Course Descriptions

SPAN 7620 - Costumbrismo

SPAN 7650 - Realism and Naturalism: The Novel

SPAN 7660 - Generation of 1898

SPAN 7700 - Generation of 1927

SPAN 7710 - Literature and the Civil War

SPAN 7720 - Contemporary Theater

SPAN 7730 - Post-Civil War Fiction

SPAN 7740 - Modern Poetry

SPAN 7800 - Colonial Spanish American Literature

SPAN 7810 - Spanish American Modernismo

SPAN 7820 - Nineteenth-Century Spanish-American Literature

SPAN 7830 - Spanish-American Poetry

SPAN 7840 - Spanish-American Fiction

SPAN 7850 - Themes and Genres

SPAN 7860 - Regional Literature

SPAN 7870 - Short Story: Twentieth-Century Spanish America

SPAN 7880 - Novel: Twentieth-Century Spanish America

SPAN 7890 - Essay: Twentieth-Century Spanish America

SPAN 8210 - Practicum in Teaching College Spanish

SPAN 8500 - Seminars: Middle Ages and Early Renaissance

SPAN 8505 - Seminars: Middle Ages and Early Renaissance

SPAN 8510 - Seminars: Golden Age

SPAN 8515 - Seminars: Golden Age

SPAN 8520 - Seminars: Enlightenment to Romanticism

SPAN 8525 - Seminars: Enlightenment to Romanticism

SPAN 8530 - Seminars: Realism and the Generation of 1898

SPAN 8535 - Seminars: Realism and the Generation of 1898

SPAN 8540 - Seminars: Modern Spanish Literature

SPAN 8545 - Seminars: Modern Spanish Literature

SPAN 8550 - Seminars: Spanish America: Colonial Period to 1900

SPAN 8555 - Seminars: Spanish America: Colonial Period to 1900

SPAN 8560 - Seminars: Spanish America: Modern Period

SPAN 8565 - Seminars: Spanish America: Modern Period

SPAN 8995 - Guided Research

SPAN 8998 - Non-Topical Research, Preparation for Research

SPAN 8999 - Non-Topical Research

SPAN 9995 - Guided Research

SPAN 9998 - Non-Topical Research, Preparation for Doctoral Research

SPAN 9999 - Non-Topical Research

STAT 5000 - Introduction to Applied Statistics

Introduces estimation and hypothesis testing in applied statistics, especially the medical sciences. Measurement issues, measures of central tendency and dispersion, probability, discrete probability distributions (binomial and Poisson), continuous probability distributions (normal, t, chi-square, and F), and one- and two-sample inference, power and sample size calculations, introduction to non-parametric methods, one-way ANOVA and multiple comparisons.
Prerequisite: Instructor permission; corequisite: STAT 5980.



Credits: 3

STAT 5010 - Statistical Computing and Graphics

Introduces statistical computing using S-PLUS. Topics include descriptive statistics for continuous and categorical variables, methods for handling missing data, basics of graphical perception, graphical displays, exploratory data analysis, the simultaneous display of multiple variables. Students should be experienced with basic text-editing and file manipulation on either a PC or a UNIX system, and with either a programming language (e.g. BASIC) or a spreadsheet program (e.g. MINITAB or EXCEL). Credit earned in this course cannot be applied toward a graduate degree in statistics.
Prerequisite: STAT 1100 or 1120, and graduate standing or instructor permission. Students who have received credit for STAT 3010 may not take STAT 5010 for credit.



Credits: 3

STAT 5020 - Mathematical Statistics

A calculus based introduction to the principles of statistical inference. Topics include sampling theory, point estimation, confidence intervals, hypothesis testing. Additional topics such as nonparametric methods or Bayesian statistics. May not be used for graduate degrees in Statistics. May not be taken if credit has been received for STAT 3120.
Prerequisites: MATH 3100 or 5100 or consent of instructor.



Credits: 3

STAT 5120 - Applied Linear Models

Linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, autocorrelation in time series data, polynomial regression, and nonlinear regression.
Prerequisite: MATH 3120 or 5100, or instructor permission; corequisite: STAT 5980.



