Finance: Courses

Formerly SM104. Finance elective for Business minors. The course is designed to continue the student experiences from SM132 and AC221. The course will provide students with an overview of a broad range of financial topics including: personal financial decision making, corporate finance, firm valuation, portfolio management, risk and return, as well as a timely discussion of current events. Upon successful completion of the course students will understand how to navigate personal finances including car loans, student debt, mortgages, retirement planning, and stock and bond investments; become familiar with methods of firm valuation, including discounted cash flow and trading multiples; be introduced to portfolio management theories such as diversification, systematic risk and beta and how to understand the risk reward tradeoff.

Component of QST SM323, The Cross Functional Core. Introduces students to the themes of financial decision making: valuation and risk management. The focus is on the problems of forecasting, capital budgeting, working capital management, project risk management, and financing in a cross-functional context. A semester-long business-plan project explores the interaction between marketing, operations, management information systems, and finance decisions. The course compares the financial objectives of the manager and the investor. Introduction to the time value of money, securities valuation, portfolio diversification, and the cost of capital. 4 cr.

Managing financial risk in the global environment. Introduction to foreign exchange markets, spot, forward, futures, options and swaps, and to the international bond and money markets. Discussion of market structure and participants, and main financial instruments. Analyzes and discusses tools of currency risk management. 4 cr.

Covers the theory of futures pricing and option pricing, and applies the theory to develop a framework for analyzing hedging and investment decisions using futures and options. Attention is paid to practical considerations in the use of these instruments, especially in financial risk management. 4 cr.

Examines financial structures and decisions in entertainment and media realms including feature film, television, music, live performance, sports, digital media, and related business endeavors. The course covers the various ways entertainment and media companies raise capital, budget capital, and manage return on investment to shareholders and other stakeholders. Students study business models within each segment to understand the financial, operational, and legal constraints and best practices under which media and entertainment firms operate. Offered in Los Angeles.

The financial system and its functions. The role of money and the importance of interest rates in determining economic activity; determinants of level of interest rates. Operation of central banks; the goals and instruments of monetary policy. The roles, activities, and risk management of financial institutions. Instruments traded in money and capital markets, and their valuation. Role of derivative securities; systemic risk and other contemporary issues in the financial system.

Required for Finance concentrators. Introduction to the investment management process. Defining investment objectives and constraints. Introduction to Modern Portfolio Theory, CAPM, APT, Efficient Markets, stock and bond valuation models. Introduction to forwards and swaps and their applications within investment strategies. Active vs. passive investment strategies, fundamental vs. technical analysis, trading practices, and performance evaluation. Introduction to the role of futures and options in hedging and speculation. Students are expected to become familiar with current events in the financial news.

Required for Finance concentrators. Covers the financial manager's role in obtaining and allocating funds. Includes topics such as cash budgeting, working capital analysis, dividend policy, capital investment analysis and debt policy as well as their associated risks. Valuation of companies, mergers and acquisitions, and bankruptcy are covered. The course requires using financial models and spreadsheets. Applications are made to current events and everyday business finance problems.

Exposes students to, and demystifies, the world of Private Equity (PE). The focus is centered on LBOs and their position in the "alternative asset" class. Students learn about the activities of a PE firm including formation, fund- raising, investing (including deal structure, terms, due diligence, and governance), and exiting. Also discussed are what other industry sectors serve or are affected by PE and who the players are. Case study and class participation will be the primary modes of learning.

Provides an overview of the economic functions provided by investment banks including a history of the industry, current events, and the difference between large, full service investment banks and smaller, boutique firms. Heavy emphasis on pro forma analysis and Initial Public Offering and M&A valuation techniques. Topics include: What do investment bankers do? What are the different types of analyses performed by investment bankers? What are the various types of financial securities? What is the underwriting process and how are securities priced? How are companies valued? How are potential synergies valued? The course will focus on the issuing process and pricing for equity, fixed income, and equity-linked securities. The course will also focus on the role of investment banks in mergers, acquisitions, divestitures, and other restructurings. Additional topics include equity research, capital markets industry regulations, as well as typical career paths and opportunities.

