Statistics and Data Sciences (SDS)
SDS 301 Elementary Statistical Methods 3 Hours
(TCCN: MATH 1342, MATH 1442)
3 Lecture Hours 0 Lab Hours
Covers the fundamental procedures for data organization and analysis. Subjects include frequency distributions, graphical presentation, sampling, experimental design, inference, and regression. Only one of the following may be counted: Educational Psychology 308, Statistics 309, 309H or Statistics and Data Sciences 301. Three lecture hours a week for one semester.
Core Curriculum: 020
Grading: Student Option
Repeatable for credit: No
Academic Level: Lower Division
SDS 302F Foundations of Data Analysis 3 Hours
3 Lecture Hours 1 Lab Hours
Introduction to data analysis and statistical methods. Subjects include random sampling; principles of observational study and experimental design; data summaries and graphics; and statistical models and inference, including the simple linear regression model and one-way analysis of variance. Only one of the following may be counted: Statistics and Data Sciences 302, 302F, 306. Three lecture hours and one laboratory hour a week for one semester.
Core Curriculum: 020
Grading: Student Option
Repeatable for credit: No
Academic Level: Lower Division
SDS 313 Introduction to Data Science 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to the principles and practice of data science. Explore R and reproducible data analysis; summarizing data using descriptive statistics; data visualization and storytelling; data wrangling and relational data; basic prediction and classification using regression models; and programming in R. Only one of the following may be counted: Statistics and Data Sciences 313, 322E, 348. The equivalent of three lecture hours a week for one semester.
Grading: Student Option
Repeatable for credit: No
Academic Level: Lower Division
SDS 315 Statistical Thinking 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to the fundamental ideas of statistical thinking with R programming. Explore survey, experimental, and observational study design; common sources of random and systematic error in data; the bootstrap as a tool for quantifying uncertainty; hypothesis testing; regression; and the role of statistics in an ethical and just society. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Statistics and Data Sciences 313 with a grade of at least C-.
Grading: Student Option
Repeatable for credit: No
Academic Level: Lower Division
SDS 119S, 219S, 319S, 419S, 519S, 619S, 719S, 819S, 919S Topics in Statistics and Data Sciences 1-9 Hours
0 Lecture Hours 0 Lab Hours
This course is used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office. Credit is recorded as assigned by the study abroad adviser in the Department of Statistics and Data Sciences. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence. Transfer credit is awarded for work in an affiliated studies program.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Lower Division
SDS 320E Elements of Statistics 3 Hours
3 Lecture Hours 1 Lab Hours
Introduction to statistics. Subjects include probability; principles of observational study and experimental design; statistical models and inference, including the multiple linear regression model and one-way analysis of variance. R programming is introduced. Only one of the following may be counted: Statistics and Data Sciences 320E, 320H, and 328M. Three lecture hours and one laboratory hour a week for one semester.
Core Curriculum: 020
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 320H Elements of Statistics: Honors 3 Hours
3 Lecture Hours 1 Lab Hours
Introduction to statistics. Subjects include probability; principles of observational study and experimental design; statistical models and inference, including the multiple linear regression model and one-way analysis of variance. R programming is introduced. Only one of the following may be counted: Statistics and Data Sciences 320E, 320H, and 328M. Three lecture hours and one laboratory hour a week for one semester.
Core Curriculum: 020
Restrictions: Restricted to students in the Dean's Scholars, Health Science Scholars, or Polymathic Scholars Honors Program in the College of Natural Sciences.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 321 Introduction to Probability and Statistics 3 Hours
4 Lecture Hours 0 Lab Hours
Covers fundamentals of probability, combinatorics, discrete and continuous random variables, jointly distributed random variables, and limit theorems. Using probability to introduce fundamentals of statistics, including Bayesian and classical inference. Statistics and Data Sciences 321 and 431 may not both be counted. The equivalent of four lectures hours a week.
Pre/Corequisites: Mathematics 408C, 408L, 408R, 408S, or 408Q with a grade of at least C-.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 322E Elements of Data Science 3 Hours
3 Lecture Hours 1 Lab Hours
Explore data science tools and examine data wrangling; exploratory data analysis and data visualization; markdown and data workflow; simulation-based inference; and classification methods. R programming is emphasized and Python programming is introduced. Only one of the following may be counted: Statistics and Data Sciences 313, 322E, 348. Three lecture hours and one laboratory hour a week for one semester.
Pre/Corequisites: An introductory statistics course.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 324E Elements of Regression Analysis 3 Hours
3 Lecture Hours 0 Lab Hours
Explore the use of regression analysis in applied research and learn about multiple linear regression; ANOVA; logistic regression; random and mixed-effects models; and models for dependent data. Engage in the identification of appropriate statistical methods and interpretation of software output. R programming is introduced. Statistics and Data Sciences 324E and 332 may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: Statistics and Data Sciences 302F and Statistics and Data Sciences 322E or Statistics and Data Sciences 320E.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 325H Honors Statistics 3 Hours
3 Lecture Hours 0 Lab Hours
An introduction to the fundamental theories, concepts, and methods of statistics. Emphasizes probability models, exploratory data analysis, sampling distributions, confidence intervals, hypothesis testing, correlation and regression, and the use of statistical software. Statistics and Data Sciences 325H and Statistics and Scientific Computation 325H may not both be counted. Three lecture hours a week for one semester.
