Duke - The Fuqua School of Business

Courses

Beginning in July and running through early May, the MQM program is organized into 5 terms that are each 6 weeks long. In the fifth term, one of your courses consists of a team capstone project.

You'll apply for a functional track in either Finance, Marketing, Forensics, or Strategy and take required courses in your functional track, data analytics, and business communications/critical thinking in every term. This gives you a distinct business lens and prepares you for an early recruiting process. Your elective courses start in the second term or later depending on your discipline and will give you more breadth in your area of expertise. 

The curriculum breaks down as:

  • Required courses in data science and analytics
  • Required courses in critical thinking, communication, and collaboration
  • Required courses in your functional track
  • Your choice of elective courses from the full MQM course offerings, including courses specific to other tracks 
course calendar

Business Fundamentals

  • The goal of this course is to give students an understanding of general business principles to enhance their effectiveness in the organizations they work for and lead in the future. The course is designed for students with limited previous exposure to the business topics of accounting, finance, operations management, marketing, and strategy. This structure is intended to provide a coherent introduction to a broad range of business topics, rather than a detailed treatment of any individual topic.

  • Management decisions are increasingly data-driven and supported by quantitative arguments, yet these decisions are necessarily made under conditions of uncertainty. This course introduces a framework for thinking about data-driven problems involving uncertainty and develops probabilistic and statistical tools for understanding, analyzing, and interpreting data. Specifically, the objective of the course is to provide an appropriate foundation in applied probability and statistics necessary for data-driven quantitative managerial decision-making and for subsequent courses in the program.

  • This course explores the fundamentals of data storage, cleansing, and retrieval. We will examine structured versus unstructured data, relational database design, and data integrity issues.

Finance Track

  • The Introductory Finance course covers all the basic concepts in finance — discounting, equities, bonds, portfolio diversification, CAPM, and WACC.

  • This course addresses the construction and interpretation of corporate financial reports. The course focuses on two complementary aspects of financial reporting: management’s responsibility for applying objective and informed judgment to implement financial reporting standards in preparing corporate financial reports and the financial statement user’s interpretation and analysis of those reports. The course will focus on areas of financial reporting with relatively difficult and complex required judgments and estimates. After completing the course, students will be able to work with the FASB’s Codification of US GAAP to identify and interpret authoritative guidance.

  • This course covers the key concepts in portfolio management. Key topics include mutual funds, multifactor models, asset classes and asset allocation, foreign exchange markets, international investment and capital budgeting, hedge funds, private equity and venture capital.

  • Explores key issues in derivatives and financial risk management. It develops tools for valuing and modeling the risk exposures of derivatives, with the ultimate goal of deploying these instruments in a corporate or financial risk management setting. The course is divided into three parts, covering (1) linear instruments including forward, futures and swaps, (2) non-linear instruments such as options, and (3) corporate finance and risk management applications of both types of instruments. 

  • This course covers key concepts in fixed income securities including: bond pricing and term structure of interest rates, interest rate risk management, interest rate derivatives, inflation, fed funds and monetary policy, term structure modeling, continuous time modeling, and no arbitrage modeling.

  • This course covers the main concepts of financial risk management for banks and asset managers. These include: risk management for banks (mortgages, prepayment risks, commercial loans, credit risk, Basel Accords, capital requirements, stress testing) as well as risk management for asset managers (security selection, selection of weights, systematic risk of portfolio).

Marketing Track

  • Communication and distribution channels are rapidly evolving in the context of digital technology and consumer migration to the Internet. As a result, advertising budgets are shifting to display and search, and goods are increasingly marketed and purchased online. This course will overview digital markets along with the associated key performance indicators and the tools being used to improve the efficiency of digital marketing. Topics include advertising markets and integrated marketing communication; attribution; ad networks and media buying; campaign performance measurement; social media; search marketing; auctions; e-commerce; marketplaces; assortment and pricing; omni-channel marketing.

  • This course is about gathering, analyzing, and interpreting data about markets and customers. It has been designed for analysts who will be working with customer-generated data, and so is intended for students wanting to go into marketing, consulting, and entrepreneurship. Topics include analyzing data to understand customers and inform marketing decisions; evaluating the quality and usefulness of available data and analyses conducted by others; communicating analysis-based conclusions to colleagues and managers.