Students will be expected to have mastered key finance concepts including financial statement analysis, NPV, IRR and basic option pricing theory prior to entering the course. Introduction to financing sources for start-up firms, including angel funding, venture capital financing, boot-strapping, debt and other sources. Focus on capital structure analysis, capitalization tables, payoff diagrams, term sheets, equity incentives, cash flow projections and negotiating with investors. Students are expected to prepare case studies for class discussion and become familiar with current events in the financial news about start-up company financings.

Covers the analytic techniques used in fixed-income markets to value and measure risk on traditional fixed-rate bonds, floating-rate notes, bonds having embedded options (callable and putable bonds), structured notes, and interest rate derivatives used to manage bond portfolios (primarily interest rate swaps, caps, and floors). Extensive use is made of Excel spreadsheet analysis, including the development of a binomial term structure model to value securities. Focus is on the impact of counterparty and issuer credit risk in fixed-income valuation.

Students will be taught the fundamental skills in how to analyze a company and determine its suitability for investment. This course will teach the value-based approach to company analysis, which focuses on assessing a company's competitive advantage and its return profile. Key topics include competitive advantage, return on invested capital, financial modeling and financial statement analysis, and valuation.

The course will teach students how to use computational techniques to implement financial algorithms for security pricing and risk analysis including, bonds, stocks, and options. This will be a rigorous, hands-on programming course to prepare students for quantitative jobs in finance. The overall objective of the course is to enhance the students' understanding of the well-known financial models used to price securities including bonds and options and to evaluate the risk and return characteristics of stocks and portfolios. After the course, students will have a deeper understanding of investment portfolios, risk management techniques that use derivatives, and arbitrage strategies. Additionally, students will become comfortable with a modern programming language based on functional and object-oriented programming which will enhance their job opportunities in a variety of fields beyond finance.

This course is specifically designed to appeal to students who have a strong interest in both strategy and financial analysis. The focus of the class will be to bring financial analysis to the study of a company's strategy and learn how to analyze a company's financial statements to help evaluate the sustainability of a company's competitive advantage. This course utilizes that case-based approach in its teaching method and encourages active class participation.

Provides an introduction to and an understanding of real estate finance. Draws together and considers major functional areas including: structuring, ownership, finance, taxation, property valuation and analysis. The course provides a framework for decision making in the real estate investment and finance fields. The course is specifically designed to offer students interested in real estate careers a foundation from which to build. 4 cr.

Directed study in Finance. 2 or 4 cr. Application available on Undergraduate Program website.

This course offers an overview of fundamental financial analyses, such as time value of money, interest rates, basic valuation of cash flow streams, and basic stock and bond valuation. The content is relevant to understand a broad class of problems and decisions for businesses or individuals. It offers applications across decision domains. The teaching materials include online problem solving and case writing. Students may not take SM132 and FE101 for credit.

The objective of this course is to introduce the students to the theory and practice of corporate finance, and to provide the students with a set of analytical tools necessary to answer the most important questions related to firms' valuation and investment decision making first under certainty and then under uncertainty. The course can be divided into the following three building blocks: valuation, investment decisions, and the relation between risk and return.

The objective of this course is to introduce the students to the theory and practice of corporate finance, and to provide the students with a set of analytical tools necessary to answer the most important questions related to firms' valuation and investment decision making first under certainty and then under uncertainty. The course can be divided into the following three building blocks: valuation, investment decisions, and the relation between risk and return.

This course extends fundamental concepts of corporate finance and asset pricing introduced in the core. Corporate finance concepts covered are capital structure decisions, payout policy decisions, and real options. Asset pricing topics include market efficiency, multi-factor models for the risk and return, arbitrage pricing theory and contingent claim analysis and its use in valuation and risk management. The concepts are illustrated in practical examples that prepare students for their summer internships.

