Track 
Course Name 
Course ID 
Risk Management 
Financial Risk Analysis
This course focuses on the ways in which risks are quantified and managed by financial institutions by covering the following topics:
 Introduction to financial institutions, such as banks, insurance companies, mutual funds and hedge funds, and their risk management issues. (The credit crisis of 2007 will be covered in this part as well.)
 The market risk, including topics on interest risk, ValueatRisk, volatility, correlations and copulas.
 Credit risk, including default probability estimation, CVA, DVA and credit value at risk.
 (If time permits) Other topics, such as operational risk, liquidity risk, model risk, etc.

FIM/MA 549 
Database Applications in Industrial Engineering
Rapid application development (RAD) tools to design and implement databasebased applications including:
 SQL query language
 VBA in database application construction
 a standard RAD environment and how to access information in a database
 entity/attribute modeling of the database structure
 anomalies of database structures that create problems for applications
 modeling of application system’s functionality
 and integrating these tools together to design and implement engineering applications
 examples from manufacturing and production systems.

ISE 519 
Enterprise Risk Management
 Expose students to techniques all types of organizations are implementing to manage
the everincreasing portfolio of risks threatening the organization’s business model and strategic plan
 Begin with obtaining an understanding of the growing expectations being placed on boards of directors and senior executives for more effective oversight of risks
 Walk through the core elements of an ERM process entities use to identify, assess, manage, and monitor its most important risks to their business model

MBA 518 
Corporate Risk Management
 Fundamentals of corporate risk management from a strategic decisionmaking perspective
 Emphasis on how exposures to financial risks (foreign currency, credit, interest rate, etc.) affect the firm, and how risk exposures can be reengineered to enhance shareholder value
 Topics include the major sources of risk, the measurement of risk exposures, methods, and strategies for managing and controlling risk
 Introduce tools of the financial engineer–futures, options, swaps, and other derivatives

MBA 527 
Advanced Corporate Finance
 Introduction, TVM, Bond and Stock valuation
 Capital budgeting, Estimating incremental FCF, NPV
 Estimating cost of debt, beta, cost of equity
 Bond and Stock valuation (DDM)
 Introduction to WRDS. Capital Structure ‐ Ideal mode
 Capital Structure ‐ Taxes, bankruptcy costs
 Capital Structure – In Practice
 How Firms Raise External Capital
 FCF valuation in practice
 Leasing
 Mergers and Acquisitions
 Options ‐ Valuation, Real Options
 Derivatives valuation and risk management

MBA 521 
Data Science for Finance 
Database Applications in Industrial Engineering
Rapid application development (RAD) tools to design and implement databasebased applications, including different method and programming language:
 SQL query language
 Visual Basic for Applications in database application
 Standard RAD environment and how to access information in a database construction
 Entity/attribute modeling of the database structure
 Anomalies of database structures that create problems for applications
 Modeling of application system’s functionality
 Integrating these tools together to design and implement engineering applications

ISE 519 
Fundamentals of Linear Models and Regression
 Estimation and testing in full and nonfull rank linear models
 Normal theory distributional properties
 Least squares principle and the GaussMarkov theorem
 Estimability, analysis of variance and co variance in a unified manner
 Practical modelbuilding in linear regression including residual analysis, regression diagnostics, and variable selection
 Emphasis on use of the computer to apply methods with data sets

ST 503 
Experimental Statistics for Engineers II
 General statistical concepts and techniques useful to research workers in engineering, textiles, wood technology, etc
 Probability distributions, measurement of precision, simple and multiple regression
 Tests of significance, analysis of variance, enumeration data and experimental designs

ST 516 
Applied Bayesian Analysis
 Introduction to Bayesian concepts of statistical inference
 Bayesian learning; Markov chain Monte Carlo methods using existing software (SAS and OpenBUGS)
 Linear and hierarchical models
 model selection and diagnostics

ST 540 
Applied Time Series
 Exploratory analysis of time series
 Time domain methods, such as ARIMA models
 Frequency domain methods (periodogram, spectrum,…) analysis, filtering, and transfer functions
 Transfer function modeling in the time domain
 Further topics, such as long memory and conditional heteroscedasticity models, and nonparametric time series methods, as time permits

ST 590 
Data Mining with SAS Enterprise Miner
 This is a handson course using modeling techniques designed mostly for large observational studies
 Estimation topics include recursive splitting, ordinary and logistic regression, neural networks, and discriminant analysis
 Clustering and association analysis are covered under the topic “unsupervised learning,” and the use of training and validation data sets are emphasized
 Model evaluation alternatives to statistical significance include lift charts and receiver operating characteristic curves
 SAS Enterprise Miner is used in the demonstrations, and some knowledge of basic SAS programming is helpful

