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Department of Statistics and Data Sciences
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Graduate Course Inventory

SDS offers graduate level courses throughout the year under the SDS field of study.

SDS Statistics Courses

Click on a course to be taken to its description.

Course Descriptions

SDS 380C: Statistical Methods I

An introduction to the fundamental concepts and methods of statistics. The course will cover topics ranging from descriptive statistics, sampling distributions, confidence intervals, and hypothesis testing. Topics could include simple and multiple linear regression, Analysis of Variance, and Categorical Analysis. Use of statistical software is emphasized. Prerequisite: Graduate standing.

[Offered in fall semesters only.]

SDS 380D: Statistical Methods II

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.

[Offered in spring semesters only.]

SDS 381M: Topics in Statistics and Data Science Foundations

Examine core concepts of statistics and data science. May be repeated for credit when the topics vary. Prerequisite: Graduate standing; Calculus, linear algebra, and introductory statistics course; or consent of instructor. Additional prerequisites vary with the topic.

SDS 384: Topics in Statistics and Probability

Concepts of probability and mathematical statistics with applications in data analysis and research. May be repeated for credit when the topics vary. Prerequisite: Graduate standing, and Statistics and Data Sciences 382, Mathematics 362K and 378K, or consent of instructor.

  • Topic 2: Mathematical Statistics I. 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.
  • Topic 3: Mathematical Statistics II. A continuation of Statistics and Scientific Computation 384 (Topic 2). Additional prerequisite: Statistics and Data Sciences 384 (Topic 2).
  • Topic 4: Regression Analysis. 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.
  • Topic 6: Design and Analysis of Experiments. 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.
  • Topic 7: Bayesian Statistical Methods. 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 386M: Topics in Statistics and Data Science Extensions

Examine concepts of statistics and data science. May be repeated for credit when the topics vary. Prerequisite: Graduate standing; Calculus, linear algebra, and introductory statistics course; or consent of instructor. Additional prerequisites vary with the topic.

SDS 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 projects, and consulting projects. Prerequisite: Enrollment in the Statistics Ph.D. Program. 

[Required for Ph.D. in Statistics students. Offered fall and spring  semesters.]

SDS 391P: Topics in Statistics and Data Science Foundations

Examine advanced core concepts of statistics and data science. Prerequisite: Graduate standing. Additional prerequisites vary with the topic. Restricted to students in the PhD statistics program.

SDS 391P.1: Advanced Statistical Modeling and Applications I

An introduction to core applied statistical modeling ideas. Topics include maximum likelihood and Bayesian inference, hierarchical models, mixture models, applied regression analysis, computational methods for fitting such models to data, model checking, and model comparison. Only one of the following may be counted: Statistics and Data Sciences 391P.1, 383C, and Statistics and Scientific Computation 383C. Prerequisite: Enrollment in the Statistics Ph.D. Program; or graduate standing and permission of the instructor.

[Required for Ph.D. in Statistics students. Offered in fall semesters only.]

SDS 391P.2: Advanced Statistical Modeling and Applications II

Examine the use of structured, probabilistic models that incorporate multiple layers of uncertainty to describe real-world systems. Analyze generalized linear models, Gaussian processes, advanced hierarchical models and latent-variable models, and advanced linear and nonlinear regression. Only one of the following may be counted: Statistics and Data Sciences 391P.2, 383D, Statistics and Scientific Computation 383D. Prerequisite: Statistics and Data Sciences 383C or 391P.1; or graduate standing and permission of the instructor.

[Required for Ph.D. in Statistics students. Offered in spring semesters only.]

SDS 391P.3: Theory of the Linear Model

Explore the mathematical underpinnings and theory of linear regression modeling in likelihood and assumption-lean settings. Discuss linear algebra, regularization, asymptotic statistics, and minimax optimality. The course is intended to provide Ph.D. students with the theoretical background and mathematical tools to conduct research in statistical methods and theory.  Only one of the following may be counted: Statistics and Data Sciences 391P.3 or 387.Prerequisite: Enrollment in the Statistics Ph.D. Program; or graduate standing and permission of the instructor.

[Required for Ph.D. in Statistics students. Offered in fall semesters only.]

SDS 391P.4: Computational Inference

Examine computational methods to implement statistical inference and prediction and explore their properties and applicability. Discuss deterministic and stochastic optimization, Monte Carlo methods, and variational inference. Only one of the following may be counted: Statistics and Data Sciences 391P.4 or 386D. Prerequisite: Statistics and Data Sciences SDS 391P.1 or 383D or permission of the instructor.

[Required for Ph.D. in Statistics students. Offered in spring semesters only.]

SDS 391P.5: Concepts in Mathematical Statistics

Examine key concepts and ideas behind the mathematics associated with statistical procedures. Analyze derivations of well-known practices, such as point estimation, uncertainty quantification, hypothesis testing, nonparametric methods, and the analysis of various types of models, including their asymptotic properties. The course starts with classical likelihood inference and nonparametric inference while the remainder covers Bayesian methods, with the necessary probability, such as convergence of random variables. Only one of the following may be counted: Statistics and Data Sciences 391P.5 and 384.2. Prerequisite: Enrollment in the Statistics Ph.D. Program.

[Required for Ph.D. in Statistics students. Offered in fall semesters only.]

SDS 391P.6: Theoretical Statistics and Machine Learning

Examine concentration inequalities and asymptotic theory. Explore Hoeffding, Chernoff, Bernstein, Martingale-based methods, the Efron-Stein inequality, the Gaussian Lipschitz theorem, U statistics, uniform laws of large numbers, VC dimension, covering and packing. Practice high-dimensional covariance estimation and resampling. Only one of the following may be counted: Statistics and Data Sciences 391P.5 or 384 .11. Prerequisite: Statistics and Data Sciences 391P.5 or 384.3 or equivalent; or graduate standing and permission of the instructor.

[Required for Ph.D. in Statistics students. Offered in spring semesters only.]

SDS 396P: Topics in Statistics and Data Science Extensions

Examine advanced concepts of statistics and data science. Restricted to students in the PhD statistics program. Graduate standing. Additional prerequisites vary with the topic.

SDS 396P.1: Statistical Machine Learning Optimization

Introduction to machine learning and optimization methods from a statistical perspective with an emphasis on mathematical theory and applications. Only one of the following may be counted:Statistics and Data Sciences 396P.1 or 384 (Topic: Stat Mach Learning Optimization). Prerequisite: Enrollment in the Statistics Ph.D. Program; or graduate standing and permission of the instructor.

[Offered every alternate fall semester only.]