Course Number Course Name and Description
STA 30 (10)

## Basic Statistics and Quantitative Literacy

Statistical concepts involved in making inferences, decisions, and predictions from data. Emphasis on applications, not formal technique. Instructor: Staff

Prerequisites: Must have taken placement test and placed accordingly

Typically offered: Fall, Spring and Summer

STA 89S (49S)

## First Year Seminar

Topics vary each semester offered. Instructor: Staff

Prerequisites: First year and Transfer students only

Audience: Introductory

Typically offered: Spring Only

STA 101 (101)

## Data Analysis and Statistical Inference

Introduction to statistics as a science of understanding and analyzing data. Major themes include data collection, exploratory analysis, inference, and modeling. Focus on principles underlying quantitative research in social sciences, humanities...

Prerequisites: None

Typically offered: Fall, Spring and Summer

STA 102 (102)

## Intro Biostatistics

Reading and interpretation of statistical analyses from life science and medical literature. Topics include: basic concepts and tools of probability and conditional probability, independence, two-by-two tables, Simpson's paradox, medical...

Prerequisites: None

Typically offered: Fall and/or Spring

STA 110FS (80FCS)

## Focus Program - Introductory Special Topics in Statistics

This is a seminar course for focus students. Topics vary every semester.

Prerequisites: Math 31 (21) is required.

Audience: Introductory

Typically offered: Occasionally

STA 111 (103)

## Probability/Stat Inference

Basic laws of probability, random events, independence and dependence, expectations, Bayes theorem. Discrete and continuous random variables, density, and distribution functions. Binomial and normal models for observational data. Introduction to...

Prerequisites: MATH 21 (31)

Typically offered: Fall and/or Spring

STA 130 (113)

## Probability/Statistics In Engineering

Introduction to probability, independence, conditional independence, and Bayes' theorem. Discrete and continuous, univariate and multivariate distributions. Linear and nonlinear transformations of random variables. Classical and Bayesian...

Prerequisites: MATH 212 (103) or equivalent

Typically offered: Fall and/or Spring

STA 210 (121)

## Regression Analysis

Extensive study of regression modeling. Multiple regression, weighted least squares, logistic regression, log-linear models, analysis of variance, model diagnostics and selection. Emphasis on applications. Examples drawn from a variety of fields...

Prerequisites: 100-level STA course.

Typically offered: Fall and/or Spring

STA 230 (104)

## Probability

Probability models, random variables with discrete and continuous distributions. Independence, joint distributions, conditional distributions. Expectations, functions of random variables, central limit theorem. Instructor: Staff

Prerequisites: MATH 202 (102) MATH 212 (103) or MATH 222 (105)

Typically offered: Fall and/or Spring

STA 250 (114)

## Statistics

An introduction to the concepts, theory, and application of statistical inference, including the structure of statistical problems, probability modeling, data analysis and statistical computing, and linear regression. Inference from the...

Prerequisites: MATH 221 (104) and STA 230 (104)/MATH 230 (135)

Typically offered: Fall and/or Spring

STA 320 (130)

## Design and Analysis of Causal Studies

Design of randomized experiments and observational studies. Role of randomization, block designs, factorial designs, fractional factorial designs, matching. Analysis of variance, contrasts, propensity score matching, instrumental variables. ...

Prerequisites: STA 210 (121)

Typically offered: Occasionally

STA 321 (135)

## Statistics Of Surveys

Design and analysis of surveys, including random sampling, stratification, clustering, and multi-stage sampling. Design-based and model-based inference. Methods for handling missing data. Instructor: Reiter

Prerequisites: STA 210 (121)

Typically offered: Occasionally

STA 340 (140)

## Introduction to Statistical Decision Analysis

Quantitative methods for decision making under uncertainty. Probability theory, personal probabilities and utilities, decision trees, ROC curves, sensitivity analysis, dominant strategies, Bayesian networks and influence diagrams, Markov models...

Prerequisites: STA 230 (104)

Typically offered: Occasionally

STA 350S (180S)

## Statistical Methods in Bioinformatics

Statistical and analytical tools for bioinformatics and genomics. Methods for comparison, database search, and functional inference for DNA, RNA, and protein sequences; analysis of families of molecular sequences and structures; inference in...

Prerequisites: Statistical Science 230 (104) required. Statistical Science 250 (114) Computer programming and molecular biology required.

Typically offered: Occasionally

STA 360 (122)

## Bayesian And Modern Statistics

Principles of data analysis and advanced statistical modeling. Bayesian inference, prior and posterior distributions, multi-level models, model checking and selection, stochastic simulation by Markov Chain Monte Carlo. Instructor: Clyde, Reiter...