Core Curriculum: 020
Pre/Corequisites: Admission to the Dean's Scholars Honors Program in the College of Natural Sciences or consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 326E Elements of Statistical Machine Learning 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to the concepts and tools of data science, statistics, and machine learning used to draw inferences about large-scale and real-world data. Explore data visualization, linear and nonlinear models, regularization, classification, resampling, tree-based methods, support vector machines, and unsupervised learning. Statistics and Data Sciences 323 and 326E may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: Statistics and Data Sciences 320E, 322E, and Statistics and Data Sciences 321 or Mathematics 362K.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 129S, 229S, 329S, 429S, 529S, 629S, 729S, 829S, 929S Topics in Statistics and Data Sciences 1-9 Hours
0 Lecture Hours 0 Lab Hours
This course is used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office. Credit is recorded as assigned by the study abroad adviser in the Department of Statistics and Data Sciences. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence. Transfer credit is awarded for work in an affiliated studies program.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Upper Division
SDS 431 Probability and Statistical Inference 4 Hours
3 Lecture Hours 2 Lab Hours
Introduction to probability and statistical inference. Examine events and random experiments; basic rules of probability; joint, conditional, and marginal probability and independence; discrete and continuous random variables; random sampling and estimation; large-sample theory results and central limit theorem-based inferential summaries; and maximum likelihood estimation. Statistics and Data Sciences 321 and 431 may not both be counted. Three lecture hours and two laboratory hours a week for one semester.
Pre/Corequisites: Statistics and Data Sciences 315 with a grade of at least C-; and credit with a grade of at least C- or registration for Mathematics 408D, 408L, or 408S.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 334 Intermediate Statistical Methods 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to applied regression analysis. Explore estimation and inference in multiple regression models; logistic regression; regression for count data; time-to-event models; and case studies in regression modeling in published work, emphasizing both the use and limitations of regression modeling in advancing scientific knowledge. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Statistics and Data Sciences 431 with a grade of at least C-; Mathematics 340L or 341 or Statistics and Data Sciences 329C with a grade of at least C-; and Computer Science 303E or 312 with a grade of at least C-.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 336 Practical Machine Learning 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to machine learning for data science with an emphasis on Python programming. Explore comparing algorithm performance; decision-tree algorithms; classification algorithms; model averaging; unsupervised learning; and neural networks. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Statistics and Data Sciences 334 with a grade of at least C-; and registration for or a grade of at least C- in Computer Science 327E.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 354 Advanced Statistical Methods 3 Hours
3 Lecture Hours 0 Lab Hours
Explore advanced methods in statistics and data science. Examine modeling data with multilevel (hierarchical) structure and causal inference, including design and analysis strategies. Discuss smoothing methods; spatial and time series models; additive models; and models for network data. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Statistics and Data Sciences 334 with a grade of at least C-.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 357 Case Studies in Data Science 3 Hours
3 Lecture Hours 0 Lab Hours
Explore advanced case studies in data science, with an emphasis on the full data analysis pipeline. Examine data collection, identification of data limitations; data privacy; data preparation and exploration; building, using, and evaluating models; creating data products; and communication and persuasion with data. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Statistics and Data Sciences 334 with a grade of at least C-; credit with a grade of at least C- or registration for Statistics and Data Sciences 336.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 364 Bayesian Statistics 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to the Bayesian approach for statistical inference. Explore prior, posterior, and predictive distributions: conjugate priors; informative and non-informative priors; models for normal, categorical, and count data; Bayesian computation, including MCMC and the Gibbs sampler; hierarchical models; and Bayesian model checking and model selection. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Statistics and Data Sciences 431 or 321 with a grade of at least C-; Mathematics 340L or 341 or Statistics and Data Sciences 329C with a grade of at least C-; and credit with a grade of at least C- or registration for Statistics and Data Sciences 334.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 365 Special Topics in Statistics and Data Science 3 Hours
3 Lecture Hours 0 Lab Hours
Examine concepts of statistics and data science. Three lecture hours a week for one semester.
Pre/Corequisites: Upper division standing; additional prerequisites may vary with the topic.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Upper Division
Topic 1: Sports Analytics. Explore fundamental principles and methods of sports analytics. Participate in practical experience in collecting and analyzing sports data. Discuss data collection, metric estimation and evaluation, predictive modeling, match-up models, game simulation, and in-game decision making. Statistics and Data Sciences 365 (Topic 1) and 375 (Topic: Sports Analytics) may not both be counted. Additional prerequisite: Statistics and Data Sciences 320E and 322E with grades of at least C-; or Statistics and Data Sciences 313 and 315 with grades of at least C-.
Topic 2: Data Product Development. Learn the fundamentals of creating data products using technologies like Shiny, R packages, and interactive graphics tools. Explore interactive maps, machine learning application programming interfaces, dashboards, and data storytelling. Focus on both the statistical details and the design process for building effective data products. Statistics and Data Sciences 365 (Topic 2) and 375 (Topic: Data Product Development) may not both be counted. Additional prerequisite: Computer Science 303E, Statistics and Data Sciences 320E, 322E, and Statistics and Data Sciences 324E or 326E; or Computer Science 303E and Statistics and Data Sciences 334.
SDS 365.1 Sports Analytics 3 Hours
3 Lecture Hours 0 Lab Hours
Explore fundamental principles and methods of sports analytics. Participate in practical experience in collecting and analyzing sports data. Discuss data collection, metric estimation and evaluation, predictive modeling, match-up models, game simulation, and in-game decision making. Statistics and Data Sciences 365 (Topic 1) and 375 (Topic: Sports Analytics) may not both be counted.