ST 562 
Statistical Programming I

ST 555 
Portfolio Management 
Introduction to Mathematical Programming
 A survey course in the theory and methods of mathematical programming to meet the needs of students from a variety of backgrounds
 A wide array of topics and applications in linear and nonlinear programming comprise the course
 The major prerequisite is familiarity with vector and matrix manipulations
 Some differential calculus is required for the discussion of nonlinear programming

OR(ISE) 504 
Linear Programming
Provide the fundamental understanding to the theory and algorithms of linear optimization. It involves mathematical analysis, theorem proving, algorithm design and numerical methods:
 Introduction to LP
 Geometric Interpretation of LP
 Simplex Method
 Duality and Sensitivity Analysis
 Interior Point Method
 Robust Optimization

OR(ISE) 505 
Algorithmic Methods in Nonlinear Programming
 Introduction to methods for obtaining approximate solutions to unconstrained and constrained minimization problems of moderate size
 Emphasis on geometrical interpretation and actual coordinate descent, steepest descent, Newton and quasiNewton methods
 Conjugate gradient search, gradient projection and penalty function methods for constrained problems
 Specialized problems and algorithms treated as time permits

OR 506 
Investment Theory and Practice
 Advanced topics in investments with a focus on underlying theory and practical application using real world data
 Stock valuation models
 Bond valuation
 Derivatives, portfolio performance evaluation
 Investment strategies, efficient market theory
 Other current issues in investment finance

MBA 523 
Equity Valuation
Advanced quantitative course on applied equity valuation.Students conduct stock valuation analysis which is then used to select stocks for the studentmanaged SunTrust MBA fund. Topics include:
 The investment decision making process
 Empirical evidence on securities returns
 Forecasting financial statements
 Industry and macroeconomic analysis
 Valuation models
 Portfolio performance evaluation and performance attribution

MBA 524 
Dynamic Systems and Multivariable Control I
 Introduction to modeling, analysis and control of linear discretetime and continuoustime dynamical systems
 State space representations and transfer methods
 Applications to biological, chemical, economic, electrical, mechanical and sociological systems

MA(OR,E) 531 
Database Applications in Industrial Engineering
Rapid application development (RAD) tools to design and implement databasebased applications, including different method and programming language:
 SQL query language
 Visual Basic for Applications in database application
 Standard RAD environment and how to access information in a database construction
 Entity/attribute modeling of the database structure
 Anomalies of database structures that create problems for applications
 Modeling of application system’s functionality
 Integrating these tools together to design and implement engineering applications

ISE 519 
Actuarial Science 
Microeconomics I & II
 Theory of consumer behavior
 Primaldual relationships in consumer theory including indirect utility functions and consumer expenditure functions
 Properties of consumer demand functions
 Consumer welfare measurement
 Longrun market equilibrium in a competitive market environment
 Market equilibrium with upward sloping input supply equations. The theory of monopoly
 General equilibrium
 Economics of information and uncertainty
 Game theory
 Mechanism design and social choice

ECG 701 & 702 
Introduction to Econometric Methods
 Introduction to principles of estimation of linear regression models, such as ordinary least squares and generalized least squares
 Extensions to time series and panel data
 Consideration of endogeneity and instrumental variables estimation
 Limited dependent variable and sample selection models
 Attention to implementation of econometric methods using a statistical package and microeconomic and macroeconomic data sets

ECG(ST) 750 
Econometric Methods
Discussion of important concepts in the asymptotic statistical analysis of vector process with application to the inference procedures based on the aforementioned estimation methods. Introduction to important econometric methods of estimation such as:
 Least Squares, instrumentatl Variables
 Maximum Likelihood, and Generalized Method of Moments
 Their application to the estimation of linear models for crosssectional ecomomic data

ECG(ST) 751 
Time Series Econometrics
 The characteristics of macroeconomic and financial time series data
 Discussion of stationarity and nonstationarity as they relate to economic time series
 Linear models for stationary economic time series: autoregressive moving average (ARMA) models; vector autoregressive (VAR) models
 Linear models for nonstationary data: deterministic and stochastic trends
 Methods for capturing volatility of financial time series such as autoregressive conditional heteroscedasticity (ARCH) models
 Generalized Method of Moments estimation of nonlinear dynamic models

ECG(ST) 752 
Microeconometrics
The characteristics of microeconomic data. Limited dependent variable models for crosssectional microeconomic data:
 Logit/probit models
 Tobit models
 Methods for accounting for sample selection
 Count data models
 Duration analysis
 Nonparametricmethods
 Panel data models
 Limited dependent variables and panel data analysis