Credits: 3

STAT 5130 - Applied Multivariate Statistics

Topics include matrix algebra, random sampling, multivariate normal distributions, multivariate regression, MANOVA, principal components, factor analysis, discriminant analysis. Statistical software, such as SAS or S-PLUS, will be utilized.
Prerequisite: MATH 3351 and 3120 or 5100, or instructor permission; corequisite: STAT 5980.



Credits: 3

STAT 5140 - Survival Analysis and Reliability Theory

Topics include lifetime distributions, hazard functions, competing-risks, proportional hazards, censored data, accelerated-life models, Kaplan-Meier estimator, stochastic models, renewal processes, and Bayesian methods for lifetime and reliability data analysis.
Prerequisite: MATH 3120 or 5100, or instructor permission; corequisite: STAT 5980.



Credits: 3

STAT 5150 - Actuarial Statistics

Covers the main topics required by students preparing for the examinations in Actuarial Statistics, set by the American Society of Actuaries. Topics include life tables, life insurance and annuities, survival distributions, net premiums and premium reserves, multiple life functions and decrement models, valuation of pension plans, insurance models, and benefits and dividends.
Prerequisite: MATH 3120 or 5100, or instructor permission.



Credits: 3

STAT 5160 - Experimental Design

Introduction to the basic concepts in experimental design, analysis of variance, multiple comparison tests, completely randomized design, general linear model approach to ANOVA, randomized block designs, Latin square and related designs, completely randomized factorial design with two or more treatments, hierarchical designs, split-plot and confounded factorial designs, and analysis of covariance.
Prerequisite: MATH 3120 or 5100, or instructor permission; corequisite: STAT 5980.



Credits: 3

STAT 5170 - Applied Time Series

Studies the basic time series models in both the time domain (ARMA models) and the frequency domain (spectral models), emphasizing application to real data sets.
Prerequisite: MATH 3120 or 5100, or instructor permission; corequisite: STAT 5980.



Credits: 3

STAT 5180 - Design and Analysis of Sample Surveys

Discussion of the main designs and estimation techniques used in sample surveys: simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation, and non response and other non sampling errors. Except for students in their first semester of graduate study, students in the graduate program in Statistics should enroll in STAT 7180.
Prerequisites: STAT 3120.



Credits: 3

STAT 5190 - Introduction to Mathematical Statistics

Studies statistical distribution theory, moments, transformations of random variables, point estimation, hypothesis testing, and confidence regions.
Prerequisite: MATH 3120 or 5100, or instructor permission.



Credits: 3

STAT 5250 - Longitudinal Data Analysis

Data structure and basic concepts of longitudinal data, modeling of mean and covariance, estimation and inference in the marginal models, linear models and linear mixed effects models and if time allows, generalized linear models and generalized linear models.
Prerequisites: STAT 5120 or STAT 6120.



Credits: 3

STAT 5260 - Categorical Data Analysis

The course covers topics in categorical data, including contingency tables, generalized linear models, logistic regression, and logit and loglinear models.
Prerequisite: STAT 3120.



Credits: 3

STAT 5265 - Investment Science I

The course will cover a broad range of topics, with the overall theme being the quantitative modeling of asset allocation and portfolio theory. It begins with deterministic cash flows (interest theory, fixed-income securities), the modeling of interest rates (term structure of interest rates), stochastic cash flows, mean-variance portfolio theory, capital asset pricing model, and the utility theory basis for financial modeling.
Prerequisite: MATH 3100.



Credits: 3

STAT 5266 - Investment Science II

This course is a follow-up to Investment Science I (Stat 5265). It begins with models for derivative securities, including asset dynamics, options and interest rate derivatives. The remaining portion of the course then combines all of the ideas from the two courses to formulate strategies of optimal portfolio growth and a general theory of investment evaluation.
Prerequisite: MATH 3100, STAT 5265.



