Graduate Courses
The faculty has approval to offer the following courses in the academic years 2013–2014 and 2014–2015; however, not all courses are taught each semester or summer session. Students should consult the Course Schedule to determine which courses and topics will be offered during a particular semester or summer session. The Course Schedule may also reflect changes made to the course inventory after the publication of this catalog.
The following courses are offered through the Division of Statistics and Scientific Computation. These courses are taught by division faculty and associated faculty members. Statistics and scientific computation courses are open to University graduate students in any academic program. Additional information about the division’s courses and its programs is available at http://ssc.utexas.edu/ . Not all courses may be applied to the Master of Science in Statistics degree requirements.
Statistics and Scientific Computation: SSC
SSC 380C. Statistical Methods I.
Introduction to the fundamental concepts and methods of statistics. Includes descriptive statistics, sampling distributions, confidence intervals, and hypothesis testing. May include simple and multiple linear regression, analysis of variance, and categorical analysis. Use of statistical software is emphasized. Three lecture hours a week for one semester. Prerequisite: Graduate standing.
SSC 380D. Statistical Methods II.
Continuation of Statistics and Scientific Computation 380C. Surveys advanced statistical modeling and may include random and mixed effects models, time series analysis, survival analysis, Bayesian methods, and multivariate analysis of variance. Use of statistical software is emphasized. Three lecture hours a week for one semester. Prerequisite: Graduate standing, and Statistics and Scientific Computation 380C or the equivalent.
SSC 381. Mathematical Methods for Statistical Analysis.
Introduction to mathematical concepts and methods essential for multivariate statistical analysis. Areas may include basic matrix algebra, eigenvalues and eigenvectors, quadratic forms, vector and matrix differentiation, unconstrained optimization, constrained optimization, and applications in multivariate statistical analysis. Three lecture hours a week for one semester. Prerequisite: Graduate standing and a course in statistics.
SSC 382. Introduction to Probability and Statistics.
Expectation and variance of random variables, conditional probability and independence, sampling distributions, point estimation, confidence intervals, hypothesis tests, and other topics. Three lecture hours a week for one semester. Prerequisite: Graduate standing, and Mathematics 408D or 408L.
SSC 383C. Statistical Modeling I.
An introduction to core applied statistical modeling ideas from a probabilistic, Bayesian perspective. Topics include exploratory data analysis, programming in R, Bayesian probability models, an introduction to the Gibbs sampler, applied regression analysis, and hierarchical models. Three lecture hours a week for one semester. Prerequisite: Graduate standing.
SSC 383D. Statistical Modeling II.
Use of structured, probabilistic models that incorporate multiple layers of uncertainty to describe real-world systems. Topics include multivariate normal distribution, mixture models, nonparametric Bayesian analysis, advanced hierarchical models and latent-variable models, generalized linear models, and advanced topics in linear and nonlinear regression. Three lecture hours per week for one semester. Prerequisite: Graduate standing; Economics 392M (Topic 19: Probability and Statistics), Statistics and Scientific Computation 384, or the equivalent; and Statistics and Scientific Computation 383C.
SSC 183K. Data Analysis Applications.
Introduction to the use of statistical or mathematical applications for data analysis. Two lecture hours a week for eight weeks. Offered on the credit/no credit basis only. May be repeated for credit when the topics vary. Offered on the credit/no credit basis only. Prerequisite: Graduate standing.
Topic 1: SPSS Software. Offered on the credit/no credit basis only.
Topic 2: SAS Software. Offered on the credit/no credit basis only.
Topic 3: Stata Software. Offered on the credit/no credit basis only.
Topic 4: The R Software Environment. Offered on the credit/no credit basis only.
SSC 384. Topics in Statistics and Probability.
Concepts of probability and mathematical statistics with applications in data analysis and research. Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing; and Mathematics 362K and 378K, Statistics and Scientific Computation 382, or consent of instructor.
Topic 1: Applied Probability. Basic probability theory, combinatorial analysis of random phenomena, conditional probability and independence, parametric families of distributions, expectation, distribution of functions of random variables, and limit theorems. May be repeated for credit when the topics vary.