Pre/Corequisites: Statistics and Data Sciences 320E and 322E with grades of at least C-; or Statistics and Data Sciences 313 and 315 with grades of at least C-.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 365.2 Data Product Development 3 Hours
3 Lecture Hours 0 Lab Hours
Learn the fundamentals of creating data products using technologies like Shiny, R packages, and interactive graphics tools. Explore interactive maps, machine learning application programming interfaces, dashboards, and data storytelling. Focus on both the statistical details and the design process for building effective data products. Statistics and Data Sciences 365 (Topic 2) and 375 (Topic: Data Product Development) may not both be counted.
Pre/Corequisites: Computer Science 303E, Statistics and Data Sciences 320E, 322E, and Statistics and Data Sciences 324E or 326E; or Computer Science 303E and Statistics and Data Sciences 334.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 366 Data Visualization 3 Hours
3 Lecture Hours 0 Lab Hours
Explore how to visualize data sets. Reason about, and communicate with, data visualizations. Statistics and Data Sciences 366 and 375 (Topic: Data Viz in R) may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: SDS 320E and SDS 322E with grades of at least C-; or SDS 313 and SDS 315 with grades of at least C-.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 368 Statistical Theory 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to the mathematical theory of statistics. Explore maximum likelihood estimation, confidence intervals, hypothesis tests and statistical decision theory, tail and concentration bounds, concentration of measure, and nonparametric statistics. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Statistics and Data Sciences 431 with a grade of at least C-; Mathematics 340L or 341 or Statistics and Data Sciences 329C with a grade of at least C-; and credit with a grade of at least C- or registration for Statistics and Data Sciences 334; and a solid foundation in calculus, probability theory, and linear algebra.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 374C Parallel Computing for Science and Engineering 3 Hours
3 Lecture Hours 0 Lab Hours
Study of parallel computing principles, architectures, and technologies; and parallel application development, performance, and scalability. Designed to help prepare students to formulate and develop parallel algorithms to implement effective applications for parallel computing systems. Statistics and Data Sciences 374C and Statistics and Scientific Computation 374C may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: Mathematics 408D or 408M, 340L, and prior programming experience using C or Fortran on Linux or Unix systems.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 374E Visualization and Data Analysis for Science and Engineering 3 Hours
3 Lecture Hours 0 Lab Hours
Scientific visualization principles, practices, and technologies, including remote and collaborative visualization. Introduces statistical analysis, data mining, and feature detection. Statistics and Data Sciences 374E and Statistics and Scientific Computation 374E may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: Mathematics 408D or 408M, 340, and prior programming experience using C or Fortran on Linux or Unix systems.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 375 Special Topics in Scientific Computation 3 Hours
3 Lecture Hours 0 Lab Hours
Statistics and Data Sciences 375 and Statistics and Scientific Computation 375 may not both be counted unless topics vary. Three lecture hours a week for one semester.
Pre/Corequisites: Upper-division standing; additional prerequisites may vary with the topic.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Upper Division
SDS 378 Introduction to Mathematical Statistics 3 Hours
3 Lecture Hours 0 Lab Hours
Sampling distributions of statistics, estimation of parameters (confidence intervals, method of moments, maximum likelihood, comparison of estimators using mean square error and efficiency, sufficient statistics), hypothesis tests (p-values, power, likelihood ratio tests), and other topics. Mathematics 378K and Statistics and Data Sciences 378 may not both be counted. Three lecture hours a week for one semester.
Same as: M 378K
Pre/Corequisites: Mathematics 362K with a grade of at least C-.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 378P Decision Analytics 3 Hours
3 Lecture Hours 0 Lab Hours
Examine decision theory with utility functions, including the use of probability, optimization, constrained optimization, and linear algebra. Only one of the following may be counted: Mathematics 375T (Topic: Decision Analytics), 378P, Statistics and Data Sciences 378P. Three lecture hours a week for one semester.
Pre/Corequisites: Mathematics 362K and Mathematics 378K with a grade of at least C-, or consent of the instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
SDS 379H Honors Undergraduate Research 3 Hours
3 Lecture Hours 0 Lab Hours
Design an individual research project under the supervision of one or more faculty members. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Upper-division standing and consent of instructor.
Restrictions: Restricted to statistics and data science honors students.
Grading: Pass/Fail
Repeatable for credit: Yes (Unlimited)
Academic Level: Upper Division
SDS 179R, 279R, 379R, 479R Undergraduate Research 1-4 Hours
1,2,3,4 Lecture Hours 0 Lab Hours
Students work on an individual research project under the supervision of one or more faculty members. For each semester hour of credit earned, the equivalent of one lecture hour a week for one semester.
Pre/Corequisites: Upper-division standing, consent of instructor, and approval by the SDS undergraduate major faculty advisor.