Credits: 3

STAT 5310 - Clinical Trials Methodology

Studies experimental designs for randomized clinical trials, sources of bias in clinical studies, informed consent, logistics, and interim monitoring procedures (group sequential and Bayesian methods).
Prerequisite: A basic statistics course (MATH 3120/5100) or instructor permission.



Credits: 3

STAT 5330 - Data Mining

This course introduces a plethora of methods in data mining through the statistical point of view. Topics include linear regression and classification, nonparametric smoothing, decision tree, support vector machine, cluster analysis and principal components analysis. Basic knowledge of R is required.
Prerequisites: Concurrent enrollment in STAT 5120 or consent of instructor.



Credits: 3

STAT 5340 - Bootstrap and Other Resampling Methods

This course introduces the basic ideas of resampling methods, from jackknife and the classic bootstrap due to Efron to advanced bootstrap techniques such as the estimating function bootstrap and the Markov chain marginal bootstrap.



Credits: 3

STAT 5430 - Statistical Computing with SAS

The course covers database management, programming, elementary statistical analysis, and report generation in SAS. Topics include: managing SAS Data Sets; DATA-step programming; data summarization and reporting using PROCs PRINT, MEANS, FREQ, UNIVARIATE, CORR, and REG; elementary graphics; introductions to the Output Delivery System, the SAS Macro language, PROC IML, and PROC SQL.
Prerequisites: Introductory statistics course.



Credits: 3

STAT 5440 - Bayesian Analysis

This introductory, graduate level course will provide an introduction Bayesian methods with emphasis on modeling and applications. The topics to be covered include methods for forming prior distributions such as conjugate and noninformative priors, derivation of posterior and predictive distributions and their moments, and development of Bayesian models including linear regression, generalized linear models and hierarchical models.
Prerequisites: At least one semester of mathematical statistics (STAT 3120 or 5190) and one course in linear models (STAT 5120 or equivalent), or instructor permission.



Credits: 3

STAT 5559 - New Course in Statistics

STAT 5980 - Applied Statistics Laboratory

This course, the laboratory component of the department’s applied statistics program, deals with the use of computer packages in data analysis. Enrollment in STAT 5980 is required for all students in the department’s 5000-level applied statistics courses (STAT 5010, 5120, 5130, 5140, 5160, 5170, 5200). STAT 5980 may be repeated for credit provided that a student is enrolled in at least one of these 5000-level applied courses; however, no more than one unit of STAT 5980 may be taken in any semester.
Corequisite: 5000-level STAT applied statistics course.



Credits: 1

STAT 5999 - Topics in Statistics

STAT 6120 - Linear Models

Linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, autocorrelation in time series data, polynomial regression, and nonlinear regression.
Prerequisite: MATH 3510, enrollment in graduate program in Statistics or instructor permission
corequisite: STAT 5980.



Credits: 3

STAT 7110 - Foundations of Statistics

Introduction to the concepts of statistics via the establishment of fundamental principles which are then applied to practical problems. Such statistical principles as those of sufficiency, ancillarity, conditionality, and likelihood will be discussed.
Prerequisite: STAT 5190 or instructor permission.



Credits: 3

STAT 7120 - Statistical Inference

A rigorous mathematical development of the principles of statistics. Covers point and interval estimation, hypothesis testing, asymtotic theory, Bayesian statistics, and decision theory from a unified perspective.
Prerequisite: STAT 7110 or instructor permission.



Credits: 3

STAT 7130 - Generalized Linear Models

Includes the origins of generalized linear models, classical linear models, probit analysis, logit models for proportions, log-linear models for counts, inverse polynomial models, binary data, polytomous data, quasi-likelihood models, and models for survival data.
Prerequisite: STAT 5120 and 5190, or instructor permission.



Credits: 3

STAT 7140 - Multivariate Statistical Analysis

Includes multivariate normal distributions, maximum likelihood inference, invariance theory, sample correlation coefficients, Hotelling’s T2 statistic, Wishart distributions, discriminant analysis, and MANOVA.
Prerequisite: STAT 5130 and 5190, or instructor permission.