This is a course primarily on fixed-income debt securities and markets. Emphasis is placed on the factors that determine bond yields, factors such as the coupon and maturity structure, liquidity, credit risk, and tax status of the security, and on measures of return and risk, statistics such as the yield to maturity, horizon yield, duration, and convexity. We will cover government debt (Treasuries and municipals), corporate bonds (investment-grade and high-yield), agency (Fannie Mae and Freddie Mac) and mortgage-backed debt created via securitization (i.e., collateralized mortgage obligations). We will emphasize how interest rate and credit derivatives are used to manage portfolios of fixed-income securities.

Introduction to the investment management process. Defining investment objectives and constraints. Introduction to Modern Portfolio Theory, CAPM, Fama- French factors, APT, efficient markets, stock, bond and option valuation models. Introduction to forwards and swaps and their applications within investment strategies. Active and passive investment strategies, fundamental analysis, trading practices, and performance evaluation. Introduction to the role of futures and options in hedging and speculation. Arbitrage and hedge fund strategies. Understanding the assumptions underlying the different approaches and their limitations. Topics related to current events and the recent financial crisis.

This course covers the theory of futures, swaps and option pricing and develops a framework for analyzing hedging and investment decisions using these instruments. Attention is paid to practical considerations in the use of these investments, tax and accounting issues and the institutional features of the market in which the various instruments are traded.

ESG Equity Investing is an introductory course that provides the appropriate tools to analyze and undertake investments in publicly listed companies taking into account the social impact of these financial decisions. Different dimensions of social impact -- Environment, Social, and Governance -- are discussed along with corresponding ESG metrics available to investors. The core of the course deals with the integration of (quantitative-based) portfolio allocation models with (qualitative-based) ESG scores and objectives. The course also discusses how impact investing may affect the behavior of firms, as well as alternative channels through which investors can provide impact (private investments, activism).

Pre-requisites: FE721/722. Emerging markets (EMs) are an expanding asset class in the world of global finance. The objective of this course is to give students a technical framework for analyzing EM risk in a portfolio context. Knowing how individual EM countries respond to global challenges allows investors to better evaluate the risks and potential returns associated with investing in these economies. While the basic laws of economics and finance that apply to EMs are not different from those that apply to developed markets (DMs), there are important and often misunderstood factors that differentiate one group of countries from the other.

Private Equity (PE) is a major force in the capital markets, acquiring household names such as Dell, Toys R Us, Neilson, Nieman Marcus, and many more. This course exposes students to, and de-mystifies, the PE world. The focus is centered on LBOs and their position in the alternative asset class. Students learn about the activities of PE firms including formation, fundraising, investing (deal structure, terms, due diligence, governance) and exiting. We also discuss how other industry sectors serve or are affected by PE and who the players are. This is a capstone course that integrates marketing, strategy and finance to further the understanding of business evaluation. Case study and class participation are the primary modes of learning. Course offered jointly with undergraduate course SMG FE 450.

The focus of FE854 is on the development of financial and business skills to identify, evaluate, start and manage new ventures. A comprehensive understanding of finance is an essential ingredient in the "recipe" for business success. No longer can the assumptions underlying financial projections be treated as "black boxes." In many cases, the answer is less important than the analytical process used to calculate it. Readings for the course will primarily be in the form of case studies, and will be supplemented by guest speakers, presentations, and readings from academia and industry.

This course introduces the analysis and management of risk in the context of financial institutions. The objective of the course is to provide a conceptual framework for thinking about financial risk, covering both theoretical background and practical implementation

Graduate-level directed study in Finance. 1, 2, or 3 cr. Application available on the Graduate Center website.

Graduate-level directed study in Finance. 1, 2, or 3 cr. Application available on the Graduate Center website.

This doctoral course, is designed to provide students with an introduction to financial economics. This lecture-based course will cover no arbitrage conditions, preferences and risk aversion, portfolio selection, the capital asset pricing model, asset pricing and dynamic asset pricing. In addition to lectures, this class will include readings and assignments. Open to MBA students with faculty member's permission. Must have strong quantitative background and several courses in finance or economics.

This course provides a comprehensive and in-depth treatment of modern asset pricing theories. Extensive use is made of continuous time stochastic processes, stochastic calculus and optimal control. In particular, martingale methods are employed to address the following topics: (i) optimal consumption- portfolio policies and (ii) asset pricing in general equilibrium models. Advances involving non-separable preferences, incomplete information and agent diversity will be discussed.

