fall 2019 courses
SDS 380C Statistical Methods I
Prof. Hersh, TTh 12:30-2 pm. 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. 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. Parker, TTh 5-6:30 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. Hersh, MW 1-2:30 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 Methods
Prof. 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 Socl Stat: Dis Multivar Models
Prof. Powers. MW 3-4:30 pm. This course deals with regression models for discrete and categorical dependent variables. Regression-like models for discrete and categorical outcomes are widely used in applied research. Students in this course should have some prior exposure to linear regression models. This class serves a wide range of students; the material presented here aims to be useful for students at all levels. In keeping with the applied nature of this course, we will provide examples drawn mainly from sociological and demographic research.
SDS 385 14-Maximum-Likelihood Stats
Prof. Lin, TW 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.Prerequisite: Graduate standing; and Statistics and Data Sciences 380C or consent of instructor.
SDS 385 Stat Models for Big Data
Prof. Sarkar, MW 10:30-12 noon. 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.
SDS 388 Consulting Seminar
Prof. Viswanathan, W 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. 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.
SDS 392 Intro Scientific Programming
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
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.
SDS 387 Linear Models
Prof. Lakshminaray, MW 6:30-8 pm. 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, Mutlinomial, 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, classication, regularization and shrinkage, dimensionality reduction, and other multivariate techniques.
spring 2020 courses