Credits: 3

STAT 7150 - Non-Parametric Statistical Analysis

Includes order statistics, distribution-free statistics, U-statistics, rank tests and estimates, asymtotic efficiency, Bahadur efficiency, M-estimates, one- and two-way layouts, multivariate location models, rank correlation, and linear models.
Prerequisite: STAT 5190 and one of STAT 5120, 5130, 5140, 5160, 5170; or instructor permission.



Credits: 3

STAT 7180 - Sample Surveys

Discussion of the main designs and estimation techniques used in sample surveys: simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation, and non response and other non sampling errors. Except for students in their first semester of graduate study, students in the graduate program in Statistics should enroll in STAT 7180.
Prerequisites: STAT 5190.



Credits: 3

STAT 7190 - Statistical Computing

Studies computational methods for multiple linear regression, unconstrained optimization and non-linear regression, model-fitting based on Lp norms, and robust estimation.
Prerequisite: STAT 5120 and 5180, or instructor permission.



Credits: 3

STAT 7200 - Advanced Probability Theory for Applied Scientists

The course will emphasize those techniques which are important for the applied statistician: various forms of convergence for random variables, central limit theorems, asymptotics for a transformation of a sequence of random variables, and an introduction to martingales.
Prerequisite: MATH 5310 or instructor permission.



Credits: 3

STAT 7210 - Advanced Linear Models

Review of matrix theory (various types of generalized inverses and their properties). Theory and analysis of fixed effects linear models. Estimation of variance components in random and mixed effects linear models. Various methods of estimation of variance components such as: Henderson’s three methods, MLE, RMLE, MINQUE (and its modifications). Theory and analysis of random and mixed effects models.
Prerequisite: MATH 3510, STAT 5120, 5130, 5190, or instructor permission.



Credits: 3

STAT 7220 - Martingale Theory

An introduction to martingale theory and stochastic differential equations with applications to survival analysis and sequential clinical trials.
Prerequisites: STAT 7200 or MATH 7360.



Credits: 3

STAT 7310 - Applied Biostatistical Data Analysis

Includes modern computer-intensive methods of data analysis, including splines and other methods of nonparametric regression, bootstrap, techniques for handling missing values and data reduction, nonlinear regression, graphical techniques, and penalized maximum likelihood estimation.
Prerequisite: STAT 5120 and 5130, or instructor permission.



Credits: 1 to 4

STAT 7559 - Applied Biostatistical Data Analysis

The objective is to help students integrate and apply statistical methods learned in other courses to real data from medial research. Students will learn to identifiy the scientific objectives of a study, and develop and implement appropriate strategies. They will present their intermediate and final results in both oral and written forms. This course will prepare the students for a future career as applied statisticians.



Credits: 1 to 4

STAT 7950 - Statistical Bioinformatics in Medicine

Provides an introduction to bioinformatics and discusses important topics in computational biology in medicine, particularly based on modern statistical computing approaches. Reviews state-of-the-art high-throughput biotechnologies, their applications in medicine, and analysis techniques. Requires active student participation in various discussions on the current topics in biotechnology and bioinformatics.



Credits: 3

STAT 7995 - Statistical Consulting

Introduces the practice of statistical consultation. A combination of formal lectures, meetings with clients of the statistical consulting service, and sessions in the statistical computing laboratory.
Prerequisite: Current registration in the statistics graduate program, or instructor permission.



Credits: 1 to 3

STAT 8120 - Topics in Statistics

STAT 8170 - Advanced Time Series

Introduces stationary stochastic processes, related limit theorems, and spectral representations. Includes an asymtotic theory for estimation in both the time and frequency domains.
Prerequisite: MATH 7360, STAT 5170, or instructor permission.



Credits: 3

STAT 8320 - Topics in Biostatistics

STAT 9120 - Statistics Seminar

Advanced graduate seminar in current research topics. Offerings in each semester are determined by student and faculty research interests.