PhD-level directed study in Finance. 1, 2, or 3 cr. Application available on the Graduate Center website.

PhD-level directed study in Finance. 1, 2, or 3 cr. Application available on the Graduate Center website.

This course prepares students in the MS Mathematical Finance program for the global employment market in quantitative finance. The course has the following objectives: to familiarize students with the foundational mathematics and statistics required for the MSMF program, to develop sound networking and job search strategies, to prepare students for 'quant' interviews, to develop good career management habits, and to familiarize students with important developments in financial markets and issues of the day that affect the global financial services industry.

MF 650 is offered to MS and PhD candidates in Mathematical Finance. The course affords graduate students the opportunity to complete an internship in the financial services (or a related) industry and serves to enhance the students' academic and/or research experience. MF 650 is required for all students pursuing the MSMF degree. It is an elective for PHD in Mathematical Finance students and has to be approved by the student's faculty advisor, department PhD Liaison and the PhD Program Director.

This course covers such topics as: financial markets (bonds, stocks, derivative securities, forward and futures contracts, exchanges, market indices, and margins); interest rates, present value, yields, term structure of interest rates, duration and immunization of bonds, risk preferences, asset valuation, Arrow-Debreu securities, complete and incomplete markets, pricing by arbitrage, the first and the second fundamental theorems of Finance, option pricing on event trees, risk and return (Sharpe ratios, risk premia), and the Capital Asset Pricing Model. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

In-depth discussion of object-oriented programming with Python and C++ for finance and data applications. Topics include built-in-types, control structure, classes, constructors, destructors, function overloading, operator functions, friend functions, inheritance, and polymorphism with dynamic binding. Case study looks at the finite differences solutions for the basic models of financial derivatives; as well as the design and development of modular, scalable, and maintainable software for modeling financial derivatives. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

The course focuses on the valuation, hedging and management of fixed income securities. Theoretical and empirical term structure concepts are introduced. Short rate models and the Heath-Jarrow-Morton methodology are presented. Market models and their application for the valuation of forwards, swaps, caps, floors and swaptions, and other interest rate derivatives are discussed in detail. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

A concise introduction to recent results on optimal dynamic consumption- investment problems is provided. Lectures will cover standard mean-variance theory, dynamic asset allocation, asset-liability management, and lifecycle finance. The main focus of this course is to present a financial engineering approach to dynamic asset allocation problems of institutional investors such as pension funds, mutual funds, hedge funds, and sovereign wealth funds. Numerical methods for implementation of asset allocation models will also be presented. The course also covers empirical features and practical implementation of dynamic portfolio problems. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

This course is an introduction to modern methods of risk management. The first half of the course focuses on market risk. Here, lectures cover risk measures (such as Value at Risk and Expected Shortfall), with a focus on computation of such measures in a dynamic, multi-asset environment using real-world data. In particular, students will learn to compute, back-test, and account for risk measures when both monitoring and constructing portfolios. Additionally, lectures cover scenario analysis, stress testing, and the measurement of severe tail risk via extreme value theory. In the second half of the course, lectures cover alternate types of risk. These include operational, liquidity, model, and counter-party credit risk. In particular, students will derive formulas for the valuation adjustments due to counter-party default. The approach to the topic is quantitative. The course is ideal for students with strong quantitative backgrounds who are seeking to understand issues pertaining to risk management and to master modern methods and techniques of risk control. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

The course covers the following topics: introduction to Blockchains and cryptocurrencies; contract theory for initial coin offerings; robo-advising; crowd wisdom; and privacy issues. Although the course introduces some Blockchain programming languages, e.g. Solidity, the emphasis of the course is on the economics of FinTech rather than on programming. Students are expected to be familiar with basic financial economics, econometrics, and stochastic processes.

