For your first course in statistics, please see the Placement Information

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 Audience: Introductory, Undergraduate 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 Audience: Introductory, Undergraduate, Minor 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 Audience: Introductory, Undergraduate 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) Audience: Introductory, Undergraduate 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 Audience: Introductory, Undergraduate 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. Audience: Undergraduate, Major, Minor 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) Audience: Introductory, Undergraduate, Major 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) Audience: Introductory, Undergraduate, Major, Minor 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) Audience: Undergraduate, Electives 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) Audience: Master's, Undergraduate, Electives 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) Audience: Master's, Undergraduate, Electives 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. Audience: Master's, Undergraduate, Electives 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) Audience: Undergraduate, Major, Electives 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 Audience: Undergraduate 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) Audience: Undergraduate, Electives 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) Audience: Undergraduate, Electives 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 Audience: Undergraduate, Electives 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 Audience: Undergraduate, Major 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) Audience: Undergraduate, Major 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 Audience: Graduate 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) Audience: Graduate, Master's, First Year 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) Audience: Graduate, Master's, First Year 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 Audience: Graduate, Master's, First Year 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 Audience: Graduate, Master's, First Year 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. Audience: Graduate, Master's, Undergraduate Typically offered: Fall and/or Spring |

STA 571 |
## Advanced Machine Learning
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. Audience: Graduate, PhD, Master's, Second Year, Undergraduate, Electives 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) Audience: Graduate, Master's, Undergraduate, Major 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 Audience: Graduate, Master's 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 Audience: Introductory, Graduate, Master's 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 Audience: Introductory, Graduate, PhD, Master's 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 Audience: Graduate, PhD, Master's 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 Audience: Graduate, PhD, Master's 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) Audience: Graduate, Master's, First Year 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 Audience: Graduate, PhD Typically offered: Fall and/or Spring |

STA 701S (395) |
## Readings in Statistical Science
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) Audience: Graduate, PhD, First Year 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. Audience: Graduate, PhD 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) Audience: Graduate, PhD 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) Audience: Graduate, PhD, Master's 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: 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 Audience: Graduate, PhD, Master's Typically offered: Fall and/or Spring |

STA 863 |
## Advanced Statistical Computing
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. Audience: Graduate, PhD, Master's 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 Audience: Graduate, PhD, Master's 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) Audience: Graduate, PhD, Master's Typically offered: Occasionally |

STA 993 (291) |
## Independent Study
Directed reading and research. Consent of instructor and director of graduate studies required. Prerequisites: None Audience: Graduate, PhD 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 Audience: Graduate, PhD Typically offered: Fall and/or Spring |