Credits: 3

STAT 9993 - Directed Reading

STAT 9998 - Non-Topical Research, Preparation for Doctoral Research

STAT 9999 - Non-Topical Research

SYS 5044 - Economics of Engineering

This course is an introduction to the theory of the industrial organization (from a game-theoretic perspective) and its applications to industries with strong engineering content (electricity, telecommunications, software and hardware, etc.). Topics include: congestion pricing in networks, pricing and efficiency in electricity markets, planned obsolescence in software development, “networks” effects and the dynamics of technology adoption.
Prerequisite: ECON 2010, APMA 3100 or 3110.



Credits: 3

SYS 5581 - Selected Topics in Systems Engineering

Detailed study of a selected topic, determined by the current interest of faculty and students. Offered as required.



Credits: 3

SYS 6001 - Introduction to Systems Engineering

An integrated introduction to systems methodology, design, and management. An overview of systems engineering as a professional and intellectual discipline, and its relation to other disciplines, such as operations research, management science, and economics. An introduction to selected techniques in systems and decision sciences, including mathematical modeling, decision analysis, risk analysis, and simulation modeling. Elements of systems management, including decision styles, human information processing, organizational decision processes, and information system design for planning and decision support. Emphasizes relating theory to practice via written analyses and oral presentations of individual and group case studies.
Prerequisite: Admission to the graduate program.



Credits: 3

SYS 6002 - Systems Integration

Provides an introduction to the problems encountered when integrating large systems, and also presents a selection of specific technologies and methodologies used to address these problems. Includes actual case-studies to demonstrate systems integration problems and solutions. A term project is used to provide students with the opportunity to apply techniques for dealing with systems integration.
Prerequisite: SYS 6001 or instructor permission.



Credits: 3

SYS 6003 - Optimization I

This course is an introduction to theory and application of mathematical optimization. The goal of this course is to endow the student with a) a solid understanding of the subject’s theoretical foundation and b) the ability to apply mathematical programming techniques in the context of diverse engineering problems. Topics to be covered include a review of convex analysis (separation and support of sets, application to linear programming), convex programming (characterization of optimality, generalizations), Karush-Kuhn-Tucker conditions, constraint qualification and Lagrangian duality. The course closes with a brief introduction to dynamic optimization in discrete time.
Prerequisite: Two years of college mathematics, including linear algebra, and the ability to write computer programs.



Credits: 3

SYS 6005 - Stochastic Systems I

Covers basic stochastic processes with emphasis on model building and probabilistic reasoning. The approach is non-measure theoretic but otherwise rigorous. Topics include a review of elementary probability theory with particular attention to conditional expectations; Markov chains; optimal stopping; renewal theory and the Poisson process; martingales. Applications are considered in reliability theory, inventory theory, and queuing systems.
Prerequisite: APMA 3100, 3120, or equivalent background in applied probability and statistics.



Credits: 3

SYS 6009 - The Art and Science of Systems Modeling

Focuses on learning and practicing the art and science of systems modeling through diverse case studies. Topics span the modeling of discrete and continuous, static and dynamic, linear and non-linear, and deterministic and probabilistic systems. Two major dimensions of systems modeling are discussed and their efficacy is demonstrated: the building blocks of mathematical models and the centrality of the state variables in systems modeling, including: state variables, decision variables, random variables, exogenous variables, inputs and outputs, objective functions, and constraints; and effective tools in systems modeling, including multiobjective models, influence diagrams, event trees, systems identification and parameter estimation, hierarchical holographic modeling, and dynamic programming.



Credits: 3

SYS 6012 - Dynamic Systems

Introduces modeling, analysis, and control of dynamic systems, using ordinary differential and difference equations. Emphasizes the properties of mathematical representations of systems, the methods used to analyze mathematical models, and the translation of concrete situations into appropriate mathematical forms. Primary coverage includes ordinary linear differential and difference equation models, transform methods and concepts from classical control theory, state-variable methods and concepts from modern control theory, and continuous system simulation. Applications are drawn from social, economic, managerial, and physical systems.
Cross-listed as MAE 6620.
Prerequisite: APMA 2130 or equivalent.