Topic 2: Mathematical Statistics I. Same as Computational Science, Engineering, and Mathematics 384R and Mathematics 384C. The general theory of mathematical statistics. Includes distributions of functions of random variables, properties of a random sample, principles of data reduction, an overview of hierarchical models, decision theory, Bayesian statistics, and theoretical results relevant to point estimation, interval estimation, and hypothesis testing. Only one of the following may be counted: Computational and Applied Mathematics 384R, Computational Science, Engineering, and Mathematics 384R, Mathematics 384C, Statistics and Scientific Computation 384 (Topic 2). May be repeated for credit when the topics vary.
Topic 3: Mathematical Statistics II. Same as Computational Science, Engineering, and Mathematics 384S and Mathematics 384D. Continuation of Computational Science, Engineering, and Mathematics 384R and Statistics and Scientific Computation 384 (Topic 2). Only one of the following may be counted: Computational and Applied Mathematics 384S, Computational Science, Engineering, and Mathematics 384S, Mathematics 384D, Statistics and Scientific Computation 384 (Topic 3). May be repeated for credit when the topics vary. Prerequisite: Graduate standing; Computational Science, Engineering, and Mathematics 384R (or Computational and Applied Mathematics 384R), Mathematics 384C, or Statistics and Scientific Computation 384 (Topic 2: Mathematical Statistics I); and Mathematics 362K and 378K, Statistics and Scientific Computation 382, or consent of instructor.
Topic 4: Regression Analysis. Same as Computational Science, Engineering, and Mathematics 384T and Mathematics 384G. Simple and multiple linear regression, inference in regression, prediction of new observations, diagnosis and remedial measures, transformations, and model building. Emphasis on both understanding the theory and applying theory to analyze data. Only one of the following may be counted: Computational and Applied Mathematics 384T, Computational Science, Engineering, and Mathematics 384T, Mathematics 384G, Statistics and Scientific Computation 384 (Topic 4). May be repeated for credit when the topics vary.
Topic 6: Design and Analysis of Experiments. Same as Computational Science, Engineering, and Mathematics 384U and Mathematics 384E. Design and analysis of experiments, including one-way and two-way layouts; components of variance; factorial experiments; balanced incomplete block designs; crossed and nested classifications; fixed, random, and mixed models; and split plot designs. Only one of the following may be counted: Computational and Applied Mathematics 384U, Computational Science, Engineering, and Mathematics 384U, Mathematics 384E, Statistics and Scientific Computation 384 (Topic 6). May be repeated for credit when the topics vary.
Topic 7: Bayesian Statistical Methods. Fundamentals of Bayesian inference in single-parameter and multi-parameter models for inference and decision making, including simulation of posterior distributions, Markov chain Monte Carlo methods, hierarchical models, and empirical Bayes models. May be repeated for credit when the topics vary.
Topic 8: Time Series Analysis. Introduction to statistical time series analysis. Includes autoregressive integrated moving average (ARIMA) and more general models, forecasting, spectral analysis, time domain regression, model identification, estimation of parameters, and diagnostic checking. May be repeated for credit when the topics vary. Additional prerequisite: Mathematics 384D.
Topic 9: Computational Statistics. Modern, computation-intensive statistical methods, including simulation, optimization methods, Monte Carlo integration, maximum likelihood estimation and expectation-maximization parameter estimation, Markov chain Monte Carlo methods, resampling methods, and nonparametric density estimation. May be repeated for credit when the topics vary.
Topic 10: Stochastic Processes. Concepts and techniques of stochastic processes, with emphasis on the nature of change of variables with respect to time. Includes characterization, structural properties, and inference. May be repeated for credit when the topics vary.
SSC 385. Topics in Applied Statistics.
Theories, models, and methods for the analysis of quantitative data. Three lecture hours a week for one semester. With consent of the graduate adviser, may be repeated for credit when the topics vary. Prerequisite: Graduate standing; and Statistics and Scientific Computation 380C, 382, or consent of instructor.
Topic 1: Experimental Design. Principles, construction, and analysis of experimental designs. Includes one-way classification, randomized blocks, Latin squares, factorial and nested designs, fixed and random effects, multiple comparisons, and analysis of covariance.