Grading: Student Option
Repeatable for credit: Yes (Unlimited)
Academic Level: Upper Division
SDS 380C Statistical Methods I 3 Hours
3 Lecture Hours 0 Lab Hours
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. Statistics and Data Sciences 380C and Statistics and Scientific Computation 380C may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
SDS 380D Statistical Methods II 3 Hours
3 Lecture Hours 0 Lab Hours
Continuation of Statistics and Data Sciences 380C (or Statistics and Scientific Computation 380C). Surveys advanced statistical modeling and 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. Statistics and Data Sciences 380D and Statistics and Scientific Computation 380D may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing, and Statistics and Data Sciences 380C (or Statistics and Scientific Computation 380C) or the equivalent.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
SDS 381M Topics in Statistics and Data Science Foundations 3 Hours
3 Lecture Hours 0 Lab Hours
Examine core concepts of statistics and data science. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing; Calculus, linear algebra, and introductory statistics course; or consent of instructor. Additional prerequisites vary with the topic.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Master's
SDS 383C Statistical Modeling I 3 Hours
3 Lecture Hours 0 Lab Hours
An 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. Only one of the following may be counted: Statistics and Data Sciences 391P (Topic 1), Statistics and Data Sciences 383C, Statistics and Scientific Computation 383C. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Restrictions: Restricted to students in the PhD Statistics program.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 383D Statistical Modeling II 3 Hours
3 Lecture Hours 0 Lab Hours
Use of structured, probabilistic models that incorporate multiple layers of uncertainty to describe real-world systems. Topics include multivariate normal distribution, mixture models, nonparametric Bayesian analysis, advanced hierarchical models and latent-variable models, generalized linear models, and advanced topics in linear and nonlinear regression. Only one of the following may be counted: Statistics and Data Sciences 391P (Topic 2), Statistics and Data Sciences 383D, Statistics and Scientific Computation 383D. Three lecture hours per week for one semester.
Pre/Corequisites: Graduate standing; Economics 392M (Topic 19), Statistics and Data Sciences 384 (or Statistics and Scientific Computation 384), or the equivalent; and 383C (or Statistics and Scientific Computation 383C).
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 183K Topics in Data Analysis Applications 1 Hour
1 Lecture Hours 0 Lab Hours
Introduction to the use of statistical or mathematical applications for data analysis. Statistics and Data Sciences 183K and Statistics and Scientific Computation 183K may not both be counted unless the topics vary. Two lecture hours a week for eight weeks.
Pre/Corequisites: Graduate standing.
Grading: Credit No Credit
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Master's
Topic 1: SPSS Software. Statistics and Data Sciences 183K (Topic 1) and Statistics and Scientific Computation 183K (Topic 1) may not both be counted.
Topic 2: SAS Software. Statistics and Data Sciences 183K (Topic 2) and Statistics and Scientific Computation 183K (Topic 2) may not both be counted.
Topic 3: Stata Software. Statistics and Data Sciences 183K (Topic 3) and Statistics and Scientific Computation 183K (Topic 3) may not both be counted.
Topic 4: The R Software Environment. Statistics and Data Sciences 183K (Topic 4) and Statistics and Scientific Computation 183K (Topic 4) may not both be counted.
SDS 384 Topics in Statistics and Probability 3 Hours
3 Lecture Hours 0 Lab Hours
Concepts of probability and mathematical statistics with applications in data analysis and research. Statistics and Data Sciences 384 and Statistics and Scientific Computation 384 may not both be counted unless topics vary. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing; and Statistics and Data Sciences 382 (or Statistics and Scientific Computation 382), an introductory probability course and a statistics course, or consent of instructor.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Doctoral
Topic 1: Applied Probability. Basic probability theory, combinatorial analysis of random phenomena, conditional probability and independence, parametric families of distributions, expectation, distribution of functions of random variables, and limit theorems. Statistics and Data Sciences 384 (Topic 1) and Statistics and Scientific Computation 384 (Topic 1) may not both be counted.
Topic 2: Mathematical Statistics I. The general theory of mathematical statistics. Includes distributions of functions of random variables, properties of a random sample, principles of data reduction, an overview of hierarchical models, decision theory, Bayesian statistics, and theoretical results relevant to point estimation, interval estimation, and hypothesis testing. Only one of the following may be counted: Computational Science, Engineering, and Mathematics 384R, Mathematics 384C, Statistics and Data Sciences 384 (Topic 2). Three lecture hours a week for one semester. Same as: CSE 384R, M 384C Additional prerequisite: Graduate standing; and Mathematics 362K and 378K, or consent of instructor.
Topic 3: Mathematical Statistics II. Continuation of Computational Science, Engineering, and Mathematics 384R and Mathematics 384C. Only one of the following may be counted: Computational Science, Engineering, and Mathematics 384S, Mathematics 384D, Statistics and Data Sciences 384 (Topic 3). Three lecture hours a week for one semester. Same as: CSE 384S, M 384D Additional prerequisite: Graduate standing; Computational Science, Engineering, and Mathematics 384R, or Mathematics 384C; and Mathematics 362K and 378K, Statistics and Data Sciences 382, or consent of instructor.
Topic 4: Regression Analysis. 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. Only one of the following may be counted: Computational Science, Engineering, and Mathematics 384T, Mathematics 384G, Statistics and Data Sciences 384 (Topic 4). Three lecture hours a week for one semester. Same as: CSE 384T, M 384G Additional prerequisite: Graduate standing; and Mathematics 362K and 378K, Statistics and Data Sciences 382, or consent of instructor.
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; and split plot designs. Only one of the following may be counted: Computational Science, Engineering, and Mathematics 384U, Mathematics 384E, Statistics and Data Sciences 384 (Topic 6). Three lecture hours a week for one semester. Same as: CSE 384U, M 384E Additional prerequisite: Graduate standing; and Mathematics 362K and 378K, Statistics and Data Sciences 382, or consent of instructor.
Topic 7: Bayesian Statistical Methods. 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. Statistics and Data Sciences 384 (Topic 7) and Statistics and Scientific Computation 384 (Topic 7) may not both be counted.