This course provides a comprehensive and in-depth treatment of valuation methods for derivative securities. Extensive use is made of continuous time stochastic processes, stochastic calculus and martingale methods. The main topics to be addressed include (i) European option valuation, (ii) Exotic options, (iii) Multiasset options, (iv) Stochastic interest rate, (v) Stochastic volatility, (vi) American options and (vii) Numerical methods. Additional topics may be covered depending on time constraints. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

The derivatives market has experienced tremendous growth during the past decade as credit risk has become a major factor fostering rapid financial innovation. This course will provide an in-depth approach to credit risk modelling for the specific purpose of pricing fixed income securities and credit-risk derivatives. The course will explore the nature of factors underlying credit risk and develop models incorporating default risk. Types and structures of credit-derivatives will be presented and discussed. Valuation formulas for popular credit-derivatives will be derived. Numerical methods, for applications involving credit derivative structures and default risks, will be presented. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

This course covers the fundamental principles of statistics and econometrics. It is mandatory for all tracks of the MSc. program. The course first reviews the needed concepts in probabilities, properties of random variables, the classic distributions encountered in Finance. Then, we cover the principles of random sampling, properties of estimators, e.g., the standard moment estimators (sample mean, variance, etc..). The next major topic is the regression analysis. We study the OLS and GLS principles, review their properties, in the standard case and when ideal assumptions are not correct. The course ends with a study of time series ARMA models and volatility models such as GARCH and Risk-Metrics. The course makes intensive use of the R package. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

The course covers: the Feynman-Kac formula and the Fokker-Plank equation, stochastic calculus with jumps, Levy processes and jump diffusion models in finance, Bellman's principle of dynamic programming and the Hamilton-Jacobi- Bellman equation, classical problems for optimal control in finance (Merton's problem, etc.), investment-consumption decisions with transaction costs, the connection between asset pricing and free-boundary problems for PDEs, optimal stopping problems and the exercise of American-style derivatives, capital structure and valuation of real options and corporate debt, exchange options, stochastic volatility models, and Dupire's formula. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

This course develops the basic tools from measure-theoretic probability theory and stochastic calculus that are needed for an in-depth study of continuous time finance. Some related tools from asset pricing (e.g., risk-preferences and state-price densities) are introduced as well, and the basic ingredients of continuous time financial modeling are developed. The following topics are covered: probability and measure, the coin-toss space and the random walk, random variables and convergence, Gaussian distribution, martingales, Brownian motion, stochastic integration for semi-martingales and Ito formula, Girsanov's theorem, stochastic differential equations, continuous time market models and pricing by arbitrage, resume of Malliavin calculus, replication and pricing of contingent claims, market completeness and the fundamental theorems of asset pricing. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

This course introduces common algorithmic and numerical schemes that are used in practice for pricing and hedging financial derivative products. Among others, the course covers Monte-Carlo simulation methods (generation of random variables, exact simulation, discretization schemes), finite difference schemes to solve partial differential equations, numerical integration, and Fourier transforms. Special attention is given to the computational requirements of these different methods, and the trade-off between computational effort and accuracy. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

The course introduces students to a number of efficient algorithms and data structures for computational problems across a variety of areas within FinTech. In the first half of the course, a special programming language for blockchains, such as Solidity, is taught, and TensorFlow, a special Python library for deep learning models, is used to solve stochastic control problems in finance. In the second half of the course, advanced techniques for improving computational performance, including the use of parallel computation and GPU acceleration are surveyed; frameworks for big data analysis such as Apache Hadoop and Apache Spark are studied. Students will have the opportunity to employ these techniques and gain hands-on experience developing advanced applications. (This course is reserved for students enrolled in the Graduate Certificate in Financial Technology.)

This course surveys applications of machine learning techniques to various types of financial datasets. This course starts with financial data structure and features, then introduces deep learning and advanced supervised learning techniques. We will examine several machine learning applications in pricing, hedging, and portfolio management. Advanced methods for clustering and classification such as support vector machine and unsupervised learning will be introduced. Reinforcement learning and its connection with optimal control will be discussed. Text data will be introduced and analyzed using text mining techniques. Machine learning techniques will be applied to asset allocation. Strategy back-testing and strategy risk will also be discussed. (This course is reserved for students enrolled in the Graduate Certificate in Financial Technology.)