Topic 2: Applied Regression. Simple and multiple linear regression, residual analysis, transformations, building models with real data, and testing models. Additional prerequisite: Statistics and Scientific Computation 385 (Topic 1) or consent of instructor.
Topic 3: Applied Multivariate Methods. Introduction to the analysis of multivariate data as applied to examples from the social sciences. Includes multivariate linear models, principal components and factor analysis, discriminant analysis, clustering, and canonical correlation. Additional prerequisite: Statistics and Scientific Computation 385 (Topic 2) or the equivalent.
Topic 4: Analysis of Categorical Data. Methods for analyzing categorical data. Includes categorical explanatory variables within the general linear model, models of association among categorical variables, and models in which the response variable is categorical or is a count. Emphasis on logical similarities across methods.
Topic 5: Structural Equation Modeling. Introduction to the basic concepts, methods, and computing tools used in structural equation modeling. Designed to help students develop a working familiarity with some common statistical procedures and their application through the use of statistical software. Additional prerequisite: Statistics and Scientific Computation 385 (Topic 2) or the equivalent or consent of instructor.
Topic 6: Hierarchical Linear Models. Introduction to multilevel data structures, model building and testing, effect size, fixed and random effects, missing data and model assumptions, hierarchical linear modeling (HLM) logistics, statistical power, and design planning. Additional prerequisite: Statistics and Scientific Computation 385 (Topic 2) or the equivalent or consent of instructor.
Topic 7: Survey Sampling and Methodology. Survey planning, execution, and analysis. Includes the principles of survey research, including sampling and measurement; questionnaire construction and distribution; response effects; validity and reliability; scaling data sources; and data reduction and analysis.
Topic 8: Introduction to Bayesian Methods. A practical introduction to Bayesian statistical interference, with an emphasis on applications in behavioral and measurement research. Examines how Bayesian statistical inference differs from classical inference in the context of simple statistical procedures and models, such as hypothesis testing, analysis of variance (ANOVA), and regression. Additional prerequisite: Statistics and Scientific Computation 385 (Topic 2) or the equivalent or consent of instructor.
Topic 9: Longitudinal Data Analysis. Applications of models to data collected at successive points in time. Includes latent growth curve models, models for nonlinear growth, discrete-time and continuous-time event history models, multilevel models for change, random coefficient models, and applications of models to event-occurrence data.
Topic 10: Modern Statistical Methods. Introduction to conducting statistical analysis using modern resampling methods, including bootstrapping and Monte Carlo simulation. Emphasis on theoretical understanding and application.
Topic 11: Applied Mathematical Statistics. Designed for doctoral students who plan to use statistical methods in their research but do not require a highly mathematical investigation of the subject. Introduction to the basic concepts of probability and mathematical statistics. Includes probability distributions and estimation theory and hypothesis testing techniques. Additional prerequisite: A calculus course covering integration and differentiation.
Topic 12: Meta-Analysis. Introduction to the statistics used to synthesize results from a set of studies. May include calculation of different effect sizes, calculating pooled estimates using fixed and random effects models, testing moderating variables using fixed and mixed effects models, testing heterogeneity of effect sizes, and assessing and correcting publication bias. Additional prerequisite: Statistics and Scientific Computation 385 (Topic 2) or the equivalent.
Topic 13: Factor Analysis. Introduction to exploratory and confirmatory factor analysis. May include review of matrix algebra and vector geometry, principal components and principal axis factoring, and factor rotation methods, as well as single-factor and multiple-factor multisample models, multitrait-multimethod technique, and latent means modeling. Emphasis on critiquing current research. Additional prerequisite: Statistics and Scientific Computation 385 (Topic 2) or the equivalent or consent of instructor.
Topic 14: Maximum-Likelihood Statistics. Introduction to the likelihood theory of statistical inference. Includes probability distributions, estimation theory, and applications of maximum-likelihood estimation (MLE) to models with categorical or limited dependent variables, even count models, event history models, models for time-series cross-section data, and models for hierarchical data.
Topic 15: Survival Analysis and Duration Modeling. Focuses on the statistical methods related to the analysis of survival or of time to event data. Emphasis on practical applications in medicine, biology, economics, criminology, sociology, and engineering. May include Kaplan-Meier estimators, semiparametric and parametric regression models, model development, and model adequacy assessment.