  • This course employs a number of overlapping frameworks. Foremost among these is the concept of the customer lifecycle. This concept decomposes customer interactions into birth, growth and death. Birth involves customer acquisition or first sale. Growth involves customers buying more items and spending more on these items (denoted cross-selling and upselling). Death involves leaving the firm, typically called attrition or churn. Collectively, the stages of the customer lifecycle imply a revenue and profit stream that can be managed. The financial discounting of this profit stream yields the net present value of a customer, often called customer lifetime value, or CLV for short. Summing CLV across customers — or customer equity — is a major component of the value of the firm, net of investments. This course will develop quantitative methods to improve the efficiency and effectiveness of customer relationship management (CRM) activities. We will introduce an array of CRM-specific models, tools, and frameworks and apply them to real-world problems.

  • While most of a firm’s marketing activities (such as product design, sales or advertising) create value for the customer, pricing is the only marketing activity that creates value for the firm. Despite the significance of pricing for a firm’s profits, many managers lack the quantitative and strategic skills to set prices. This course will examine the quantitative tools used to formulate pricing strategy, and address how to formulate pricing tactics. Topics include estimating the value of a product or service; how to estimate own-price and cross-price elasticities to use in pricing decisions; determining when promotions should occur; and how to set prices that are consistent with both the firm’s pricing strategy and its overall marketing strategy.

Strategy Track

  • This course focuses on empirical techniques used to more deeply understand economic markets in which firms operate. We will identify and empirically analyze a variety of market structures, ranging from perfect competition to oligopoly (rivalry between a small number of competitors) to monopoly (one dominant firm). We want to develop the skills necessary to make effective managerial decisions and strategic choices based on the quantitative analysis of a firm’s productive capabilities and the market in which it operates. We will also focus strongly on the insight necessary to optimally identify, estimate, interpret, and test an economic model. This course is designed for students who are interested in utilizing empirical economic models for market analysis. While not fully inclusive, this would comprise students interested in consulting, strategic analysis, operations, marketing, and mergers & acquisitions.

  • This course focuses on prescriptive analytics techniques to understand and improve a firm’s operational capabilities. The course is divided into two modules. In the first module, called Process Analytics, the focus will be on individual manufacturing and service processes. Students will learn to map and visualize complex processes, identify and improve process performance, quantify and analyze the impact of randomness on processes, and visualize process quality. In the second module, called Supply Chain Analytics, the focus will broaden from a single process to the entire supply chain.  Students will learn to forecast uncertain demand, optimize inventory, and design distribution networks and supply chains to match supply with demand. The course is designed for students who are interested in operational and consulting positions, who may interact with operations units of a firm (e.g., product or marketing managers), and who need to understand supply chain strategies at a high level. Key methodologies used in the course include estimation, forecasting, analytic modeling, optimization, simulation analysis, and data visualization.

  • Strategic management raises the broad questions faced by management and explores how to structure the firm to use the insights from externally-focused and internally-focused analytics teams. The course will cover: industry analysis, business unit strategy, corporate strategy, estimating learning curves, project management, implementation and organizational form, and incentives.

  • This course focuses on prescriptive analytics techniques to understand and improve a firm's organizational processes. Topics include diversity analytics, predicting employee turnover, predicting employee performance, recruitment analytics, and intervention impact.

Forensics Track

  • This course addresses the construction and interpretation of corporate financial reports. The course focuses on two complementary aspects of financial reporting: management’s responsibility for applying objective and informed judgment to implement financial reporting standards in preparing corporate financial reports and the financial statement user’s interpretation and analysis of those reports. The course will focus on areas of financial reporting with relatively difficult and complex required judgments and estimates. After completing the course, students will be able to work with the FASB’s Codification of US GAAP to identify and interpret authoritative guidance.