Topic 8: Time Series Analysis. Introduction to statistical time series analysis. Includes autoregressive integrated moving average (ARIMA) and more general models, forecasting, spectral analysis, time domain regression, model identification, estimation of parameters, and diagnostic checking. Statistics and Data Sciences 384 (Topic 8) and Statistics and Scientific Computation 384 (Topic 8) may not both be counted. Additional prerequisite: Mathematics 384D.
Topic 9: Computational Statistics. Modern, computation intensive statistical methods, including simulation, optimization methods, Monte Carlo integration, maximum likelihood estimation and expectation-maximization parameter estimation, Markov chain Monte Carlo methods, resampling methods, and nonparametric density estimation. Statistics and Data Sciences 384 (Topic 9) and Statistics and Scientific Computation 384 (Topic 9) may not both be counted.
Topic 10: Stochastic Processes. Concepts and techniques of stochastic processes, with emphasis on the nature of change of variables with respect to time. Includes characterization, structural properties, and inference. Statistics and Data Sciences 384 (Topic 10) and Statistics and Scientific Computation 384 (Topic 10) may not both be counted.
Topic 11: Theoretical Statistics. Examination of asymptotic theory and empirical processes. The former would include minimax theory, Bernstein von Mises theorem, and Bayesian asymptotics. The latter, would include U statistics and robust estimation. Statistics and Data Sciences 391P (Topic 6) and 384 (Topic 11) may not both be counted. Additional prerequisite: Statistics and Data Sciences 384 (Topic 3) or the equivalent; and advanced probability.
SDS 384.1 Applied Probability 3 Hours
3 Lecture Hours 0 Lab Hours
Basic probability theory, combinatorial analysis of random phenomena, conditional probability and independence, parametric families of distributions, expectation, distribution of functions of random variables, and limit theorems. Statistics and Data Sciences 384 (Topic 1) and Statistics and Scientific Computation 384 (Topic 1) may not both be counted.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 384.10 Stochastic Processes 3 Hours
3 Lecture Hours 0 Lab Hours
Concepts and techniques of stochastic processes, with emphasis on the nature of change of variables with respect to time. Includes characterization, structural properties, and inference. Statistics and Data Sciences 384 (Topic 10) and Statistics and Scientific Computation 384 (Topic 10) may not both be counted.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 384.11 Theoretical Statistics 3 Hours
3 Lecture Hours 0 Lab Hours
Examination of asymptotic theory and empirical processes. The former would include minimax theory, Bernstein von Mises theorem, and Bayesian asymptotics. The latter, would include U statistics and robust estimation. Statistics and Data Sciences 391P (Topic 6) and 384 (Topic 11) may not both be counted.
Pre/Corequisites: Statistics and Data Sciences 384 (Topic 3) or the equivalent; and advanced probability.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 384.2 Mathematical Statistics I 3 Hours
3 Lecture Hours 0 Lab Hours
The general theory of mathematical statistics. Includes distributions of functions of random variables, properties of a random sample, principles of data reduction, an overview of hierarchical models, decision theory, Bayesian statistics, and theoretical results relevant to point estimation, interval estimation, and hypothesis testing. Only one of the following may be counted: Computational Science, Engineering, and Mathematics 384R, Mathematics 384C, Statistics and Data Sciences 384 (Topic 2). Three lecture hours a week for one semester.
Same as: CSE 384R, M 384C
Pre/Corequisites: Graduate standing; and Mathematics 362K and 378K, or consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 384.3 Mathematical Statistics II 3 Hours
3 Lecture Hours 0 Lab Hours
Continuation of Computational Science, Engineering, and Mathematics 384R and Mathematics 384C. Only one of the following may be counted: Computational Science, Engineering, and Mathematics 384S, Mathematics 384D, Statistics and Data Sciences 384 (Topic 3). Three lecture hours a week for one semester.
Same as: CSE 384S, M 384D
Pre/Corequisites: Graduate standing; Computational Science, Engineering, and Mathematics 384R, or Mathematics 384C; and Mathematics 362K and 378K, Statistics and Data Sciences 382, or consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 384.4 Regression Analysis 3 Hours
3 Lecture Hours 0 Lab Hours
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. Only one of the following may be counted: Computational Science, Engineering, and Mathematics 384T, Mathematics 384G, Statistics and Data Sciences 384 (Topic 4). Three lecture hours a week for one semester.
Same as: CSE 384T, M 384G
Pre/Corequisites: Graduate standing; and Mathematics 362K and 378K, Statistics and Data Sciences 382, or consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 384.6 Design and Analysis of Experiments 3 Hours
3 Lecture Hours 0 Lab Hours
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; and split plot designs. Only one of the following may be counted: Computational Science, Engineering, and Mathematics 384U, Mathematics 384E, Statistics and Data Sciences 384 (Topic 6). Three lecture hours a week for one semester.
Same as: CSE 384U, M 384E
Pre/Corequisites: Graduate standing; and Mathematics 362K and 378K, Statistics and Data Sciences 382, or consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 384.7 Bayesian Statistical Methods 3 Hours
3 Lecture Hours 0 Lab Hours
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. Statistics and Data Sciences 384 (Topic 7) and Statistics and Scientific Computation 384 (Topic 7) may not both be counted.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 384.8 Time Series Analysis 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to statistical time series analysis. Includes autoregressive integrated moving average (ARIMA) and more general models, forecasting, spectral analysis, time domain regression, model identification, estimation of parameters, and diagnostic checking. Statistics and Data Sciences 384 (Topic 8) and Statistics and Scientific Computation 384 (Topic 8) may not both be counted.