SSC 386C. Probabilistic Graphical Models.
An introduction to statistical learning methods, exploring both the computational and statistical aspects of data analysis. Topics include numerical linear algebra, convex optimization techniques, basics of stochastic simulation, nonparametric methods, kernel methods, graphical models, decision trees, and data resampling. Three lecture hours a week for one semester. Prerequisite: Graduate standing.
SSC 386D. Monte Carlo Methods in Statistics.
Stochastic simulation for Bayesian inference, designed to develop an understanding of Markov chair Monte Carlo methods and their underlying theoretical framework. Topics include Markov chains, Monte Carlo integration, Gibbs sampler, Metropolis-Hastings algorithms, slice sampling, and sequential Monte Carlo. Three lecture hours a week for one semester. Prerequisite: Graduate standing; and Economics 392M (Topic 19: Probability and Statistics), Statistics and Scientific Computation 384, or the equivalent.
SSC 387. Linear Models.
An exploration of practical applications of the projection approach to linear models, building from a review of essential linear algebra concepts to the theory of linear models from a projection-based perspective. Introduction to Bayesian ideas. Additional topics include analysis of variance, generalized linear models, and variable selection techniques. Three lecture hours a week for one semester. Prerequisite: Graduate standing; Economics 392M (Topic 19: Probability and Statistics), Statistics and Scientific Computation 384, or the equivalent; and basic coding skills in R, Matlab, or Stata.
SSC 388. Consulting Seminar.
Supervised experience in applying statistical or mathematical methods to real problems. Includes participation in weekly consulting sessions, directed readings in the statistical literature, the ethics of research and consulting, and report writing and presentations. The equivalent of three lecture hours a week for one semester. May be repeated for credit. Prerequisite: Graduate standing and consent of instructor.
SSC 389. Time Series and Dynamic Models.
Exploration of the general class of state-space models, or dynamic models. Emphasis is placed on the implementation and use of the models presented, with applications focused on the social sciences. Topics include dynamic regression models, the Kalman filter, time series models, multivariate time series models, conditional variance models, Markov chain Monte Carlo algorithms for state-space models, and particle filters. Three lecture hours a week for one semester. Prerequisite: Graduate standing; Economics 392M (Topic 19: Probability and Statistics), Statistics and Scientific Computation 384, or the equivalent; and coding skills in R, Matlab, or Stata.
SSC 189R, 289R, 389R, 489R. Graduate Research.
Individual research project supervised by one or more faculty members. For each semester hour of credit earned, the equivalent of one lecture hour a week for one semester. May be repeated for credit. Prerequisite: Graduate standing.
SSC 190. Readings in Statistics.
Faculty-directed research seminar. Activities may vary, but will include readings of cutting-edge research papers, discussion of on-going student and faculty research projects, and consulting projects. May be repeated for credit. Prerequisite: Graduate standing.
SSC 391D. Data Mining.
Study of various mathematical and statistical aspects of data mining. Includes supervised learning (regression, classification, and support vector machines) and unsupervised learning (clustering, principal components analysis, and dimensionality reduction). Uses technical tools drawn from linear algebra, multivariate statistics, and optimization. Three lecture hours a week for one semester. Prerequisite: Graduate standing, and Mathematics 341 or the equivalent.
SSC 292, 392. Introduction to Scientific Programming.
Introduction to programming using both the C and Fortran (95/2003) languages, with applications to basic scientific problems. Covers common data types and structures, control structures, algorithms, performance measurement, and interoperability. For each semester hour of credit earned, one lecture hour a week for one semester. Statistics and Scientific Computation 322 and 392 may not both be counted. Prerequisite: Graduate standing; and credit or registration for Mathematics 408C or 408K.
SSC 392M. Computational Economics.
Introduction to the development and solution of economic models of growth; macroeconomic fluctuations; environmental economics; financial economics; general equilibrium models; game theory; and industrial economics. Includes neural nets, genetic algorithms and agent-based methods, and stochastic control theory applied to a variety of economic topics. Three lecture hours a week for one semester. Prerequisite: Graduate standing.