Prerequisites: STA 230 (104) , STA 250 (114), and STA (210) 121 (or equivalent)

Typically offered: Fall and/or Spring

STA 393 (191)

## Research Independent Study

Individual research in a field of special interest, under the supervision of a faculty member, resulting in a substantive paper or written report containing significant analysis and interpretation of a previously approved topic. Consent of...

Prerequisites: None

Typically offered: Fall and/or Spring

STA 470S (145S)

## Introduction to Statistical Consulting

Participation by students in data analysis projects from the DSS Statistical Consulting Center. Projects led and directed by DSS faculty. Instructor: Lucas

Prerequisites: STA 360 (122)

Typically offered: Fall and/or Spring

STA 471S (175S)

## Computational Data Analysis

Data analysis, exploration, and representation. Scientific modeling and computation. Data mining for large datasets, algebraic decomposition methods, stochastic simulation for temporal models of dynamic processes, graphical and network data,...

Prerequisites: STA 360 (122)

Typically offered: Spring Only

STA 490S (393)

## Special Topics in Statistics

Special topics not covered in core courses and more advanced topics related to current research directions in statistics.  Consent of instructor required.

Prerequisites: None

Typically offered: Occasionally

STA 497S (190AS)

## Research Seminar in Statistical Science I

Statistical and mathematical underpinnings of methodological research in statistical science. Student presentations of their statistical research in collaboration with, and under the supervision of, an DSS faculty mentor.

Prerequisites: None

Typically offered: Fall Only

STA 498S (190)

## Research Seminar in Statistical Science II

Continuation of Statistics 497S (190AS). Statistical and mathematical underpinnings of methodological research in statistical science. Student presentations of their statistical research in collaboration with, and under the supervision of, a DSS...

Prerequisites: STA 497S (STA 190AS)

Typically offered: Spring Only

STA 503 (234)

## Choice Theory

This seminar deals with the foundations and applications of the theory of rational choice, including Bayesian decision theory (subjective expected utility) as well as nonexpected utility theory, noncooperative game theory, and arbitrage theory...

Prerequisites: None

Typically offered: Occasionally

STA 521

## Modern Regression and Predictive Modeling

Exploratory data analysis techniques and visualization of data with interactive graphics. Multiple linear regression and model building, predictive distributions, penalized and Bayesian estimation, model selection and model uncertainty including...

Prerequisites: STA 210, 230 and 250, or equivalents; STA 601 (or co-registration)

Typically offered: Spring Only

STA 523

## Programming for Statistical Science

Statistical programming and computation using R, Python, and/or Matlab or other languages and environments. Custom code development for central statistical models using Python as a core language interfacing with R, Matlab and C/C++, based on the...

Prerequisites: STA 210, 230 and 250, or equivalents; STA 601 (or co-registration)

Typically offered: Fall Only

STA 531

## Advanced Bayesian Inference and Stochastic Modeling

Art and science of building graphical models and stochastic simulation methods for inference and prediction. Mixture models, networks, and other latent variable probability models, i.e. hidden Markov models. Review of discrete and continuous...

Prerequisites: STA 521, 523, 601

Typically offered: Spring Only

STA 532

## Theory of Statistical Inference

Core mathematical foundations of classical and Bayesian statistical inference. Theory of point and interval estimation and testing based on efficiency, consistency, sufficiency and robustness. Maximum likelihood, moments and non-parametric...

Prerequisites: STA 521, 523, 601

Typically offered: Spring Only

STA 561

## Probabilistic Machine Learning

Introduction to concepts in probabilistic machine learning with a focus on discriminative and hierarchical generative models....

Prerequisites: Linear algebra, Statistical Science 250 or Statistical Science 611.

Typically offered: Fall and/or Spring

STA 571

Advanced concepts in probabilistic machine learning with a focus on discriminative and hierarchical generative models and applications. Topics include nonparametric Bayesian methods, optimization, sparsity, topic models, ranking, social network...

Prerequisites: Linear algebra and Statistics 250 or Statistics 611 required. Statistics 561 recommended.

Typically offered: Spring Only

STA 581

## ProSeminar: Becoming a Statistical Scientist

Statistical paradigms and current directions, communication of statistical ideas and arguments, statistical ethics, overview of study designs, building a statistical network, professional societies, developing a web/social media presence, career...