Pre/Corequisites: Mathematics 384D.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 384.9 Computational Statistics 3 Hours
3 Lecture Hours 0 Lab Hours
Modern, computation intensive statistical methods, including simulation, optimization methods, Monte Carlo integration, maximum likelihood estimation and expectation-maximization parameter estimation, Markov chain Monte Carlo methods, resampling methods, and nonparametric density estimation. Statistics and Data Sciences 384 (Topic 9) and Statistics and Scientific Computation 384 (Topic 9) may not both be counted.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 385 Topics in Applied Statistics 3 Hours
3 Lecture Hours 0 Lab Hours
Theories, models, and methods for the analysis of quantitative data. Statistics and Data Sciences 385 and Statistics and Scientific Computation 385 may not both be counted unless topics vary. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing; and Statistics and Data Sciences 380C (or Statistics and Scientific Computation 380C), 382 (or Statistics and Scientific Computation 382), or consent of instructor.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Doctoral
Topic 1: Experimental Design. Principles, construction, and analysis of experimental designs. Includes one-way classification, randomized blocks, Latin squares, factorial and nested designs, fixed and random effects, multiple comparisons, and analysis of covariance. Statistics and Data Sciences 385 (Topic 1) and Statistics and Scientific Computation 385 (Topic 1) may not both be counted.
Topic 2: Applied Regression. Examination of the foundation of regression analysis. Subjects include simple and multiple linear regression, residual analysis, transformations, building models with real data, testing models, logistic regression, and panel data analysis. Statistics and Data Sciences 385 (Topic 2) and Statistics and Scientific Computation 385 (Topic 2) may not both be counted. Additional prerequisite: Graduate standing, and one of the following: Public Affairs 397, Statistics and Data Sciences 380C (or Statistics and Scientific Computation 380C), 382 (or Statistics and Scientific Computation 382), or consent of instructor.
Topic 3: Applied Multivariate Methods. Introduction to the analysis of multivariate data as applied to examples from the social sciences. Includes multivariate linear models, principal components and factor analysis, discriminant analysis, clustering, and canonical correlation. Statistics and Data Sciences 385 (Topic 3) and Statistics and Scientific Computation 385 (Topic 3) may not both be counted. Additional prerequisite: Statistics and Data Sciences 385 (Topic 2) (or Statistics and Scientific Computation 385 (Topic 2)), or the equivalent.
Topic 4: Analysis of Categorical Data. Methods for analyzing categorical data. Includes categorical explanatory variables within the general linear model, models of association among categorical variables, and models in which the response variable is categorical or is a count. Emphasis on logical similarities across methods. Statistics and Data Sciences 385 (Topic 4) and Statistics and Scientific Computation 385 (Topic 4) may not both be counted.
Topic 5: Structural Equation Modeling. Introduction to the basic concepts, methods, and computing tools used in structural equation modeling. Designed to help students develop a working familiarity with some common statistical procedures and their application through the use of statistical software. Statistics and Data Sciences 385 (Topic 5) and Statistics and Scientific Computation 385 (Topic 5) may not both be counted. Additional prerequisite: Statistics and Data Sciences 385 (Topic 2) (or Statistics and Scientific Computation 385 (Topic 2)), or the equivalent or consent of instructor.
Topic 6: Hierarchical Linear Models. Introduction to multilevel data structures, model building and testing, effect size, fixed and random effects, missing data and model assumptions, hierarchical linear modeling (HLM) logistics, statistical power, and design planning. Statistics and Data Sciences 385 (Topic 6) and Statistics and Scientific Computation 385 (Topic 6) may not both be counted. Additional prerequisite: Statistics and Data Sciences 385 (Topic 2) (or Statistics and Scientific Computation 385 (Topic 2)), or the equivalent or consent of instructor.
Topic 7: Survey Sampling and Methodology. Survey planning, execution, and analysis. Includes the principles of survey research, including sampling and measurement; questionnaire construction and distribution; response effects; validity and reliability; scaling data sources; and data reduction and analysis. Statistics and Data Sciences 385 (Topic 7) and Statistics and Scientific Computation 385 (Topic 7) may not both be counted.
Topic 8: Introduction to Bayesian Methods. A practical introduction to Bayesian statistical interference, with an emphasis on applications in behavioral and measurement research. Examines how Bayesian statistical inference differs from classical inference in the context of simple statistical procedures and models, such as hypothesis testing, analysis of variance (ANOVA), and regression. Statistics and Data Sciences 385 (Topic 8) and Statistics and Scientific Computation 385 (Topic 8) may not both be counted. Additional prerequisite: Statistics and Data Sciences 385 (Topic 2) (or Statistics and Scientific Computation 385 (Topic 2)) or the equivalent, or consent of instructor.
Topic 9: Longitudinal Data Analysis. Applications of models to data collected at successive points in time. Includes latent growth curve models, models for nonlinear growth, discrete-time and continuous-time event history models, multilevel models for change, random coefficient models, and applications of models to event-occurrence data. Statistics and Data Sciences 385 (Topic 9) and Statistics and Scientific Computation 385 (Topic 9) may not both be counted.
Topic 10: Modern Statistical Methods. Introduction to conducting statistical analysis using modern resampling methods, including bootstrapping and Monte Carlo simulation. Emphasis on theoretical understanding and application. Statistics and Data Sciences 385 (Topic 10) and Statistics and Scientific Computation 385 (Topic 10) may not both be counted.