ECG(ST) 753 
Probability and Stochastic Processes II
 Conditional expectation, Martingales, submartingales, supermartingales
 Doob’s decomposition, Doob’s inequality, Uniform integrability
 Convergence theorems, Optional stopping theorems
 Markov chains: Discretetime, examples of Markov chains (queueing, birthdeath, etc.) properties of Markov chains (recurrence, transient, etc.) and stationary measures
 Brownian motion: Probability spaces for continuoustime processes (E.g. “path space”), definition and some properties of Brownian motion and applications with Brownian motion models

MA(ST) 747 
Enterprise Risk Management
 Expose students to techniques all types of organizations are implementing to manage the everincreasing portfolio of risks threatening the organization’s business model and strategic plan
 Begin with obtaining an understanding of the growing expectations being placed on boards of directors and senior executives for more effective oversight of risks
 Walk through the core elements of an ERM process entities use to identify, assess, manage, and monitor its most important risks to their business model

MBA 518 
PhD Preparation 
Linear Programming
Provide the fundamental understanding to the theory and algorithms of linear optimization. It involves mathematical analysis, theorem proving, algorithm design and numerical methods:
 Introduction to LP
 Geometric Interpretation of LP
 Simplex Method
 Duality and Sensitivity Analysis
 Interior Point Method
 Robust Optimization

OR(ISE) 505 
Econometric Methods
Discussion of important concepts in the asymptotic statistical analysis of vector process with application to the inference procedures based on the aforementioned estimation methods. Introduction to important econometric methods of estimation such as:
 Least Squares, instrumentatl Variables
 Maximum Likelihood, and Generalized Method of Moments
 Their application to the estimation of linear models for crosssectional ecomomic data

ECG(ST) 751 
Time Series Econometrics
 The characteristics of macroeconomic and financial time series data
 Discussion of stationarity and nonstationarity as they relate to economic time series
 Linear models for stationary economic time series: autoregressive moving average (ARMA) models; vector autoregressive (VAR) models
 Linear models for nonstationary data: deterministic and stochastic trends
 Methods for capturing volatility of financial time series such as autoregressive conditional heteroscedasticity (ARCH) models
 Generalized Method of Moments estimation of nonlinear dynamic models

ECG(ST) 752 
Linear Transformations and Matrix Theory
 Vector spaces, linear transformations and matrices
 Orthogonality, orthogonal transformations with emphasis on rotations and reflections
 Matrix norms, projectors
 Least squares
 Generalized inverses
 Definite matrices and ingular values

MA 523 
Uncertainty Quantification for Physical Models
 Motivating applications and prototypical models
 Fundamental aspects of probability, random processes and statistics
 Representation of random inputs
 Parameter selection techniques
 Frequentist and Bayesian model calibration
 Uncertainty propagation in models
 Stochastic spectral methods and sparse grid techniques
 Prediction in the presence of model discrepancy
 Surrogate models
 Global sensitivity analysis

MA 540 
Probability and Stochastic Processes I
 Foundation of probability theory including random variables, conditioning, independence
 Limit theorems in the context of independent random variables/vectors
 Probability distributions and conditional expectations
 Characteristic functions, Gaussian processes, sums of independent random variables
 Laws of large numbers and central limit theorem

MA(ST) 546 
Applied Time Series Analysis
To be updated…

ST 730 
Bayesian Inference and Analysis
 Introduction to Bayesian inference
 Specifying prior distributions
 Conjugate priors, summarizing posterior information, predictive distributions, hierachical models, asymptotic consistency and asymptotic normality
 Markov Chain Monte Carlo (MCMC) methods and the use of exising software(e.g., WinBUGS)

ST 740 
Functional Analysis
Spectral Theory of Linear Operators in Normed Spaces:
 Spectral theory in finite dimensional normed spaces
 Resolvent and spectrum
 Spectral properties of bounded linear operators
 Compact linear operators and their spectral analysis
 Spectral properties of bounded selfadjoint linear operators
Semigroups of Bounded Linear Operators:
 Uniformly continuous semigroups of bounded linear operators
 Strongly continuous semigroups of bounded linear operators
 The HilleYosida Theorem, The Lumer Phillips Theorem, Infinitesimal generators of C0 semigroups
Unbounded Linear Operators in Hilbert Spaces:
 Hilbertadjoint operators
 Symmetric and selfadjoint linear operators
 Spectral properties of selfadjoint linear operators
 Closed linear operators

MA 791 