  • Students will develop increased awareness of fraud in businesses, the circumstances in which fraud arises, techniques for detecting, measuring and preventing fraud, and skills needed to help in the eventual resolution of discovered frauds. This course demonstrates the various aspects of fraudulent financial reporting, including the identification of fraud schemes and analytical techniques in uncovering fraud in financial reports. The course includes written projects on executed frauds in public companies and material weakness reports. Students will gain an understanding of SAS 99 as it pertains to the consideration of fraud in a financial statement audit, the nature of internal controls and the role of initiatives to curb fraud both from the PCAOB and from provisions of the Sarbanes-Oxley Act. The course presumes students have basic knowledge of the following components on Form 10-K: MD&A, financial statements and footnotes, auditor report, and management representations.

  • The cutting edge of fraud detection now combines data analytics with expert analysis. With the processing power and volume of data available to most large businesses, it is now possible to use quantitative techniques to identify potentially fraudulent behavior. In some cases, this behavior can be identified without ever having previously seen the potential fraud pattern. But when a new fraud pattern is detected, these techniques can help identify potential perpetrators and put corrective measures into place.  This course will explore analytics techniques currently being used to identify and prevent fraud. They will be looked at in relevant business contexts.

  • This course introduces the concepts of (a) Enterprise Risk Management which is the enterprise-wide process applied in a strategic setting to identify potential events that may affect the entity, to manage the risk of these events, and to provide reasonable assurance regarding the achievement of entity objectives. (b) Internal Control which is a process that provides reasonable assurance regarding the achievement of objectives relating to operations, reporting, and compliance. And (c) Fraud Deterrence which entails detecting, preventing, and responding to individuals acting outside the organization’s expected standards of ethical conduct for financial or personal gain.

    The class will be viewed through the COSO (Committee of Sponsoring Organizations of the Treadway Commission) framework, as well as incorporating process analysis and individual psychology.

Critical Thinking, Communication & Collaboration

  • Introduces basic topics in business communications. These include interacting with clients, running meetings, and business etiquette. It also covers career management skills such as networking, preparing resumes and cover letters, and interviewing. 

  • Explores techniques to help students learn how to effectively interact in the business environment. Building on Business Communications 1, this course will provide additional opportunities to develop presentation and career management skills. We will explore change management, cultural differences, and interviewing skills.

  • Data problems are typically ill-structured and ambiguous, at least at the outset. Aside from having technical expertise, as a successful analyst you must excel in 3 additional areas — critical thinking, communication, and collaboration. Critical thinking encompasses overcoming common cognitive biases, in addition to activities such as defining the problem appropriately, asking good questions, identifying the needed data, and exploring the data from multiple perspectives. Communication is important because an analysis will only have impact and positive value to the extent that the results are communicated both accurately and comprehensibly. Finally, collaborating effectively is crucial, because today’s problems are often complex and best tackled in teams where members differ in expertise and specialization. After taking this course, you will be a better thinker, communicator, and collaborator — 3 skills that will serve you well in both analysis and in life.

Multi-Track Courses

  • This course will investigate the algorithms/techniques currently being used in many industries to convert data into insights. We will focus on (1) General principles that are long-lasting despite the rapidly changing technology. (2) Specific analytics techniques such as predictive modeling, clustering, random forests, neural networks, etc. (3) "Hands-on" analyses of actual datasets to develop methodologies. Ultimately, the course aims to develop "data-analytic" thinking that will enable you to evaluate how data can improve performance, identify opportunities, and assist in decision making for managers.

  • Successful management requires the ability to recognize a decision problem, understand its essential features, and make a smart choice. However, many decision problems — particularly those involving uncertainty or many variables — are difficult to grasp intuitively. In these cases we may benefit from using a computer-based mathematical model to explore and evaluate the possibilities in a systematic fashion. This course introduces several commonly used modeling frameworks and provides an introduction to the art and science of modeling decisions. The ideas and skills learned in this course are applicable in most areas of business. The course is divided into 3 parts: (a) the use of decision trees for structuring decision problems under uncertainty; (b) Monte Carlo simulation, a technique for simulating decision situations with many uncertainties; (c) optimization, an approach for finding the best possible solution in problems with many decision variables and constraints.

  • This course explores techniques to effectively communicate information about data using graphical means. We will utilize popular data visualization tools such as Tableau, Crystal Report, and/or R.