Topic 11: Applied Mathematical Statistics. Introduction to the basic concepts of probability and mathematical statistics. Includes probability distributions and estimation theory and hypothesis testing techniques. Designed for doctoral students who plan to use statistical methods in their research but do not require a highly mathematical investigation of the subject. Statistics and Data Sciences 385 (Topic 11) and Statistics and Scientific Computation 385 (Topic 11) may not both be counted. Additional prerequisite: A calculus course covering integration and differentiation.
Topic 12: Meta-Analysis. Introduction to the statistics used to synthesize results from a set of studies. May include calculation of different effect sizes, calculating pooled estimates using fixed and random effects models, testing moderating variables using fixed and mixed effects models, testing heterogeneity of effect sizes, and assessing and correcting publication bias. Statistics and Data Sciences 385 (Topic 12) and Statistics and Scientific Computation 385 (Topic 12) may not both be counted.
Topic 13: Factor Analysis. Introduction to exploratory and confirmatory factor analysis. May include review of matrix algebra and vector geometry, principal components and principal axis factoring, and factor rotation methods, as well as single-factor and multiple-factor multisample models, multitrait-multimethod technique, and latent means modeling. Emphasis on critiquing current research. Statistics and Data Sciences 385 (Topic 13) and Statistics and Scientific Computation 385 (Topic 13) may not both be counted. Additional prerequisite: Statistics and Data Sciences 385 (Topic 2) (or Statistics and Scientific Computation 385 (Topic 2)), or the equivalent or consent of instructor.
Topic 14: Maximum-Likelihood Statistics. 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. Statistics and Data Sciences 385 (Topic 14) and Statistics and Scientific Computation 385 (Topic 14) may not both be counted.
Topic 15: Survival Analysis and Duration Modeling. Focuses on the statistical methods related to the analysis of survival or of time to event data. Emphasis on practical applications in medicine, biology, economics, criminology, sociology, and engineering. May include Kaplan-Meier estimators, semiparametric and parametric regression models, model development, and model adequacy assessment. Statistics and Data Sciences 385 (Topic 15) and Statistics and Scientific Computation 385 (Topic 15) may not both be counted.
Topic 16: Statistical Models for Big Data. An introduction to approaches for modeling large data sets in the biological, social, and physical sciences. Subjects include: linear and generalized-linear models for very large data sets, graphical models, matrix factorization, latent-variable models, large-scale spatial smoothing, online learning, multiple testing, and convex optimization with applications to data analysis. Additional prerequisite: Basic knowledge of linear algebra and multivariable calculus; knowledge of basic probability and statistics at the level of Statistics and Data Sciences 321 and Mathematics 358K; some familiarity with linear regression modeling; programming experience in R, Python, Matlab, or a similar language.
SDS 386M Topics in Statistics and Data Science Extensions 3 Hours
3 Lecture Hours 0 Lab Hours
Examine concepts of statistics and data science. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing; Calculus, linear algebra, and introductory statistics course; or consent of instructor. Additional prerequisites vary with the topic.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Master's
Topic 1: Data Visualization. Statistics and Data Sciences 386M (Topic 1) and 395 (Topic: Data Viz in R) may not both be counted.
SDS 386M.1 Data Visualization 3 Hours
3 Lecture Hours 0 Lab Hours
Statistics and Data Sciences 386M (Topic 1) and 395 (Topic: Data Viz in R) may not both be counted.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
SDS 387 Linear Models 3 Hours
3 Lecture Hours 0 Lab Hours
An exploration of practical applications of the projection approach to linear models, building from a review of essential linear algebra concepts to the theory of linear models from a projection-based perspective. Introduction to Bayesian ideas. Additional topics include analysis of variance, generalized linear models, and variable selection techniques. Statistics and Data Sciences 387 and Statistics and Scientific Computation 387 may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing; Economics 392M (Topic 19: Probability and Statistics), Statistics and Data Sciences 384 (or Statistics and Scientific Computation 384), or the equivalent; and basic coding skills in R, Matlab, or Stata.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 189R, 289R, 389R, 489R Graduate Research 1-4 Hours
1,2,3,4 Lecture Hours 0 Lab Hours
Individual research project supervised by one or more faculty members. For each semester hour of credit earned, the equivalent of one lecture hour a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: Yes (Unlimited)
Academic Level: Master's
SDS 190 Readings in Statistics 1 Hour
1 Lecture Hours 0 Lab Hours
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.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: Yes (Unlimited)
Academic Level: Doctoral
SDS 391P Topics in Statistics and Data Science Foundations 3 Hours
3 Lecture Hours 0 Lab Hours
Examine advanced core concepts of statistics and data science. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing. Additional prerequisites vary with the topic.
Restrictions: Restricted to students in the PhD statistics program.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Doctoral
Topic 1: Advanced Statistical Modeling and Applications I. Only one of the following may be counted: Statistics and Data Sciences 391P (Topic 1), Statistics and Data Sciences 383C, Statistics and Scientific Computation 383C.
Topic 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 (Topic 2), Statistics and Data Sciences 383D, Statistics and Scientific Computation 383D. Additional prerequisite: Statistics and Data Sciences 383C or 381P.1; or consent of instructor.
Topic 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.
Topic 4: Computational Inference. Examine computational methods for inference in statistical models. Explore inference methods, their properties, and their applicability. Discuss deterministic and stochastic optimization, Monte Carlo methods, and variational inference. Statistics and Data Sciences 391P (Topic 4) and 386D may not both be counted. Three lecture hours a week for one semester. Additional prerequisite: Statistics and Data Sciences 391P (Topic 1) or 383D or consent of instructor.
