fall 2020 courses
SDS 380C STATISTICAL METHODS I
Prof. Matt Hersh, MWF 9-10 am. 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. Prerequisite: Graduate standing.
SDS 383C STATISTICAL MODELING I
Prof. Abhra Sarkar, TTh 12:30-2 pm. Rigorous and fast-paced 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. Prerequisite: Graduate standing, admission to PhD in Statistics program.
SDS 384 - 2 MATHEMATICAL STATISTICS I
Prof. Mary Parker, TTh 3:30-5 pm. Concepts of probability and mathematical statistics with applications in data analysis and research. The general theory of mathematical statistics. Includes distributions offunctions of random variables, properties of a random sample, principlesof data reduction, an overview of hierarchical models, decision theory, Bayesian statistics, and theoretical results relevant to point estimation, interval estimation, and hypothesis testing. Prerequisite: Graduate standing; an introductory probability course (M362K) and a mathematical statistics course (M 378K), or consent of instructor.
SDS 384 - 4 REGRESSION ANALYSIS
Prof. Bindu Viswanthan, MW 12-1:30 pm. 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. Prerequisite: Graduate standing; an introductory probability course and a statistics course, or consent of instructor.
SDS 384 - 7 BAYESIAN STATISTICAL MTHDS
Prof. Rama Lingham, MW 2:30-4 pm. 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. Prerequisite: Graduate standing; an introductory probability course and a statistics course, and linear algebra or consent of instructor.
SDS 385 STAT MODELS FOR BIG DATA
Prof. Purna Sarkar. MW 2-3:30 pm. This course is focused on the statistical and algorithmic principles needed to confront large data sets. It is not, at least primarily, about the practical issues associated with gathering, storing, cleaning, curating, and managing large data sets. Prerequisite: Graduate standing and consent of instructor.
SDS 387 LINEAR MODELS
Prof. Cory Zigler, MW 9-10:30 am. Generalized Linear Models is the swiss army knife of statistical learning. It provides us necessary tools to model discrete and continuous random variables that follow Bernoulli, Multinominal, Poisson, and Normal distributions. Combined with classical linear model theory with modern advances in large-scale statistical learning, the course will cover basic linear algebra, random vectors and matrices, the multivariate Gaussian distribution theory, estimation, regression, classification, regularization and shrinkage, dimensionality reduction, and other multivariate techniques.
SDS 388 CONSULTING SEMINAR
Prof. Bindu Viswanathan, T 11-2 pm. 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. Restricted to MS Stats students.
SDS 190 READINGS IN STATISTICS
Prof. Stephen Walker, W 3-4 pm. Faculty directed research seminar. Activities may vary, but will include readings of cutting-edge research papers, discussion of on-going student and faculty projects, and consulting projects. Restricted to PhD in Statistics students.
SDS 392 INTRO SCIENTIFIC PROGRAMMING
TACC - Jeaime Powell, 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 394 SCIENTIF & TECHNICAL COMPUTING
TACC-Lars Koesterke, 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.
spring 2021 courses
SDS 380D STATISTICAL METHODS II
Prof. Matt Hersh, TTh 12:30-2 pm. emphasized. Prerequisite: Graduate standing, and Statistics and Data Sciences 380C or the equivalent.
SDS 383D STATISTICAL MODELING II
Prof. Anthony Linero, MW 3-4:30 pm. 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. Prerequisite: Graduate standing. Restricted to PhD in Statistics students.
SDS 384 - 3 MATHEMATICAL STATISTICS II
Prof. Mary Parker, TTh 5-6:30 pm. This is the second semester of a two-semester course designed to provide a solid theoretical foundation in mathematical statistics. It focuses on the theory of point estimation, interval estimation, and hypothesis testing. Although this is largely a course in classical methods, some materials on hierarchical models, Bayesian methods, and decision theory are included.
SDS 384 - 6 DESIGN & ANLY OF EXPERIMENTS
Prof. Matt Hersh, MW 1-2:30 pm. 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 MTHDS
Prof. Rama Lingham, TTh 2-3:30 pm. 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. Prerequisite: Graduate standing; an introductory probability course and a statistics course, and linear algebra or consent of instructor.
SDS 384 - 11 THEORETICAL STATISTICS
Prof. Purna Sarkar, MW 10-11:30 am. This course provides an introduction to theoretical frequentist Statistics. The first half of the course covers concentration of measure and U statistics, etc. The second half introduces basics from empirical processes, asymptotic testing and applications including bootstrap, subsampling, kernel regression etc. Prerequisites: Graduate standing, good familiarity with calculus, and undergraduate probability. Restricted to PhD in Statistics students.
SDS 386D MONTE CARLO MTHDS IN STATS
Prof. Peter Mueller, TTh 11-12:30 pm. Graduate standing, knowledge of mathematical statistics as well as basic coding skills (ideally R, or Matlab or something equivalent). Some prior knowledge of Bayesian inference is needed. (at the level of Peter Hoff’s book). The course will start with a brief review of Bayesian inference (see the textbook below). For a quick self-test, whether this class is right for you: Do you know (1) Bayesian inference? Do you know how to do posterior inference in a normal linear regression? In a hierarchical model? (2) basic probability? Do you know what a Markov chain is? (3) some basic computation, ideally in R (or matlab or anything equivalent). The class will not be about computation, but you will need it for homeworks. Can you program an iterative loop, functions (macros)?
SDS 190 READINGS IN STATISTICS
Prof. Stephen Walker, M 1:30-2:30 pm. Faculty directed research seminar. Activities may vary, but will include readings of cutting-edge research papers, discussion of on-going student and faculty projects, and consulting projects. Restricted to PhD in Statistics students.
SDS 392 INTRO SCIENTIFIC PROGRAMMING
TTh 3:30-5 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
Prof. Robert A Van De Geijn, TTh 2-3:30 pm. Survey of numerical methods in linear algebra: floating-point computation, solution of linear equations, least squares problems, algebraic eigenvalue problems. Prerequisite: Graduate standing
SDS 394C PARALLEL COMPUT FOR SCI & ENGR
TTh 12:30-2 pm. 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.
