fall 2016 courses
SDS 380C Statistical Methods I
Dr. M. Hersh, TTH 1-2:30pm. 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.
SDS 383C Statistical Modeling I
Dr. P. Sarkar, TTH 11-12:30pm. Course is restricted to SDS graduate students and portfolio students ONLY. Introduction to core applied statistical modeling ideas from a probabilistic, Bayesian perspective. Topics include: (i) Exploratory Data Analysis; (ii) Programming and Graphics in R; (iii) Bayesian Probability Models; (iv) Intro to the Gibbs Sampler; (v) Applied Regression Analysis; (vi) The “Normal-means” problem; and (vii) Hierarchical Models. Prerequisite: Graduate standing. https://stat.utexas.edu/images/SSC/Site/documents/SDS383C.pdf
SDS 384 2-Mathematical Statistics I
Dr. M. Parker, TTh 5-6:30 pm. The first semester of a two-semester course covering the general theory of mathematical statistics. The two-semester course covers distributions of functions of random variables, properties of a random sample, principles of data reduction, overview of hierarchical models, decision theory, Bayesian statistics, and theoretical results relevant to point estimation, interval estimation, and hypothesis testing. https://stat.utexas.edu/images/SSC/Site/documents/SSC_384.2_Mathematical_Statistics_I.pdf
SDS 384 4-Regression Analysis
Dr. M. Hersh, MW 1-2:30 pm. Simple and multiple linear regression, inference in regression, prediction of new observations, diagnostics and remedial measures, transformations, model building. Emphasis will be on both understanding the theory and applying theory to analyze real data. https://stat.utexas.edu/images/SSC/Site/documents/2012_ssc_384_regression_syllabus.pdf
SDS 384 7-Bayesian Statistical Methods
Dr. S. Walker, MW 2:30-4 pm. Fundamentals of Bayesian inference in single 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. https://stat.utexas.edu/images/SSC/Site/documents/bayesianstatistics_f12.pdf
SDS 385 Socl Stat: Dis Multivar Models
Dr. D. Powers, MW 3:30-5 pm. https://stat.utexas.edu/images/SSC/Site/documents/SSC_385_SocStat_DiscreteMultiMdl.pdf
SDS 385 14-Maximum-Likelihood Stats
Dr. T. Lin, MW 5-6:30 pm. 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.
SDS 385 Statistical Models for Big Data
Prof. J. Scott, MW 9:30-11 am. Course is restricted to SDS graduate students and portfolio students ONLY.
SDS 388 Consulting Seminar
Prof. M. Mahometa, M 9:30-12:30 pm. Course is restricted to SDS graduate students and portfolio students ONLY. Supervised experience in applying statistical or mathematical methods to real problems. Participation in weekly consulting sessions; directed readings in the statistical literature; the ethics of research and consulting; report writing and presentations. May be repeated for credit. Prerequisite: Graduate standing, and consent of instructor. **This course is only open for SDS Graduate Fellows and MS in Statistics 2nd year students. https://stat.utexas.edu/images/SSC/Site/documents/syllabus_ssc_388_-_fall_2012.pdf
SDS 190 Readings in Statistics
F 10-11 am. Course is restricted to SDS graduate students and portfolio students ONLY.
SDS 392 Intro Scientific Programming
Texas Advance Computing Center, TTh 12:30-2 pm. 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.
SDS 393C Numerical Anly: Linear Algebra
Dr. G. Biros, TTH 9:30-11am. Matrix Computations arise in a varied number of applications, such as, quantum chemistry computations, statistics, economics, data mining, etc. This first year graduate course focuses on some of the fundamental computations that occur in these applications. The standard problems whose numerical solutions we will study are (i) systems of linear equations, (ii) least squares problems, (iii) eigenvalue problems as well as SVD computations. We will also learn basic principles applicable to a variety of numerical problems and apply them to the standard problems. These principles include (i) matrix factorizations, (ii) perturbation theory and condition numbers, (iii) effects of roundoff error on algorithms and (iv) analysis of the speed of algorithms.
SDS 394 Scientific & Technical Computing
Texas Advance Computing Center, TTh 2-3:30 pm. 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.Prerequisite: Graduate standing, and Mathematics 408D or 408M. Prior programming experience is recommended. https://stat.utexas.edu/images/SSC/Site/documents/SSC_394_ScientificTechCmptg.pdf
spring 2017 courses
SDS 380D Statistical Methods II
Dr. M. Hersh, TTH 12:30-2:00pm. A continuation of SDS 380C: Statistical Methods I. The course presents an overview of advanced statistical modeling topics. Topics 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. Prerequisite: Graduate standing, and Statistics and Data Sciences 380C or the equivalent. https://stat.utexas.edu/images/SSC/Site/documents/SSC_380D_StatMethdI_f10.pdf
SDS 383D Statistical Modeling II
Prof. J. Scott, MWF 3:00-4:30pm, Course is restricted to SDS graduate students and portfolio students ONLY.