Topic 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, and their asymptotic properties.
Topic 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. Statistics and Data Sciences 391P (Topic 6) and 384 (Topic 11) may not both be counted. Additional prerequisite: Statistics and Data Sciences 391P (Topic 5) or Statistics and Data Sciences 384 (Topic 3) or equivalent; or consent of instructor.
SDS 391P.1 Advanced Statistical Modeling and Applications I 3 Hours
3 Lecture Hours 0 Lab Hours
Only one of the following may be counted: Statistics and Data Sciences 391P (Topic 1), Statistics and Data Sciences 383C, Statistics and Scientific Computation 383C.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 391P.2 Advanced Statistical Modeling and Applications II 3 Hours
3 Lecture Hours 0 Lab Hours
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 (Topic 2), Statistics and Data Sciences 383D, Statistics and Scientific Computation 383D.
Pre/Corequisites: Statistics and Data Sciences 383C or 381P.1; or consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 391P.3 Theory of the Linear Model 3 Hours
3 Lecture Hours 0 Lab Hours
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.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 391P.4 Computational Inference 3 Hours
3 Lecture Hours 0 Lab Hours
Examine computational methods for inference in statistical models. Explore inference methods, their properties, and their applicability. Discuss deterministic and stochastic optimization, Monte Carlo methods, and variational inference. Statistics and Data Sciences 391P (Topic 4) and 386D may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: Statistics and Data Sciences 391P (Topic 1) or 383D or consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 391P.5 Concepts in Mathematical Statistics 3 Hours
3 Lecture Hours 0 Lab Hours
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, and their asymptotic properties.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 391P.6 Theoretical Statistics and Machine Learning 3 Hours
3 Lecture Hours 0 Lab Hours
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. Statistics and Data Sciences 391P (Topic 6) and 384 (Topic 11) may not both be counted.
Pre/Corequisites: Statistics and Data Sciences 391P (Topic 5) or Statistics and Data Sciences 384 (Topic 3) or equivalent; or consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 393C Numerical Analysis: Linear Algebra 3 Hours
3 Lecture Hours 0 Lab Hours
Survey of numerical methods in linear algebra: floating-point computation, solution of linear equations, least squares problems, algebraic eigenvalue problems. Only one of the following may be counted: Computational Science, Engineering, and Mathematics 383C, Computer Science 383C, Mathematics 383E, Statistics and Data Sciences 393C. Three lecture hours a week for one semester.
Same as: C S 383C, CSE 383C, M 383E
Pre/Corequisites: Graduate standing; Computer Science 367 or Mathematics 368K; and Mathematics 340L, 341, or consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 393D Numerical Analysis: Interpolation, Approximation, Quadrature, and Differential Equations 3 Hours
3 Lecture Hours 0 Lab Hours
Survey of numerical methods for interpolation, functional approximation, integration, and solution of differential equations. Only one of the following may be counted: Computational Science, Engineering, and Mathematics 383D, Computer Science 383D, Mathematics 383F, Statistics and Data Sciences 393D. Three lecture hours a week for one semester.
Same as: C S 383D, CSE 383D, M 383F
Pre/Corequisites: Graduate standing; Computational Science, Engineering, and Mathematics 383C, Computer Science 383C, Mathematics 383E, or Statistics and Data Sciences 393C; and Mathematics 427K and 365C, or consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 396P Topics in Statistics and Data Science Extensions 3 Hours
3 Lecture Hours 0 Lab Hours
Examine advanced concepts of statistics and data science. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing. Additional prerequisites vary with the topic.
Restrictions: Restricted to students in the PhD statistics program.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Doctoral
Topic 1: Statistical Machine Learning Optimization. Introduction to machine learning and optimization methods from a statistical perspective with an emphasis on mathematical theory and applications. Statistics and Data Sciences 396P.1 and 384 (Topic: Stat Mach Learng Optimization) may not both be counted. Three lecture hours a week for one semester.
SDS 396P.1 Statistical Machine Learning Optimization 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to machine learning and optimization methods from a statistical perspective with an emphasis on mathematical theory and applications. Statistics and Data Sciences 396P.1 and 384 (Topic: Stat Mach Learng Optimization) may not both be counted. Three lecture hours a week for one semester.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
SDS 398R Master's Report 3 Hours
3 Lecture Hours 0 Lab Hours
Preparation of a report to fulfill the requirement for the master's degree under the report option. Statistics and Data Sciences 398R and Statistics and Scientific Computation 398R may not both be counted. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing in statistics and data sciences, consent of supervising professor, and consent of graduate adviser.
Grading: Credit No Credit
Repeatable for credit: No
Academic Level: Doctoral
SDS 398T Supervised Teaching in Statistics and Data Sciences 3 Hours
3 Lecture Hours 0 Lab Hours
Supervised teaching experience; weekly group meetings, individual consultations, and reports. Statistics and Data Sciences 398T and Statistics and Scientific Computation 398T may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing and appointment as a teaching assistant.
Grading: Credit No Credit
Repeatable for credit: No
Academic Level: Master's
SDS 399W, 699W, 999W Dissertation 3,6,9 Hours
3,6,9 Lecture Hours 0 Lab Hours
Statistics and Data Sciences 399W, 699W, 999W and Statistics and Scientific Computation 399W, 699W, 999W may not both be counted.
Pre/Corequisites: Admission to candidacy for the doctoral degree.
Grading: Credit No Credit
Repeatable for credit: Yes (Unlimited)
Academic Level: Doctoral