SSC 393C. Numerical Analysis: Linear Algebra.
Same as Computational Science, Engineering, and Mathematics 383C, Computer Science 383C, and Mathematics 383E. Survey of numerical methods in linear algebra: floating-point computation, solution of linear equations, least squares problems, algebraic eigenvalue problems. Three lecture hours a week for one semester. Only one of the following may be counted: Computational and Applied Mathematics 383C, Computational Science, Engineering, and Mathematics 383C, Computer Science 383C, Mathematics 383E, Statistics and Scientific Computation 393C. Prerequisite: Graduate standing; Computer Science 367 or Mathematics 368K; and Mathematics 340L, 341, or consent of instructor.
SSC 393D. Numerical Analysis: Interpolation, Approximation, Quadrature, and Differential Equations.
Same as Computational Science, Engineering, and Mathematics 383D, Computer Science 383D, and Mathematics 383F. Survey of numerical methods for interpolation, functional approximation, integration, and solution of differential equations. Three lecture hours a week for one semester. Only one of the following may be counted: Computational and Applied Mathematics 383D, Computational Science, Engineering, and Mathematics 383D, Computer Science 383D, Mathematics 383F, Statistics and Scientific Computation 393D. Prerequisite: Graduate standing; Computational Science, Engineering, and Mathematics 383C (or Computational and Applied Mathematics 383C), Computer Science 383C, Mathematics 383E, or Statistics and Scientific Computation 393C; and Mathematics 427K and 365C, or consent of instructor.
SSC 394. Scientific and Technical Computing.
Comprehensive introduction to computing techniques and methods applicable to many scientific disciplines and technical applications. Covers computer hardware and operating systems, systems software and tools, code development, numerical methods and math libraries, and basic visualization and data analysis tools. Three lecture hours a week for one semester. Prerequisite: Graduate standing, and Mathematics 408D or 408M. Prior programming experience is recommended.
SSC 394C. Parallel Computing for Science and Engineering.
Parallel computing principles, architectures, and technologies. Parallel application development, performance, and scalability. Designed to prepare students to formulate and develop parallel algorithms to implement effective applications for parallel computing systems. Three lecture hours a week for one semester. Prerequisite: Graduate standing, Mathematics 408D or 408M, Mathematics 340L, and prior programming experience using C or Fortran on Linux or Unix systems.
SSC 394D. Distributed and Grid Computing for Science and Engineering.
Distributed and grid computing principles and technologies. Covers common modes of grid computing for scientific applications, developing grid-enabled applications, and future trends in grid computing. Three lecture hours a week for one semester. Prerequisite: Graduate standing, Mathematics 408D or 408M, Mathematics 340L, and prior programming experience using C or Fortran on Linux or Unix systems.
SSC 394E. Visualization and Data Analysis for Science and Engineering.
Scientific visualization principles, practices, and technologies, including remote and collaborative visualization. Introduces statistical analysis, data mining, and feature detection. Three lecture hours a week for one semester. Prerequisite: Graduate standing, Mathematics 408D or 408M, Mathematics 340L, and prior programming experience using C or Fortran on Linux or Unix systems.
SSC 395. Advanced Topics in Scientific Computation.
Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing. Additional prerequisites vary with the topic and are given in the Course Schedule.
SSC 398R. Master's Report.
Preparation of a report to fulfill the requirement for the master's degree under the report option. The equivalent of three lecture hours a week for one semester. Offered on the credit/no credit basis only. Prerequisite: Graduate standing in statistics and scientific computation and consent of the supervising professor and the graduate adviser.
SSC 398T. Supervised Teaching in Statistics and Scientific Computation.
Supervised teaching experience; weekly group meetings, individual consultations, and reports. Three lecture hours a week for one semester. Offered on the credit/no credit basis only. Prerequisite: Graduate standing and appointment as a teaching assistant.
SSC 399R, 699R, 999R. Dissertation.
Offered on the credit/no credit basis only. Prerequisite: Admission to candidacy for the doctoral degree and written consent form.
SSC 399W, 699W, 999W. Dissertation.
Offered on the credit/no credit basis only. Prerequisite: Statistics and Scientific Computation 399R, 699R, or 999R; and written consent form.