In this course, students will learn to describe real-world systems using structured probabilistic models that incorporate multiple layers of uncertainty. Major topics to be covered include: (i) theory of the multivariate normal distribution; (ii) mixture models; (iii) introduction to nonparametric Bayesian analysis; (iv) advanced hierarchical models and latent-variable models; (v) Generalized Linear Models; and (vi) advanced topics in linear and nonlinear regression. Examples will be taken from a wide variety of applied fields in the physical, social, and biological sciences. https://stat.utexas.edu/images/SSC/Site/documents/SDS383D_Spring2015.pdf
SDS 384 3-Mathematical Statistics II
Dr. M. Parker, TTH 5:00-6:30pm. A continuation of Statistics and Scientific Computation 384 (Topic 2). Additional prerequisite: Statistics and Data Sciences 384 (Topic 2).
SDS 384 6-Design & Analysis of Experiments
Dr. M. Hersh, MW 1:00-2:30pm. 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; split plot designs.
SDS 384 7-Bayesian Statistical Methods
Dr. Rama Lingham, TTH 2:00-3:30pm. Fundamentals of Bayesian inference in single 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.
SDS 385 Introduction to Statistical Learning
Dr. Kam Hamidieh, MW 10:-11:30am. Description: You will be introduced to the most widely used statistical learning techniques and tools, and how to compare and evaluate their performance. Technical material will be covered in an intuitive way. This course will be hands-on. Our main tool will be R: www.r-project.org. Towards the end of the semester, you will participate in a prediction competition. Prerequisites: The prerequisites for this course are: (i) graduate standing, (ii) basic knowledge of multiple regression, and (iii) some rudimentary programming experience. If you are not sure whether you meet the prerequisites, please contact Kam Hamidieh at firstname.lastname@example.org.
SDS 385 Computational Biology & Bioinformatics
Prof. C. Wilke, TTH 9:30-11:00am. In this class, students will learn the basic skills required to handle the kind of data sets current-day working biologists will encounter. Because any kind of large-scale, automated data analysis requires programming skills, a substantial component of this class will be dedicated to learning how to program in the two languages most commonly used by computational biologists, R and Python. The class will also put substantial emphasis on good data management practices, on data visualization, and on interpreting the patterns that are seen in the data. The class is designed as an introduction to efficient data analysis for all biology students; no prior programming experience is needed.
SDS 385 2-Applied Regression
Dr. Jay Zarnikau, M 6:00-9:00pm. 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.
SDS 386D Monte Carlo Methods in Statistics
Prof. S. Walker, MW 11:00am-12:30pm. Course is restricted to SDS graduate students and portfolio students ONLY. This course focuses on stochastic simulation for Bayesian inference. The main focus is for students to develop a solid understanding of MCMC methods and the underlying theoretical framework. Topics include: (i) Markov chains; (ii) Intro to MC integration; (iii) Gibbs Sampler; (iv) Metropolis-Hastings algorithms; (v) Slice sampling; and (vi) Sequential Monte Carlo. Prerequisites: Graduate standing, knowledge of mathematical statistics as well as basic coding skills (R, Matlab, or Stata).
SDS 387 Linear Models
Prof. P. Mueller, MW 9:30-11:00am. Course is restricted to SDS graduate students and portfolio students ONLY. This course focuses on the practical application of the projection approach to linear models. The course will begin with a review of essential linear algebra concepts including vector spaces, basis, linear transformations, norms, orthogonal projections, and simple matrix algebra. It continues by presenting the theory of linear models from a projection-based perspective. Still on the projection framework, Bayesian ideas will be introduced. Additional topics include: (i) Analysis of Variance; (ii) Generalized Linear Models; and (iii) Variable Selection Techniques. Prerequisites: Graduate standing, knowledge of mathematical statistics at a graduate level and linear algebra at an advanced undergraduate level is required as well as basic coding skills (R, Matlab, or Stata).
SDS 392 Intro to Scientific Programming
Texas Advanced Computing Center. TTH 3:30-5:00pm. 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.
SDS 394C Parallel Computing
Drs. Koesterke and Millfeld, TTH 12:30-2:00pm. Parallel computing principles, architectures, and technologies. Parallel application development, performance, and scalability. Prepares students to formulate and develop parallel algorithms to implement effective applications for parallel computing systems.