from large data sets. In particular, the course examines the following data mining techniques: classification, estimation, prediction, and clustering. During the course, students will also discuss knowledge management, how organizations manage and use the knowledge that they acquire, and presentation of data.

This course is structured around enterprise and web applications and the data security associated with these applications. It encompasses the encryption schemes of transmission to execution of code and complete flight of an execution. Common countermeasure and best business practices that help ensure a solid security understanding are the objectives of the course.

This course presents concepts and develops skills needed in designing, implementing, and troubleshooting local and wide area networks. Students design and configure LAN and WAN using routers and switches, learn the components of wireless networks, and how to configure and troubleshoot a network and optimize its performance. The course also provides numerous lab opportunities to configure and troubleshoot networks with Cisco routers and switches.

This course covers current information security practices and countermeasures put in place to safeguard against security breaches. The course reviews internet infrastructures such as firewalls, IDS systems, and honey pots. Additional areas include risk analysis, computer-use policies, physical security, internet/intranet security, malware, firewall infrastructure, and current information security issues.

In this two-semester capstone sequence, students form teams, perform a technical study, and design software systems based on either their customer's requirements, develop, test, and deploy software systems. The results of these projects provide a library of case studies, designs, and software development techniques, and project management skills that are of general interest to local information technology professionals. A capstone prospectus, approved by your advisor, must be submitted to and accepted by the director of the program prior to starting the capstone sequence.

In this two-semester capstone sequence, students form teams, perform a technical study, and design software systems based on either their customer's requirements, develop, test, and deploy software systems. The results of these projects provide a library of case studies, designs, and software development techniques, and project management skills that are of general interest to local information technology professionals. A capstone prospectus, approved by your advisor, must be submitted to and accepted by the director of the program prior to starting the capstone sequence.

In this two-semester sequence of thesis courses, students will work on an individual research project that they should formulate as a problem, solve under the guidance of a faculty member, and communicate the results. Work involves literature search, writing a proposal, analysis and/or implementation with critical thinking, and writing convincingly. The student must also submit a final paper for possible publication in a refereed journal appropriate to the topic.

In this two-semester sequence of thesis courses, students will work on an individual research project that they should formulate as a problem, solve under the guidance of a faculty member, and communicate the results. Work involves literature search, writing a proposal, analysis and/or implementation with critical thinking, and writing convincingly. The student must also submit a final paper for possible publication in a refereed journal appropriate to the topic.