Prerequisites: STA 531, 532, 523 (or co-registration)

Audience: Introductory, Graduate, Master's, First Year

Typically offered: Fall Only

STA 582

## DataFest

Students work in teams to solve this year’s big data challenge on campus. Instructor: Cetinkaya-Rundel

Prerequisites: STA 531, 532, 523 (or co-registration)

Typically offered: Spring Only

STA 601

## Bayesian and Modern Statistical Data Analysis

Principles of data analysis and modern statistical modeling. Exploratory data analysis. Introduction to Bayesian inference, prior and posterior distributions, predictive distributions, hierarchical models, model checking and selection, missing...

Prerequisites: STA 210, 230 and 250 (or equivalent)

Audience: Graduate, PhD, Master's, First Year

Typically offered: Fall and/or Spring

STA 611 (213)

## Introduction to Mathematical Statistics

Formal introduction to basic theory and methods of probability and statistics: probability and sample spaces, independence, conditional probability and Bayes' theorem; random variables, distributions, moments and...

Prerequisites: Multivariable calculus

Typically offered: Fall Only

STA 613 (270)

## Statistical Methods/Computational Biology

Methods of statistical inference and stochastic modeling with application to functional genomics and computational molecular biology.

Prerequisites: STA 611 (213). linear algebra, and multivariate calculus

Typically offered: Spring Only

STA 623

## Statistical Decision Theory

Formulation of decision problems; criteria for optimality: maximum expected utility and minimax. Axiomatic foundations of expected utility; coherence and the axioms of probability (the Dutch Book theorem). Elicitation of probabilities and...

Prerequisites: Statistical Science 732 or consent of instructor.

Audience: Graduate, PhD, Master's, Second Year

Typically offered: Fall and/or Spring

STA 640

## Causal Inference

Statistical issues in causality and methods for estimating causal effects. Randomized designs and alternative designs and methods for when randomization is infeasible: matching methods, propensity scores, longitudinal treatments, regression...

Prerequisites: STA 531, 532, 523

Typically offered: Fall and/or Spring

STA 641

## Statistical Learning and Bayesian Nonparametrics

Nonparametric Bayesian models and methods for complex data analyses with non-linearity adjustment, flexible borrowing of information, local uncertainty quantification and interaction discovery.

Prerequisites: STA 531, 532, 523

Typically offered: Fall and/or Spring

STA 642

## Time Series and Dynamic Models

Statistical models for modeling, monitoring, assessing and forecasting time series. Univariate and multivariate dynamic models; state space modeling approaches; Bayesian inference and prediction; computational methods for fast data analysis,...

Prerequisites: STA 531, 532, 523

Audience: PhD, Master's

Typically offered: Fall and/or Spring

STA 643

## Modern Design of Experiments

Classical and Bayesian design notions and techniques -- experimental units, randomization, treatments, blocking and restrictions to randomization, and utility of designs. Optimal sample size determination for estimation and testing. Factorial and...

Prerequisites: STA 531, 532, 523

Typically offered: Fall and/or Spring

STA 663

## Statistical Computing and Computation

Statistical modeling and machine learning involving complex, large data sets and challenging computational problems.

Prerequisites: Prereqs: STA 521, 523; STA 531, 532 (or co-registration)

Typically offered: Spring Only

STA 690 (293)

## Special Topics in Statistics

Current topics vary by semester; please see Current Course Schedule for latest offering. Instructor: Staff

Prerequisites: None

Typically offered: Fall and/or Spring

STA 701S (395)

Advanced seminar on topics at research frontiers in statistical sciences. Consent of instructor required. Instructor: Staff

Prerequisites: None

Audience: Graduate, PhD, Second Year, First Year

Typically offered: Fall and/or Spring

STA 711 (205)

## Probability and Measure Theory

Introduction to probability spaces, the theory of measure and integration, random variables, and limit theorems. Distribution functions, densities, and characteristic functions; convergence of random variables and of their distributions; uniform...

Prerequisites: Real Analysis

Audience: Graduate, PhD, Master's, First Year

Typically offered: Fall Only

STA 721 (244)

## Linear Models

Multiple linear regression and model building. Exploratory data analysis techniques, variable transformations and selection, parameter estimation and interpretation, prediction, Bayesian hierarchical models, Bayes factors and intrinsic Bayes...

Prerequisites: None

Audience: Graduate, PhD, Master's, First Year

Typically offered: Fall Only

STA 723 (245)

## Statistics Case Studies

Advanced Bayesian statistical modelling from an applied perspective; problems and data from a range of application areas; focus on statistical thought and practice with in-depth examination of applications; statistical topics drawn from...