Credits: 3

SYS 6013 - Applied Multivariate Statistics

The theory and applications of primary methods for multivariate data analysis, such as MANOVA, principal components, factor analysis, canonical correlation, and discriminant analysis, are covered in this course. Students are expected to be familiar with at least one statistical software package and with concepts of linear algebra. It is cross-listed as STAT 5130.
Prerequisites: SYS 6018, SYS 4021/6021, or STAT 5120 (or their equivalents); courses in linear algebra and univariate statistics; or instructor permission.



Credits: 3

SYS 6014 - Decision Analysis

Principles and procedures of decision-making under uncertainty and with multiple objectives. Topics include representation of decision situations as decision trees, influence diagrams, and stochastic dynamic programming models; Bayesian decision analysis, subjective probability, utility theory, optimal decision procedures, value of information, multiobjective decision analysis, and group decision making.
Prerequisite: SYS 6003, 6005, or equivalent.



Credits: 3

SYS 6016 - Machine Learning

A graduate-level course on machine learning techniques and applications with emphasis on their application to systems engineering. Topics include: Bayesian learning, evolutionary algorithms, instance-based learning, reinforcement learning, and neural networks. Students are required to have sufficient computational background to complete several substantive programming assignments.
Prerequisite: A course covering statistical techniques such as regression.
Co-Listed with CS 6316.



Credits: 3

SYS 6018 - Data Mining

Data mining describes approaches to turning data into information. Rather than the more typical deductive strategy of building models using known principles, data mining uses inductive approaches to discover the appropriate models. These models describe a relationship between a system’s response and a set of factors or predictor variables. Data mining in this context provides a formal basis for machine learning and knowledge discovery. This course investigates the construction of empirical models from data mining for systems with both discrete and continuous valued responses. It covers both estimation and classification, and explores both practical and theoretical aspects of data mining.
Prerequisite: SYS 6021, SYS 4021, or STAT 5120.



Credits: 3

SYS 6021 - Linear Statistical Models

This course shows how to use linear statistical models for analysis in engineering and science. The course emphasizes the use of regression models for description, prediction, and control in a variety of applications. Building on multiple regression, the course also covers principal component analysis, analysis of variance and covariance, logistic regression, time series methods, and clustering. Course lectures concentrate on theory and practice.



Credits: 3

SYS 6023 - Cognitive Systems Engineering

Introduces the field of cognitive systems engineering, which seeks to characterize and support human-systems integration in complex systems environments. Covers key aspects of cognitive human factors in the design of information support systems. Reviews human performance (memory, learning, problem-solving, expertise and human error); characterizes human performance in complex, socio-technical systems, including naturalistic decision making and team performance; reviews different types of decision support systems, with a particular focus on representation aiding systems; and covers the human-centered design process (task analysis, knowledge acquisition methods, product concept, functional requirements, prototype, design, and testing).



Credits: 3

SYS 6026 - Quantitative Models of Human Perceptual Information Processing

An introduction to the measurement and modeling of human perceptual information processing, with approaches from neurophysiology to psychophysics, for the purposes of system design. Measurement includes classical psychophysics, EEG field potentials, and single-neuron recordings. Modeling includes signal detection theory, neuronal models (leaky integrate-and-fire, Hodgkin-Huxley, and models utilizing regression, probability, and ODEs).
Prerequisities: Graduate standing in Systems and Information Engineering; background courses in ordinary differential equations, statistics and probability; or consent of instructor.



Credits: 3

SYS 6034 - Discrete-Event Stochastic Simulation

A first graduate course covering the theory and practice of discrete-event stochastic simulation. Coverage includes Monte Carlo methods and spreadsheet applications, generating random numbers and variates, specifying input probability distributions, discrete-event simulation logic and computational issues, review of basic queueing theory, analysis of correlated output sequences, model verification and validation, experiment design and comparison of simulated systems, and simulation optimization. Emphasis includes state-of-the-art simulation programming languages with animation on personal computers. Applications address operations in manufacturing, distribution, transportation, communication, computer, health care, and service systems.
Prerequisite: SYS 6005 or equivalent background in probability, statistics, and stochastic processes.



Credits: 3