This course will introduce concepts of electronic markets, and statistical and optimal control techniques to model and trade in these markets. We will begin with a description of the basic elements of electronic markets, some of the features of the data, its empirical implications and simple microeconomic models. Next, we will study statistical tools to estimate and predict price and volatility of the high-frequency price. Then we will investigate algorithmic trading problems from the stochastic optimal control perspective, including the optimal execution problem and show how to modify the classical approaches to include order-flow information and the effect that dark pools have on trading. Trading pairs of assets that mean-revert is another important algorithmic strategy, and we will see how stochastic control methods can be utilized to inform agents how to optimally trade. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

This course is designed for students seeking to work as quants in a quantitative finance investments group. It covers utility theory, portfolio optimization, asset pricing, and some aspects of factor models, incorporating the impact of parameter uncertainty. The course does not cover risk management or fixed income instruments, nor does it describe how the financial services industry works. Rather, it teaches how a quant should optimize a portfolio. The course makes extensive use of R (Excel or VBA are not substitutes), optimization theory, statistics, regression theory (OLS, GLS, testing theory), and matrix algebra. Students should be very comfortable with these concepts before taking the course; further, students should already have taken a finance course covering expected returns models (CAPM), options and futures. The course emphasizes the ability to prove theoretical results and their validity, an essential trait for investments quants. Students who completed QST FE825 may not take this course for credit. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

This is the second course of the econometrics sequence in the Mathematical Finance program. The course quickly reviews OLS, GLS, the Maximum Likelihood principle (MLE). Then, the core of the course concentrates on Bayesian Inference, now an unavoidable mainstay of Financial Econometrics. After learning the principles of Bayesian Inference, we study their implementation for key models in finance, especially related to portfolio design and volatility forecasting. We also briefly discuss the Lasso and Ridge methods, and contrast them with the Bayesian approach Over the last twenty years, radical developments in simulation methods, such as Markov Chain Monte Carlo (MCMC) have extended the capabilities of Bayesian methods. Therefore, after studying direct Monte Carlo simulation methods, the course covers non-trivial methods of simulation such as Markov Chain Monte Carlo (MCMC), applying them to implement models such as stochastic volatility. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

This course explores algorithmic and numerical schemes used in practice for the pricing and hedging of financial derivative products. The focus of this course lies on data analysis. It covers such topics as: stochastic models with jumps, advanced simulation methods, optimization routines, and tree-based approaches. It also introduces machine learning concepts and methodologies, including cross validation, dimensionality reduction, random forests, neural networks, clustering, and support vector machines. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

This course provides a comprehensive and in-depth treatment of modern asset pricing theories. Extensive use is made of continuous time stochastic processes, stochastic calculus and optimal control. Particular emphasis will be placed on (i) stochastic calculus with jumps; (ii) asset pricing models with jumps; (iii) the Hamilton-Jacobi-Bellman equation and stochastic control; (iv) numerical methods for stochastic control problems in finance. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

This doctoral level class on corporate finance covers both theoretical and empirical work. Rather than explaining the underpinnings of basic corporate research (e.g., model/applications dealing with asymmetric information, agency problems, and capital market frictions), we go deeper in understanding "how to operationalize" research on concrete topics that are central to contemporary corporate finance, such as bankruptcy, capital structure, mergers and acquisitions, the firm boundaries, investment, and much more. The class also looks at the interface between corporate finance and other research areas, such as asset pricing and banking. The course is a blend of new approaches to modeling in corporate research (e.g., dynamic, structural models of financial policy that generate typically quantitative predictions) and new approaches to testing design (e.g., regression discontinuities and natural experiments). The goal is to expose the students to the "state-of-the-art" of research in corporate finance and prepare them to do research in corporate finance using new methods and tools. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)

For PhD students in the Mathematical Finance program. Registered by permission only.

PhD-level directed study in Mathematical Finance. 1, 2, or 3 cr. Application available on the Graduate Center website.

PhD-level directed study in Mathematical Finance. 1, 2, or 3 cr. Application available on the Graduate Center website.