Prerequisites: STA 721 (244), suggested co-requisite STA 831 (214)

Typically offered: Spring Only

STA 732 (215)

## Statistical Inference

Classical, likelihood, and Bayesian approaches to statistical inference. Foundations of point and interval estimation, and properties of estimators (bias, consistency, efficiency, sufficiency, robustness). Testing: Type I and II errors, power,...

Prerequisites: STA 611 (213) and STA 721 (244) or consent of instructor

Audience: Graduate, PhD, Master's, First Year

Typically offered: Spring Only

STA 790 (294)

## Special Topics in Statistics

Current topics vary by semester; please see Current Course Schedule for latest offering. Instructor: Staff

Prerequisites: STA 611 (213) or consent of instructor. Pass/Fail grading only.

Typically offered: Fall and/or Spring

STA 811 (207)

## Probability

Theoretic probability.  Triangular arrays, weak laws of large numbers, variants of the central limit theorem, rates of convergence of limit theorems, local limit theorems, stable laws, infinitely divisible distributions, general state space...

Prerequisites: STA 711 (205)

Typically offered: Fall and/or Spring

STA 831 (214)

## Probability/Statistical Models

Theory, modeling, and computational topics in probability and statistics: distribution theory and modeling, simulation and applied probability models in statistics, generation of random variables. Monte Carlo method and integration; Markov Chain...

Prerequisites: STA 601 (290), STA 721 (244) and STA 732 (215)

Audience: Graduate, PhD, Master's, First Year

Typically offered: Spring Only

STA 832 (345)

## Multivariate Statistical Analysis

Review of matrix algebra, transformations, and Jacobians. The multivariate normal, Wishart, multivariate t, and related distributions are given special emphasis. Topics such as principal components, factor analysis, discrimination and...

Prerequisites: STA 732 (244) and STA 841 (216)

Typically offered: Occasionally

STA 841 (216)

## Models and Methods for Categorical Data

This course covers statistical methods for analyzing categorical data. Model and theory includes:
generalized linear models; models for binary data, polytomous data (ordered and unordered), counts,
contingency tables, matrix and...

Prerequisites: STA 721, 831 and STA 832, or consent of instructor

Audience: Graduate, PhD, Master's, Second Year

Typically offered: Fall Only

STA 851 (390)

## Statistical Consulting Workshop

Under faculty supervision, students address and solve consulting problems submitted to the Department of Statistical Science's  campus-wide consulting program, and present their solutions to the class. May be taken more than once. Consent of...

Prerequisites: None

Typically offered: Fall and/or Spring

STA 863

Advanced numerical methods and algorithms for statistical computing, emphasizing techniques relevant to modern Bayesian statistical research.

Prerequisites: STA 831

Audience: Graduate, PhD, Master's, Second Year

Typically offered: Spring Only

STA 941 (281)

## Modern Nonparametric Theory and Methods

Modern nonparametric approaches for exploring and drawing inferences from data. Topics may include: resampling methods, nonparametric density estimation, nonparametric regression and classification, bootstrapping, kernel methods, splines, local...

Prerequisites: None

Audience: PhD

Typically offered: Occasionally

STA 942 (356)

## Time Series and Forecasting

Time series data and models: trend, seasonality, and regressions. Traditional models: EWMA, EWR, ARMA. Dynamic linear models (DLMs). Bayesian learning, forecasting, and smoothing. Mathematical structure of DLMs and related models. Intervention,...

Prerequisites: Prerequisite: STA 732 and STA 831 or equivalent.

Typically offered: Occasionally

STA 944 (280)

## Spatial Statistics

Modeling data with spatial structure;point-referenced (geo-statistical)data, areal (lattice) data, and point process data; stationarity, valid covariance functions; Gaussian processes and generalizations; kriging; Markov random fields (CAR and...

Prerequisites: None

Typically offered: Occasionally

STA 961 (357)

## Stochastic Processes

Conditional probabilities and Radon-Nikodym derivatives of measures; tightness and weak convergence of probability measures, measurability and observability. Markov chains, Brownian motion, Poisson processes. Gaussian processes, birth-and-death...

Prerequisites: STA 711 (205)

Typically offered: Occasionally

STA 993 (291)

## Independent Study

Directed reading and research. Consent of instructor and director of graduate studies required.

Prerequisites: None

Typically offered: Fall and/or Spring

STA 994 (293)

## Independent Study

Directed reading and research. Consent of instructor and director of graduate studies required.

Prerequisites: None