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Introduces reproducible data management, modeling, analysis, and statistical inference through a practical, hands-on case studies approach. Topics include the use of an integrated statistical computing environment, data wrangling, the R programming language, data graphics and visualization, random variables and concepts of probability including the binomial and normal distributions, data modeling, statistical inference in one- and two- sample settings for proportions and means, simple linear regression, and report generation using R Markdown with applications to a wide variety of data to address open-ended questions. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Satisfied Quantitative Reasoning (QR) A requirementCourse Designation: Gen Ed - Quantitative Reasoning Part B Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. wrangle data: transform data, possibly from multiple sources, into a form convenient for analysis Audience: Undergraduate 2. explore data: visualize and summarize data, generate questions/hypotheses, and address them Audience: Undergraduate 3. program: write R code using the R Studio integrated statistical computing environment to carry out reproducible data analysis Audience: Undergraduate 4. model data: use probability and random variables in statistical computing environment to carry out reproducible data analysis Audience: Undergraduate 5. interpret data: explain what can be inferred from the data analysis and make predictions Audience: Undergraduate 6. communicate: use R Markdown to integrate prose, visualizations, code, interpretation, and results Audience: Undergraduate 7. collaborate: work with other students to solve data challenges Audience: Undergraduate 8. make statistical inferences: employ confidence intervals and hypothesis tests using both computational and standard methods such as binomial and t-tests Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock gradcap "> STAT 301 — INTRODUCTION TO STATISTICAL METHODS 3 credits. Distributions, measures of central tendency, dispersion and shape, the normal distribution; experiments to compare means, standard errors, confidence intervals; effects of departure from assumption; method of least squares, regression, correlation, assumptions and limitations; basic ideas of experimental design. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Satisfied Quantitative Reasoning (QR) A requirement. Not open to students with credit for STAT 302, <a href="/search/?P=STAT%20324" title="STAT 324" class="bubblelink code" onclick="return showCourse(this, 'STAT 324');">324</a>, or <a href="/search/?P=STAT%20371" title="STAT 371" class="bubblelink code" onclick="return showCourse(this, 'STAT 371');">371</a>Course Designation: Gen Ed - Quantitative Reasoning Part B Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 303 — R FOR STATISTICS I 1 credit. An understanding of the commonly used statistical language R. Topics will include using R to manipulate data and perform exploratory data analysis. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20240" title="STAT 240" class="bubblelink code" onclick="return showCourse(this, 'STAT 240');">STAT 240</a>, <a href="/search/?P=STAT%20301" title="STAT 301" class="bubblelink code" onclick="return showCourse(this, 'STAT 301');">301</a>, 302, <a href="/search/?P=STAT%20312" title="STAT 312" class="bubblelink code" onclick="return showCourse(this, 'STAT 312');">312</a>, <a href="/search/?P=STAT%20324" title="STAT 324" class="bubblelink code" onclick="return showCourse(this, 'STAT 324');">324</a>, <a href="/search/?P=STAT%20371" title="STAT 371" class="bubblelink code" onclick="return showCourse(this, 'STAT 371');">371</a>, <a href="/search/?P=MATH%20310" title="MATH/STAT 310" class="bubblelink code" onclick="return showCourse(this, 'MATH 310');">MATH/STAT 310</a>, <a href="/search/?P=ECON%20310" title="ECON 310" class="bubblelink code" onclick="return showCourse(this, 'ECON 310');">ECON 310</a>, GEN BUS 303, 304, <a href="/search/?P=GEN%20BUS%20306" title="GEN BUS 306" class="bubblelink code" onclick="return showCourse(this, 'GEN BUS 306');">306</a>, <a href="/search/?P=GEN%20BUS%20307" title="GEN BUS 307" class="bubblelink code" onclick="return showCourse(this, 'GEN BUS 307');">307</a>, <a href="/search/?P=GEN%20BUS%20317" title="GEN BUS 317" class="bubblelink code" onclick="return showCourse(this, 'GEN BUS 317');">317</a>, <a href="/search/?P=PSYCH%20210" title="PSYCH 210" class="bubblelink code" onclick="return showCourse(this, 'PSYCH 210');">PSYCH 210</a>, <a href="/search/?P=B%20M%20E%20325" title="B M E 325" class="bubblelink code" onclick="return showCourse(this, 'B M E 325');">B M E 325</a>, <a href="/search/?P=I%20SY%20E%20210" title="I SY E 210" class="bubblelink code" onclick="return showCourse(this, 'I SY E 210');">I SY E 210</a>, <a href="/search/?P=SOC%20360" title="SOC/C&E SOC 360" class="bubblelink code" onclick="return showCourse(this, 'SOC 360');">SOC/C&E SOC 360</a>, graduate/professional standing, or declared in Statistics VISPCourse Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Use basic R vocabulary Audience: Undergraduate 2. Manipulate data in R Audience: Undergraduate 3. Produce graphics and reports Audience: Undergraduate 4. Apply statistical methods Audience: Undergraduate 5. Run basic simulations Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 304 — R FOR STATISTICS II 1 credit. Provides an understanding of the commonly used statistical language R. Topics will include writing conditional expressions, loops, and functions; manipulating data matrices and arrays; extracting data from text; and making high level visualizations of data. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20303" title="STAT 303" class="bubblelink code" onclick="return showCourse(this, 'STAT 303');">STAT 303</a>Course Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 305 — R FOR STATISTICS III 1 credit. Provides an understanding of the commonly used statistical language R. Learn to combine R with high performance computing tools to do scientific computing. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20304" title="STAT 304" class="bubblelink code" onclick="return showCourse(this, 'STAT 304');">STAT 304</a>Course Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/MATH 309 — INTRODUCTION TO PROBABILITY AND MATHEMATICAL STATISTICS I 3 credits. Probability and combinatorial methods, discrete and continuous, univariate and multivariate distributions, expected values, moments, normal distribution and derived distributions, estimation. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=MATH%20234" title="MATH 234" class="bubblelink code" onclick="return showCourse(this, 'MATH 234');">MATH 234</a>, <a href="/search/?P=MATH%20376" title="MATH 376" class="bubblelink code" onclick="return showCourse(this, 'MATH 376');">376</a>, or concurrent enrollment. Not open to students with credit for <a href="/search/?P=STAT%20431" title="STAT/MATH 431" class="bubblelink code" onclick="return showCourse(this, 'STAT 431');">STAT/MATH 431</a> or <a href="/search/?P=STAT%20311" title="STAT 311" class="bubblelink code" onclick="return showCourse(this, 'STAT 311');">STAT 311</a>Course Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Recall the definitions of fundamental objects and concepts underlying probability theory (e.g. sample spaces and events, the axioms of probability, the notions of conditional probability and independence, random variables and their probability distributions, mathematical expectation, and the joint distribution of one or more random variables) and demonstrate understanding of their properties Audience: Undergraduate 2. Perform important operations in probability (e.g. calculate the probabilities of events, derive the probability distributions of random variables, compute moments and the expectation of functions of random variables, calculate covariances and correlations, and obtain conditional distributions and conditional expectations) and interpret the results Audience: Undergraduate 3. Explain the meaning of key results in probability theory that are especially important in mathematical statistics (e.g. Bayes’ Theorem, probabilistic tail inequalities such as Markov’s and Chebyshev’s inequalities, the Law of Large Numbers, and the Central Limit Theorem) Audience: Undergraduate 4. Identify, utilize, and understand the key properties of, probability distributions that are especially important in statistics, including discrete families of distributions (e.g. the binomial, Poisson, geometric, and negative binomial distributions) and continuous families of distributions (e.g. the uniform, exponential, gamma, and normal distributions) Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/MATH 310 — INTRODUCTION TO PROBABILITY AND MATHEMATICAL STATISTICS II 3 credits. Mathematical statistical inference aims at providing an understanding of likelihood's central role to statistical inference, using the language of mathematical statistics to analyze statistical procedures, and using the computer as a tool for understanding statistics. Specific topics include: samples and populations, estimation, hypothesis testing, and theoretical properties of statistical inference. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=STAT%20309" title="STAT/MATH 309" class="bubblelink code" onclick="return showCourse(this, 'STAT 309');">STAT/MATH 309</a>, <a href="/search/?P=STAT%20311" title="STAT 311" class="bubblelink code" onclick="return showCourse(this, 'STAT 311');">STAT 311</a>, <a href="/search/?P=STAT%20431" title="STAT/MATH 431" class="bubblelink code" onclick="return showCourse(this, 'STAT 431');">STAT/MATH 431</a>, or <a href="/search/?P=MATH%20531" title="MATH 531" class="bubblelink code" onclick="return showCourse(this, 'MATH 531');">MATH 531</a>) and (<a href="/search/?P=STAT%20240" title="STAT 240" class="bubblelink code" onclick="return showCourse(this, 'STAT 240');">STAT 240</a>, <a href="/search/?P=STAT%20301" title="STAT 301" class="bubblelink code" onclick="return showCourse(this, 'STAT 301');">STAT 301</a>, STAT 302, <a href="/search/?P=STAT%20324" title="STAT 324" class="bubblelink code" onclick="return showCourse(this, 'STAT 324');">STAT 324</a>, <a href="/search/?P=STAT%20371" title="STAT 371" class="bubblelink code" onclick="return showCourse(this, 'STAT 371');">STAT 371</a>, or <a href="/search/?P=ECON%20310" title="ECON 310" class="bubblelink code" onclick="return showCourse(this, 'ECON 310');">ECON 310</a>), or graduate/professional standingCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Construct point estimators including maximum likelihood estimators, understand the theoretical properties of point estimation methods, and evaluate their performance Audience: Undergraduate 2. Construct hypothesis tests including likelihood ratio tests, interpret their results, evaluate their performance, and understand the theoretical properties of hypothesis testing methods Audience: Undergraduate 3. Construct interval estimators to quantify uncertainty, understand the theoretical properties of interval estimation methods, and interpret their results Audience: Undergraduate 4. Mathematically derive key quantities required for statistical inference methods and be familiar with simulation-based techniques for obtaining those quantities Audience: Undergraduate 5. Describe the Bayesian approach to inference and contrast it with the frequentist approach Audience: Undergraduate 6. Identify and describe the assumptions underlying methods of statistical inference and explain their importance Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 311 — INTRODUCTION TO THEORY AND METHODS OF MATHEMATICAL STATISTICS I 3 credits. Elements of probability, important discrete distributions, acceptance sampling by attributes, sample characteristics, probability distributions and population characteristics, the normal distribution, acceptance sampling plans based on sample means and variances, sampling from the normal, the central limit theorem, point and interval estimation. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=MATH%20234" title="MATH 234" class="bubblelink code" onclick="return showCourse(this, 'MATH 234');">MATH 234</a>, <a href="/search/?P=MATH%20376" title="MATH 376" class="bubblelink code" onclick="return showCourse(this, 'MATH 376');">376</a>, or concurrent enrollment or graduate/professsional standing. Not open to students with credit for <a href="/search/?P=STAT%20309" title="STAT/MATH 309" class="bubblelink code" onclick="return showCourse(this, 'STAT 309');">STAT/MATH 309</a> or <a href="/search/?P=STAT%20431" title="STAT/MATH 431" class="bubblelink code" onclick="return showCourse(this, 'STAT 431');">STAT/MATH 431</a>Course Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 312 — INTRODUCTION TO THEORY AND METHODS OF MATHEMATICAL STATISTICS II 3 credits. Unbiased estimation, maximum likelihood estimation, confidence intervals, tests of hypotheses, Neyman-Pearson lemma, likelihood ratio test, regression, analysis of variance with applications. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20309" title="STAT/MATH 309" class="bubblelink code" onclick="return showCourse(this, 'STAT 309');">STAT/MATH 309</a>, <a href="/search/?P=STAT%20311" title="STAT 311" class="bubblelink code" onclick="return showCourse(this, 'STAT 311');">STAT 311</a>, <a href="/search/?P=STAT%20431" title="STAT/MATH 431" class="bubblelink code" onclick="return showCourse(this, 'STAT 431');">STAT/MATH 431</a>, <a href="/search/?P=MATH%20531" title="MATH 531" class="bubblelink code" onclick="return showCourse(this, 'MATH 531');">MATH 531</a>, or graduate/professional standingCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Be able to use probability theory to understand and utilize three principal tools of statistical inference: point estimators, confidence intervals, and hypothesis tests. Audience: Undergraduate 2. Understand and be able to use standard statistical procedures for analyzing numerical data in certain contexts. These include inference for the mean based on a single random sample; comparing two means based on two random samples; Analysis of Variance; simple linear regression. Audience: Undergraduate 3. Understand and be able to use standard statistical procedures for analyzing binary and categorical data in certain contexts. These include one- and two-sample inference for proportions, and the analysis of one- and two-way contingency tables for multi-category data. Audience: Undergraduate 4. Identify the assumptions behind statistical procedures and understand their importance. Be able to recognize when techniques based on standard reference distributions (normal, chi-square, T- and F-distribution) are valid or not, and be able to utilize certain alternatives such as exact or nonparametric methods when they are required or preferable. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 324 — INTRODUCTORY APPLIED STATISTICS FOR ENGINEERS 3 credits. Descriptive statistics, probability concepts and distributions, random variables. Hypothesis tests and confidence intervals for one- and two-sample problems. Linear regression, model checking, and inference. Analysis of variance and basic ideas in experimental design. Utilizes the R programming language. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=MATH%20211" title="MATH 211" class="bubblelink code" onclick="return showCourse(this, 'MATH 211');">MATH 211</a>, <a href="/search/?P=MATH%20217" title="MATH 217" class="bubblelink code" onclick="return showCourse(this, 'MATH 217');">217</a>, or <a href="/search/?P=MATH%20221" title="MATH 221" class="bubblelink code" onclick="return showCourse(this, 'MATH 221');">221</a>. Not open to students with credit for STAT 302 or <a href="/search/?P=STAT%20371" title="STAT 371" class="bubblelink code" onclick="return showCourse(this, 'STAT 371');">371</a>Course Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Articulate the basics of probability and statistics. Audience: Undergraduate 2. Make numeric and graphical summaries of simple data Audience: Undergraduate 3. Produce appropriate statistical analyses of simple data sets Audience: Undergraduate 4. Design simple experiments with data that will suit basic statistical analysis Audience: Undergraduate 5. Use R for statistical computations and graphs Audience: Undergraduate 6. Learn additional statistical methods Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock gradcap "> STAT 333 — APPLIED REGRESSION ANALYSIS 3 credits. An introduction to regression with emphasis on the practical aspects. Topics include: straight-line model, role of assumptions, residual analysis, transformations, multiple regression (with some use of matrix notation), multicollinearity, subset selection, and a brief introduction to mixed models. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=STAT%20240" title="STAT 240" class="bubblelink code" onclick="return showCourse(this, 'STAT 240');">STAT 240</a>, <a href="/search/?P=STAT%20301" title="STAT 301" class="bubblelink code" onclick="return showCourse(this, 'STAT 301');">301</a>, 302, <a href="/search/?P=STAT%20312" title="STAT 312" class="bubblelink code" onclick="return showCourse(this, 'STAT 312');">312</a>, <a href="/search/?P=STAT%20324" title="STAT 324" class="bubblelink code" onclick="return showCourse(this, 'STAT 324');">324</a>, <a href="/search/?P=STAT%20371" title="STAT 371" class="bubblelink code" onclick="return showCourse(this, 'STAT 371');">371</a>, <a href="/search/?P=ECON%20310" title="ECON 310" class="bubblelink code" onclick="return showCourse(this, 'ECON 310');">ECON 310</a>, <a href="/search/?P=B%20M%20E%20325" title="B M E 325" class="bubblelink code" onclick="return showCourse(this, 'B M E 325');">B M E 325</a>, or <a href="/search/?P=I%20SY%20E%20210" title="I SY E 210" class="bubblelink code" onclick="return showCourse(this, 'I SY E 210');">I SY E 210</a>) and (STAT 327 or <a href="/search/?P=STAT%20303" title="STAT 303" class="bubblelink code" onclick="return showCourse(this, 'STAT 303');">303</a>, or concurrent enrollment)Course Designation: Gen Ed - Quantitative Reasoning Part B Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Correctly choose and apply common regression methods that are used in practice to analyze data, including simple and multiple linear regressions, ANOVAs/ANCOVAs, generalized linear models (e.g. logistic and Poisson) and fixed/random/mixed effect models Audience: Undergraduate 2. Identify the underlying assumptions behind common regression methods and utilize diagnostic tools to detect violations of said assumptions Audience: Undergraduate 3. Correctly interpret and explain results from regression methods, including interpretation of the coefficients, the p-values, R-squared, and other statistical summaries from regression Audience: Undergraduate 4. Apply these methods to real data using the free statistical software R Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 340 — DATA SCIENCE MODELING II 4 credits. Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation. Features the analysis of real-world data sets and the communication of findings in a clear and reproducible manner within a project setting. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20211" title="MATH 211" class="bubblelink code" onclick="return showCourse(this, 'MATH 211');">MATH 211</a>, <a href="/search/?P=MATH%20217" title="MATH 217" class="bubblelink code" onclick="return showCourse(this, 'MATH 217');">217</a>, or <a href="/search/?P=MATH%20221" title="MATH 221" class="bubblelink code" onclick="return showCourse(this, 'MATH 221');">221</a>) and <a href="/search/?P=STAT%20240" title="STAT 240" class="bubblelink code" onclick="return showCourse(this, 'STAT 240');">STAT 240</a>Course Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Understand and apply basic concepts in probability; combine basic probability models to build more complicated ones; and critique models and their assumptions Audience: Undergraduate 2. Formulate statistical hypotheses for different kinds of research questions and test those hypotheses using both classical and Monte Carlo methods. Audience: Undergraduate 3. Understand and apply principles of statistical estimation and prediction, including fitting models and assessing model quality, in the context of both linear and logistic regression. Audience: Undergraduate 4. Apply statistical tools to answer research questions using real-world data and present these findings clearly in both spoken and written form to non-experts. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 349 — INTRODUCTION TO TIME SERIES 3 credits. Autocorrelation; stationarity and non-stationarity; heteroscedasticity; dynamic models; auto-regressive and moving average models; identification and fitting; forecasting; seasonal adjustment; applications for financial time series, social sciences and environmental studies. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20333" title="STAT 333" class="bubblelink code" onclick="return showCourse(this, 'STAT 333');">STAT 333</a>, <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>, graduate/professional standing, or declared in Statistics VISPCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 351 — INTRODUCTORY NONPARAMETRIC STATISTICS 3 credits. Distribution free statistical procedures or methods valid under nonrestrictive assumptions: basic tools; counting methods; order statistics, ranks, empirical distribution functions; distribution free tests and associated interval and point estimators; sign test; signed rank tests; rank tests; Mann Whitney Wilcoxon procedures; Kolmogorov Smirnov tests; permutation methods; kernel density estimation; kernel and spline regression estimation; computer techniques and programs; discussion and comparison with parametric methods. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20333" title="STAT 333" class="bubblelink code" onclick="return showCourse(this, 'STAT 333');">STAT 333</a>, <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>, graduate/professional standing, or declared in Statistics VISPCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 360 — TOPICS IN STATISTICS STUDY ABROAD 1-3 credits. Credit is awarded for students having completed an advanced statistics course in a study abroad program for which there is no direct equivalence to the statistics department course offerings. The study abroad course must be pre-approved by the statistics department. Enrollment in a UW-Madison resident study abroad program. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: NoneCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: Yes, unlimited number of completions</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock gradcap "> STAT 371 — INTRODUCTORY APPLIED STATISTICS FOR THE LIFE SCIENCES 3 credits. Introduction to modern statistical practice in the life sciences, using the R programming language. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20112" title="MATH 112" class="bubblelink code" onclick="return showCourse(this, 'MATH 112');">MATH 112</a> and placed out of <a href="/search/?P=MATH%20113" title="MATH 113" class="bubblelink code" onclick="return showCourse(this, 'MATH 113');">MATH 113</a>), (<a href="/search/?P=MATH%20113" title="MATH 113" class="bubblelink code" onclick="return showCourse(this, 'MATH 113');">MATH 113</a> and placed out of <a href="/search/?P=MATH%20112" title="MATH 112" class="bubblelink code" onclick="return showCourse(this, 'MATH 112');">MATH 112</a>), (<a href="/search/?P=MATH%20112" title="MATH 112" class="bubblelink code" onclick="return showCourse(this, 'MATH 112');">MATH 112</a> and <a href="/search/?P=MATH%20113" title="MATH 113" class="bubblelink code" onclick="return showCourse(this, 'MATH 113');">113</a>), <a href="/search/?P=MATH%20114" title="MATH 114" class="bubblelink code" onclick="return showCourse(this, 'MATH 114');">MATH 114</a>, <a href="/search/?P=MATH%20171" title="MATH 171" class="bubblelink code" onclick="return showCourse(this, 'MATH 171');">171</a>, <a href="/search/?P=MATH%20211" title="MATH 211" class="bubblelink code" onclick="return showCourse(this, 'MATH 211');">211</a> or <a href="/search/?P=MATH%20221" title="MATH 221" class="bubblelink code" onclick="return showCourse(this, 'MATH 221');">221</a> or placement in <a href="/search/?P=MATH%20221" title="MATH 221" class="bubblelink code" onclick="return showCourse(this, 'MATH 221');">MATH 221</a>. Not open to students with credit for STAT 302 or <a href="/search/?P=STAT%20324" title="STAT 324" class="bubblelink code" onclick="return showCourse(this, 'STAT 324');">324</a>Course Designation: Gen Ed - Quantitative Reasoning Part B Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Articulate the basics of probability and statistics Audience: Undergraduate 2. Make numeric and graphical summaries of simple data Audience: Undergraduate 3. Produce appropriate statistical analyses of simple data sets Audience: Undergraduate 4. Design simple experiments whose data will suit basic statistical analysis Audience: Undergraduate 5. Use RStudio, a free statistical software package, for statistical computations and graphs Audience: Undergraduate 6. Study and learn additional statistical methods Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/COMP SCI 403 — INTERNSHIP COURSE IN COMP SCI AND DATA SCIENCE 1 credit. Enables students with outside internships to earn academic credit connected to their work experience related to the Computer Sciences or Data Science programs. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: Yes, for 3 number of completionsLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Understand the challenges and opportunities in Computer Sciences and Data Science professions Audience: Undergraduate 2. Be prepared to find, apply and interview for a job and/or additional education Audience: Undergraduate 3. Articulate your career goals and long-term trajectory Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 405 — DATA SCIENCE COMPUTING PROJECT 3 credits. The development of tools necessary for collecting, managing, and analyzing large data sets. Examples of techniques and programs used include Linux, R, distributed computing, text editor(s), git/github, and other related tools. Work in teams to research, develop, write, and make presentations related to a variety of data analysis projects. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=STAT%20240" title="STAT 240" class="bubblelink code" onclick="return showCourse(this, 'STAT 240');">STAT 240</a> or <a href="/search/?P=STAT%20303" title="STAT 303" class="bubblelink code" onclick="return showCourse(this, 'STAT 303');">303</a>) and (<a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>, <a href="/search/?P=COMP%20SCI%20220" title="COMP SCI 220" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 220');">220</a>, <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">300</a>, or placement into <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>), or graduate/professional standingCourse Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Collect and manage data and write programs and documentation viatools suited to large computations including an operating system, aneditor, and a version control system. Audience: Undergraduate 2. Run analyses too large for a laptop on cluster, grid, and/or cloudcomputing environments. Audience: Undergraduate 3. Work in teams to research, develop, write, and make presentations ona data analysis proposal, a draft data analysis, and a revised dataanalysis. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 411 — AN INTRODUCTION TO SAMPLE SURVEY THEORY AND METHODS 3 credits. An introduction to the methods used to design sample surveys and analyze the results. Topics covered include: basic tools, simple random sampling, ratio and regression estimation, stratification, systematic sampling, cluster (area) sampling, two-stage sampling, unequal probability sampling, non-sampling errors, and missing data. For illustration and clarification, examples are drawn from diverse areas of application. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20333" title="STAT 333" class="bubblelink code" onclick="return showCourse(this, 'STAT 333');">STAT 333</a>, <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>, graduate/professional standing, or declared in Statistics VISPCourse Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2023</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 421 — APPLIED CATEGORICAL DATA ANALYSIS 3 credits. Analysis of multidimensional contingency tables, Poisson regression, and logistic regression, with emphasis on practical applications. Use of computer programs for such analyses. Model selection, testing goodness of fit, estimation of parameters, measures of association and methods for detecting sources of significance. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20333" title="STAT 333" class="bubblelink code" onclick="return showCourse(this, 'STAT 333');">STAT 333</a>, <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>, graduate/professional standing, or declared in Statistics VISPCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/M E 424 — STATISTICAL EXPERIMENTAL DESIGN 3 credits. Introduction to statistical design and analysis of experiments. Topics include: principles of randomization, blocking and replication, randomized blocking designs, Latin square designs, full factorial and fractional factorial designs and response surface methodology. Substantial focus will be devoted to engineering applications. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20240" title="STAT 240" class="bubblelink code" onclick="return showCourse(this, 'STAT 240');">STAT 240</a>, <a href="/search/?P=STAT%20301" title="STAT 301" class="bubblelink code" onclick="return showCourse(this, 'STAT 301');">301</a>, 302, <a href="/search/?P=STAT%20312" title="STAT 312" class="bubblelink code" onclick="return showCourse(this, 'STAT 312');">312</a>, <a href="/search/?P=STAT%20324" title="STAT 324" class="bubblelink code" onclick="return showCourse(this, 'STAT 324');">324</a>, <a href="/search/?P=STAT%20371" title="STAT 371" class="bubblelink code" onclick="return showCourse(this, 'STAT 371');">371</a>, or <a href="/search/?P=MATH%20310" title="MATH/STAT 310" class="bubblelink code" onclick="return showCourse(this, 'MATH 310');">MATH/STAT 310</a>Course Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/MATH 431 — INTRODUCTION TO THE THEORY OF PROBABILITY 3 credits. Topics covered include axioms of probability, random variables, the most important discrete and continuous probability distributions, expectation and variance, moment generating functions, conditional probability and conditional expectations, multivariate distributions, Markov's and Chebyshev's inequalities, laws of large numbers, and the central limit theorem. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=MATH%20234" title="MATH 234" class="bubblelink code" onclick="return showCourse(this, 'MATH 234');">MATH 234</a> or <a href="/search/?P=MATH%20376" title="MATH 376" class="bubblelink code" onclick="return showCourse(this, 'MATH 376');">376</a> or graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Recall and state the formal definitions of the mathematical objects and their properties used in probability theory (e.g., probability spaces, random variables and random vectors and their probability distributions, named distributions, conditional probability, independence, linearity of expectation, etc.). Audience: Undergraduate 2. Use such definitions to argue that a mathematical object does or does not have the condition of being a particular type or having a particular property (e.g., whether certain events or random variables are independent or not, whether a random variable has one of the named distributions, whether or not a sequence of random variables is exchangeable, etc.). Audience: Undergraduate 3. Recall and state the standard theorems of probability theory. (e.g., Bayes' theorem, the law of large numbers, the central limit theorem, etc.), and apply these theorems to solve problems in probability theory. Audience: Undergraduate 4. Use multiple approaches to compute and estimate probabilities and expectations (e.g., using the indicator method, using conditioning, estimating probabilities using normal or Poisson approximation etc.). Audience: Undergraduate 5. Construct mathematical arguments related to the above definitions, properties, and theorems, including the construction of examples and counterexamples. Audience: Undergraduate 6. Convey his or her arguments in oral and written forms using English and appropriate mathematical terminology and notation (and grammar). Audience: Undergraduate 7. Model simple real-life situations using techniques in probability theory and calculate probabilities and expectations associated with those models. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 433 — DATA SCIENCE WITH R 3 credits. Perform Data Science as an iterative (back and forth) process of four different types of activities (data collection, data wrangling, data analysis, communication). Traverse through the five requisite stances (scientist, coder, mathematician, methodologist, skeptic). Develop and hone a broad set of computational tools in R (but not the broadest) and a broad set of statistical/machine learning tools (but not the broadest). Focus on doing these with agility to make the coding "transparent" and serve the large goals of the project. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=STAT%20333" title="STAT 333" class="bubblelink code" onclick="return showCourse(this, 'STAT 333');">STAT 333</a> or <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>) and (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>), graduate/professional standing, or declared in Statistics VISPCourse Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2022<div class="bubblehide">Learning Outcomes: 1. Describe and apply the key steps taken in most data science projects and how these steps fit into a coherent whole Audience: Undergraduate 2. Identify how a data set can be used for a specific purpose Audience: Undergraduate 3. Clean and analyze data Audience: Undergraduate 4. Communicate the results of data analysis Audience: Undergraduate 5. Develop agile and reproducible code that enables iterative development of a data science project using the tools in the tidyverse Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 436 — STATISTICAL DATA VISUALIZATION 3 credits. Techniques for visualization within data science workflows. Topics include data preparation; exploratory data analysis; spatial, tabular, and graph structured data; dimensionality reduction; model visualization and interpretability; interactive queries and navigation. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=STAT%20240" title="STAT 240" class="bubblelink code" onclick="return showCourse(this, 'STAT 240');">STAT 240</a> or <a href="/search/?P=STAT%20303" title="STAT 303" class="bubblelink code" onclick="return showCourse(this, 'STAT 303');">303</a>), graduate professional/standing, or declared in Statistics VISPCourse Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Apply preprocessing strategies, including outlier removal, missing data imputation, and tidying, in a way that supports downstream visualization Audience: Undergraduate 2. Develop a vocabulary of visual encoding that support exploration of geospatial, temporal, tree-structured, and network data, and demonstrate facility implementing them using packages in the R programming language Audience: Undergraduate 3. Design dynamic queries that support interactive visualization of heterogenous data and demonstrate facility implementing them using the shiny package in the R programming language Audience: Undergraduate 4. Design effective visualizations to summarize the results of dimensionality reduction and clustering algorithms Audience: Undergraduate 5. Use visual artifacts derived from complex statistical machine learning models to discuss the patterns they learn and mistakes they make Audience: Undergraduate 6. Recognize chart junk in real-word visualizations and propose improved alternatives Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 443 — CLASSIFICATION AND REGRESSION TREES 3 credits. Introduction to algorithms and applications of classification and regression trees. Recursive partitioning, pruning, and cross-validation estimation of prediction error. Class priors and misclassification costs. Univariate and linear splits. Linear and kernel discriminant analysis and nearest-neighbor classification. Unbiased variable selection and importance scoring of variables. Least-squares, quantile, Poisson, logistic, and proportional hazards regression tree models. Tree ensembles. Subgroup identification of differential treatment effects. Multiple and longitudinal response variables. Missing values and multiple missing value codes. Comparisons with neural networks, support vector machines, and other methods. Bootstrap calibration and post-selection inference. Applications to business, social science, engineering, biology, medicine, and other fields. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20333" title="STAT 333" class="bubblelink code" onclick="return showCourse(this, 'STAT 333');">STAT 333</a>, <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>, graduate/professional standing, or declared in Statistics VISPCourse Designation: Breadth - Either Social Science or Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2022<div class="bubblehide">Learning Outcomes: 1. Know the weaknesses and limitations of traditional statistical modeling methods Audience: Undergraduate 2. Understand the strengths and capabilities of the classification and regression tree approach Audience: Undergraduate 3. Learn to analyze data with missing values without missing value imputation Audience: Undergraduate 4. Learn to analyze data containing circular or periodic variables, such as angle of impact, time of day, day of week, and month of year Audience: Undergraduate 5. Learn how to build regression tree models for least squares regression, logistic regression, Poisson regression, quantile regression, and proportional hazards regression Audience: Undergraduate 6. Learn how to build prediction models for univariate, multivariate, longitudinal, and censored dependent variables Audience: Undergraduate 7. Learn to use GUIDE and R software for algorithms such as AMELIA and MICE (for missing value imputation), and RPART, MOB, random Forest (for tree and forest models) Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 451 — INTRODUCTION TO MACHINE LEARNING AND STATISTICAL PATTERN CLASSIFICATION 3 credits. Pattern classification, regression analysis, clustering, and dimensionality reduction. For each category, covers fundamental algorithms and selections of contemporary, current state-of-the-art algorithms. Focus on evaluation of machine learning models using statistical methods. Statistical pattern classification approaches, including maximum likelihood estimation and Bayesian decision theory, algorithmic and nonparametric approaches. Practical use of machine learning algorithms using open source libraries from the Python programming ecosystem. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20321" title="MATH 321" class="bubblelink code" onclick="return showCourse(this, 'MATH 321');">321</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>, graduate/professional standing, or declared in Statistics VISPCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Understand the different subfields of machine learning, such as supervised and unsupervised learning and being familiar with essential algorithms from each subfield. Audience: Undergraduate 2. Identify whether machine learning is appropriate for solving a given problem task and which class of algorithms is best suited for real-world problem solving. Audience: Undergraduate 3. Use statistical learning theory to combine multiple machine learning models via ensemble methods. Audience: Undergraduate 4. Apply best-practices for statistical model evaluation, model selection and algorithm comparisons including suitable statistical hypothesis tests. Audience: Undergraduate 5. Use contemporary programming languages and machine learning libraries for implementing machine learning algorithms such that they can be readily applied for practical problem solving. Audience: Undergraduate 6. Connect concepts from probability theory with supervised learning by implementing models based on Bayes' theorem. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 453 — INTRODUCTION TO DEEP LEARNING AND GENERATIVE MODELS 3 credits. Deep learning is a field that specializes in discovering and extracting intricate structures in large, unstructured datasets for parameterizing artificial neural networks with many layers. Since deep learning has pushed the state-of-the-art in many research and application areas, it's become indispensable for modern technology. Focuses on a understanding deep, artificial neural networks by connecting it to related concepts in statistics. Beyond covering deep learning models for predictive modeling, focus on deep generative models. Besides explanations on a mathematical and conceptual level, emphasize the practical aspects of deep learning. Open-source computing provides hands-on experience for implementing deep neural nets, working on supervised learning tasks, and applying generative models for dataset synthesis. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20321" title="MATH 321" class="bubblelink code" onclick="return showCourse(this, 'MATH 321');">321</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>, graduate/professional standing, or declared in Statistics VISPCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Develop an advanced understanding of deep learning and generative models, which represent state-of-the-art approaches for predictive modeling in today’s data-driven world. Audience: Undergraduate 2. Identify scenarios where it makes sense to deep learning for real-world problem-solving. Audience: Undergraduate 3. Build a repertoire of different algorithms and approaches to deep learning and understanding their various strengths and weaknesses. Audience: Undergraduate 4. Employ the Python programming language and Python’s scientific computing stack for implementing deep learning algorithms to 1) enhance the learning experience, 2) conduct research and be able to develop novel algorithms, and 3) apply deep learning to problem-solving in various fields and application areas. Audience: Undergraduate 5. Apply both the theoretical and practical concepts taught in this class to creative, real-world problem solving and communicating the outcome professionally in form of a scientific paper and a formal oral presentation. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 456 — APPLIED MULTIVARIATE ANALYSIS 3 credits. Theory and applications of multivariate statistical methods. Basic concepts and statistical reasoning which underlie the techniques of multivariate analysis. Ideas rather than derivations stressed although basic models discussed to give the student some feeling for their adequacy in particular situations. Acquaintance with and use of existing computer programs in the multivariate analysis area. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=STAT%20333" title="STAT 333" class="bubblelink code" onclick="return showCourse(this, 'STAT 333');">STAT 333</a> or <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>) and (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>), graduate/professional standing, or declared in Statistics VISPCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 461 — FINANCIAL STATISTICS 3 credits. Stochastic models and statistical methodologies are widely employed in modern finance. The models and their inferences are very important for academic research and financial practices. Financial stochastic models and their statistical inferences with applications to volatility analysis and risk management, introduction to discrete models such as binomial trees and GARCH and stochastic volatility models as well as simple continuous models like the Black-Scholes model. The focus will be on statistical inference, data analysis and risk management regarding these models. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=STAT%20333" title="STAT 333" class="bubblelink code" onclick="return showCourse(this, 'STAT 333');">STAT 333</a>, <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>, or <a href="/search/?P=ECON%20410" title="ECON 410" class="bubblelink code" onclick="return showCourse(this, 'ECON 410');">ECON 410</a>) and (<a href="/search/?P=MATH%20309" title="MATH/STAT 309" class="bubblelink code" onclick="return showCourse(this, 'MATH 309');">MATH/STAT 309</a>, <a href="/search/?P=STAT%20311" title="STAT 311" class="bubblelink code" onclick="return showCourse(this, 'STAT 311');">STAT 311</a>, <a href="/search/?P=MATH%20331" title="MATH 331" class="bubblelink code" onclick="return showCourse(this, 'MATH 331');">MATH 331</a>, <a href="/search/?P=MATH%20431" title="MATH/STAT 431" class="bubblelink code" onclick="return showCourse(this, 'MATH 431');">MATH/STAT 431</a>, or <a href="/search/?P=MATH%20531" title="MATH 531" class="bubblelink code" onclick="return showCourse(this, 'MATH 531');">MATH 531</a>), graduate/professional standing, or declared in Statistics VISPCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Understand basic stochastic models used in pricing financial instruments. Audience: Undergraduate 2. Develop statistical inferences for financial applications to volatility analysis and risk management. Audience: Undergraduate 3. Use computer packages to perform statistical analysis of financial data. Audience: Undergraduate 4. Interpret statistical analysis in the context of financial applications Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/COMP SCI 471 — INTRODUCTION TO COMPUTATIONAL STATISTICS 3 credits. Classical statistical procedures arise where closed-form mathematical expressions are available for various inference summaries (e.g. linear regression; analysis of variance). A major emphasis of modern statistics is the development of inference principles in cases where both more complex data structures are involved and where more elaborate computations are required. Topics from numerical linear algebra, optimization, Monte Carlo (including Markov chain Monte Carlo), and graph theory are developed, especially as they relate to statistical inference (e.g., bootstrapping, permutation, Bayesian inference, EM algorithm, multivariate analysis). <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20310" title="STAT/MATH 310" class="bubblelink code" onclick="return showCourse(this, 'STAT 310');">STAT/MATH 310</a> and (<a href="/search/?P=STAT%20333" title="STAT 333" class="bubblelink code" onclick="return showCourse(this, 'STAT 333');">STAT 333</a> or <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>), graduate/professional standing, or declared in Statistics VISPCourse Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2020<div class="bubblehide">Learning Outcomes: 1. Use computational tools (alongside mathematical ones) to extract information from (a) the likelihood function, the central object of interest in frequentist statistics, and (b) the posterior distribution, the central object of interest in Bayesian statistics Audience: Undergraduate 2. Describe, understand the theoretical properties of, and implement basic algorithms for optimizing likelihood functions, including least squares and the IRLS algorithm, and the EM algorithm Audience: Undergraduate 3. Understand random numbers and pseudorandom numbers and how to distinguish them, and utilize a variety of techniques for generating random variates from a probability distribution Audience: Undergraduate 4. Use Monte Carlo methodology for such purposes as (a) carrying out a simulation study to study the properties of a statistical method, or (b) performing statistical inference via the bootstrap, or MCMC Audience: Undergraduate 5. Understand the use of graphical models for representing the structure of complex joint distributions, and be able to use computational tools to extract information from graphical models Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/COMP SCI/MATH 475 — INTRODUCTION TO COMBINATORICS 3 credits. Problems of enumeration, distribution, and arrangement. Inclusion-exclusion principle. Generating functions and linear recurrence relations. Combinatorial identities. Graph coloring problems. Finite designs. Systems of distinct representatives and matching problems in graphs. Potential applications in the social, biological, and physical sciences. Puzzles. Problem solving. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>) or graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Understand basic counting strategies, such as staged thought-experiments, inclusion/exclusion, generating functions, and recurrence relations, and apply these strategies to solve a wide variety of counting problems. Audience: Undergraduate 2. Recall basic objects that are used in combinatorics, such as permutations and combinations of sets and multisets, binomial and multinomial coefficients, the Catalan numbers, the Stirling numbers, and the partition numbers. Audience: Undergraduate 3. Analyze a given combinatorial problem using the standard theorems of combinatorics, such as the pigeonhole principle, the Newton binomial theorem, the multinomial theorem, the Ramsey theorem, the Dilworth theorem, the Burnside theorem, and the Polya counting theorem. Audience: Undergraduate 4. Construct mathematical arguments related to combinatorial problems using the above definitions, properties, theorems, and counting strategies; including the construction of examples and counterexamples. Audience: Undergraduate 5. Convey his or her arguments in oral and written form in English, using appropriate mathematical terminology, notation, and grammar. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 479 — SPECIAL TOPICS IN STATISTICS 1-3 credits. Special topics of interest in undergraduate students. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: NoneCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/COMP SCI/I SY E/MATH 525 — LINEAR OPTIMIZATION 3 credits. Introduces optimization problems whose constraints are expressed by linear inequalities. Develops geometric and algebraic insights into the structure of the problem, with an emphasis on formal proofs. Presents the theory behind the simplex method, the main algorithm used to solve linear optimization problems. Explores duality theory and theorems of the alternatives. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>, or <a href="/search/?P=MATH%20443" title="MATH 443" class="bubblelink code" onclick="return showCourse(this, 'MATH 443');">443</a> or graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Use linear programming to formulate real world decision problems. Audience: Both Grad & Undergrad 2. Apply algorithms to solve linear programming problems and demonstrate their correctness. Audience: Both Grad & Undergrad 3. Combine different proving techniques explored in class in an original way to show new results. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/B M I 541 — INTRODUCTION TO BIOSTATISTICS 3 credits. Course designed for the biomedical researcher. Topics include: descriptive statistics, hypothesis testing, estimation, confidence intervals, t-tests, chi-squared tests, analysis of variance, linear regression, correlation, nonparametric tests, survival analysis and odds ratio. Biomedical applications used for each topic. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standing. Not open to students with credit for STAT 511 or <a href="/search/?P=POP%20HLTH%20551" title="POP HLTH/B M I 551" class="bubblelink code" onclick="return showCourse(this, 'POP HLTH 551');">POP HLTH/B M I 551</a>Course Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Understand building blocks and fundamentals that support core themes of Biostatistics in the application of biomedicine and public health Audience: Both Grad & Undergrad 2. Conduct basic statistical analyses of biomedical data Audience: Both Grad & Undergrad 3. Use R for statistical computing Audience: Both Grad & Undergrad 4. Critique methods and evidence from others' studies Audience: Graduate 5. Collaborate effectively with biostatisticians Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/B M I 542 — INTRODUCTION TO CLINICAL TRIALS I 3 credits. Intended for biomedical researchers interested in the design and analysis of clinical trials. Topics include definition of hypotheses, measures of effectiveness, sample size, randomization, data collection and monitoring, and issues in statistical analysis. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=B%20M%20I%20541" title="B M I/STAT 541" class="bubblelink code" onclick="return showCourse(this, 'B M I 541');">B M I/STAT 541</a>Course Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Develop skills to critically review clinical trials literature Audience: Graduate 2. Formulate focused research questions, specific aims, and key outcomes Audience: Graduate 3. Recognize the strengths and weaknesses of alternative clinical trials designs and design components Audience: Graduate 4. Develop related technical skills, including basic sample size calculations and survival analysis Audience: Graduate 5. Write a clinical trial protocol with all its core components Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/F&W ECOL 571 — STATISTICAL METHODS FOR BIOSCIENCE I 4 credits. Descriptive statistics, distributions, one- and two-sample normal inference, power, one-way ANOVA, simple linear regression, categorical data, non-parametric methods; underlying assumptions and diagnostic work. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/F&W ECOL 572 — STATISTICAL METHODS FOR BIOSCIENCE II 4 credits. Polynomial regression, multiple regression, two-way ANOVA with and without interaction, split-plot design, subsampling, analysis of covariance, elementary sampling, introduction to bioassay. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20571" title="STAT/F&W ECOL 571" class="bubblelink code" onclick="return showCourse(this, 'STAT 571');">STAT/F&W ECOL 571</a>Course Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 575 — STATISTICAL METHODS FOR SPATIAL DATA 3 credits. Detecting, quantifying, and modeling spatial patterns and structure in data. Variograms and covariance functions, linear predictions with uncertainty qualification, and conditional simulations. Spectral domain models and spectral densities. Spatial point processes. Contemporary applications and Gaussian process model fitting at scale. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=STAT%20333" title="STAT 333" class="bubblelink code" onclick="return showCourse(this, 'STAT 333');">STAT 333</a> or <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>) and (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>), graduate/professional standing, or declared in Statistics VISPCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Undertake exploratory analysis of spatial and spatio-temporal data with nonparametric estimators like empirical variograms. Audience: Both Grad & Undergrad 2. Fit parametric and semi-parametric mean and covariance models to spatial and spatio-temporal data for the purposes of prediction and uncertainty quantification. Audience: Both Grad & Undergrad 3. Understand important differences between interpolation and extrapolation and how to choose and design models more effectively for each application. Audience: Both Grad & Undergrad 4. Use the R programming language to load, manipulate, and effectively work with spatial data in popular file formats like CSV, NetCDF, HDF5. Audience: Undergraduate 5. Use several popular software libraries and modeling paradigms for spatial and spatio-temporal problems like estimation and prediction. Audience: Both Grad & Undergrad 6. Understand problems of identifiability and consistency in popular modeling paradigms. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 601 — STATISTICAL METHODS I 4 credits. Provides a thorough grounding in modern statistical methods. The specific learning outcomes for the course are to understand data collection in context (how/why data were collected, key questions under study); explore data by effective graphical and numerical summaries; understand probability concepts and models as tools for studying random phenomena and for statistical inference; analyze data using appropriate, modern statistical models, methods, and software; understand the statistical concepts underlying methods; develop the ability to interpret results and critically evaluate the methods used; communicate data analysis and key findings in context. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standing or declared in Statistics VISPCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 602 — STATISTICAL METHODS II 4 credits. Provides a thorough grounding in modern statistical methods. The specific learning outcomes for the course are to understand data collection in context (how/why data were collected, key questions under study); explore data by effective graphical and numerical summaries; understand probability concepts and models as tools for studying random phenomena and for statistical inference; analyze data using appropriate, modern statistical models, methods, and software; understand the statistical concepts underlying methods; develop the ability to interpret results and critically evaluate the methods used; communicate data analysis and key findings in context. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20601" title="STAT 601" class="bubblelink code" onclick="return showCourse(this, 'STAT 601');">STAT 601</a>Repeatable for Credit: NoLast Taught: Spring 2022</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 605 — DATA SCIENCE COMPUTING PROJECT 3 credits. The development of tools necessary for collecting, managing, and analyzing large data sets. Examples of techniques and programs utilized include Linux, R, distributed computing, powerful editor(s), git/github, and other related tools. Work in the class will be done in teams to research, develop, write, and make presentations related to a variety of data analysis projects. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Declared in Statistics MS or Statistics VISPCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Collect and manage data and write programs and documentation via tools suited to large computations. Use the Linux operating system and write shell scripts. Use an editor to write and manage local and remote files. Use the git/github version control system to track changes and manage collaboration. Audience: Graduate 2. Be able to use Linux, R, and the Slurm job scheduler to run tens of parallel jobs on the Statistics High Performance Computing (HPC) Cluster. Use Linux, R, and distributed high-throughput computing via HTCondor to run thousands of parallel jobs at UW's Center for High-Throughput Computing (CHTC). Audience: Graduate 3. Work in teams to research, develop, write, and make three presentations including one on a data analysis proposal consisting of data, a question, and a suggested analysis; one on a draft data analysis; and one on a revised data analysis. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 606 — COMPUTING IN DATA SCIENCE AND STATISTICS 3 credits. A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Declared in Statistics MS or Statistics VISP (undergraduate)Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Understand and apply the basics of the Python programming language and basic programming patterns in both the object-oriented and functional programming frameworks Audience: Graduate 2. Collect and clean data from a variety of data sources including markup languages from the web, databases, and by using APIs Audience: Graduate 3. Understand the MapReduce framework and apply it to large-scale data sets in a distributed environment using modern cloud computing platforms Audience: Graduate 4. Use numerical and scientific computing libraries to build and fit statistical models on large datasets Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 609 — MATHEMATICAL STATISTICS I 3 credits. Review of probability, random variables and vectors and their distributions, moments and inequalities, generating functions, transformations of random variables, sampling and distribution theory, convergence concepts for sequences of random variables, laws of large numbers, central limit and other limit theorems. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standing or declared in Statistics VISPCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 610 — INTRODUCTION TO STATISTICAL INFERENCE 4 credits. Conditioning, distribution theory, approximation to distributions, modes of convergence, limit theorems, statistical models, parameter estimation, comparision of estimators, confidence sets, theory of hypothesis tests, introduction to Bayesian inference and nonparametric estimation. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standing or declared in Statistics VISPCourse Designation: Breadth - Physical Sci. Counts toward the Natural Sci req Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 611 — STATISTICAL MODELS FOR DATA SCIENCE 3 credits. Probability, random variables and their distributions, joint and conditional distributions, moments and inequalities, generating functions, transformations of random variables, sampling and distribution theory, convergence concepts and limit theorems for sequences of random variables. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Declared in Data Science MS or Data Engineering MSCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Describe the foundations of probability theory, including: the axioms of probability, the concepts of sample space and probability measure, events and their probabilities, and the notions of conditional probabilityand independence Audience: Graduate 2. Explain the concepts of random variables and probability distributions, and calculate or otherwise utilize key mathematical objects and results related to them, including: random number generation, expected values and moments, moment-based probabilistic inequalities, moment generating functions, joint probability distributions of multiple random variables Audience: Graduate 3. Identify the most important probability distributions used in Statistics, cite their properties, create computer visualizations of them, and simulate random variates from these distributions. This includes the normal distribution, the binomial distribution, the exponential distribution, the Poisson distribution, and more Audience: Graduate 4. Apply, and explain the significance of, key results related to the limits of sequences of random variables, including laws of large numbers, the Central Limit Theorem, and the delta method Audience: Graduate 5. Grasp important elements of sampling theory, including: random samples and how to generate them; statistics, such as the sample mean and sample variance, and how to derive or simulate their sampling distributions; and the distributions that arise from a normal random sample, namely the chi-square,T, and F distributions. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 612 — STATISTICAL INFERENCE FOR DATA SCIENCE 3 credits. Statistical models, methods and theory for parameter estimation, Bayesian approach to parameter estimation, methods and theory for hypothesis tests, confidence sets, two-sample testing and ANOVA, categorical data analysis, linear regression. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20611" title="STAT 611" class="bubblelink code" onclick="return showCourse(this, 'STAT 611');">STAT 611</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Construct point estimators including maximum likelihood estimators, understand the theoretical properties of point estimation methods, evaluate their performance using mathematical derivations and simulation-based techniques, and identify optimal point estimators Audience: Graduate 2. Describe the Bayesian approach to point estimation and contrast it with the frequentist approach Audience: Graduate 3. Construct and evaluate hypothesis tests (such as likelihood ratio tests) using mathematical derivations or simulation-based techniques, interpret their results, understand theoretical properties of hypothesis testing methods, and identify optimal hypothesis tests Audience: Graduate 4. Construct and evaluate interval estimators using mathematical and simulation-based techniques, understand the theoretical properties of interval estimation methods, and interpret their results Audience: Graduate 5. Identify and describe the assumptions underlying methods of statistical inference and explain their importance Audience: Graduate 6. Fit models and carry out statistical inference in classical situations: namely: the comparison of two or more samples (ANOVA), the analysis of categorical data, linear regression, generalized linear models, and random and mixed effects models Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 613 — STATISTICAL METHODS FOR DATA SCIENCE 3 credits. Provides a thorough grounding in modern statistical methods. Introduces statistical techniques and methods of data analysis, including data description, linear regression models, diagnostic tools, prediction and model selection, and experimental design. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Declared in Data Science MS or Data Engineering MSCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Strategize and select a regression equation, examine residuals, transform data, recognize biases due to excluded variables and measurement error Audience: Graduate 2. Conduct general linear modeling for exponential family data and specifically models for binary, count, and categorical data, perform model fitting and inference Audience: Graduate 3. Develop the concepts and relevant methodology and ability to design and analyze experiments Audience: Graduate 4. Use and interpret computer package for regression programs Audience: Graduate 5. Present clear data structure and analysis in the context of data drawn from real-world applications Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 615 — STATISTICAL LEARNING 3 credits. The development of a variety of mathematical theories and statistical concepts (1) to understand the properties of those models and methods used for the purpose of prediction from data or decision making from data, and (2) to criticize such models, methods and their consequences. Specifically, the theories and tools that will be developed will include complexity theory, Hilbert spaces, Gaussian processes, Variational Analysis, and concentration inequalities. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Declared in Statistics: Statistics and Data Science MS, Data Science MS, Data Engineering MS, or Statistics VISPCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Demonstrate understanding of statistical theories, methodologies, and applications as tools in scientific inquiries. Audience: Graduate 2. Select and utilize the most appropriate statistical methodologies and practices. Audience: Graduate 3. Synthesize information pertaining to questions in empirical studies. Audience: Graduate 4. Communicate data concepts and analysis results clearly. Audience: Graduate 5. Recognize and apply principles of ethical and professional conduct. Audience: Graduate 6. Demonstrate knowledge of theoretical properties of many procedures used in machine learning for the purposes of classification, regression, and beyond. Audience: Graduate 7. Demonstrate knowledge of classical and modern notions in statistical learning theory including concentration inequalities, measures of statistical complexity, kernel methods for learning, Gaussian processes, and basics of variational inference. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/B M I 620 — STATISTICS IN HUMAN GENETICS 3 credits. Provides a comprehensive survey of statistical methods in human genetics research. Covered topics include linkage analysis, genome-wide association study, rare variant association analysis, meta-analysis, genome and variant annotation, heritability estimation, multi-trait modeling techniques, multi-omic data integration, and genetic risk prediction. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20333" title="STAT 333" class="bubblelink code" onclick="return showCourse(this, 'STAT 333');">STAT 333</a>, <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>, or graduate/professional standingCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: No<div class="bubblehide">Learning Outcomes: 1. Recognize problems in human genetics that are appropriate for statistical modeling Audience: Both Grad & Undergrad 2. Identify appropriate statistical procedures and computational algorithms for different tasks Audience: Both Grad & Undergrad 3. Gain practical experience in applying a select set of statistical methods on real data and evaluate its outputs Audience: Both Grad & Undergrad 4. Evaluate the strengths and weaknesses of different statistical and computational approaches designed for a specific biological problem Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 627 — PROFESSIONAL SKILLS IN DATA SCIENCE 1-3 credits. Covers important aspects of professional development in statistics, including skills with internet tools, sophisticated use of statistical languages (such as R) and other emerging topics. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 628 — DATA SCIENCE PRACTICUM 3 credits. Provides an understanding of and experience with turning statistics concepts into practice through data science practicums inspired by realistic projects. Combine theory and methods expertise with communications skills to translate from a vaguely stated project description and complex data set into a concisely summarized analysis, including both written and graphical interpretation that can be used by decision makers in an organization. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Declared in Statistics: Statistics and Data Science MSCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Frame real-world data problems into testable and solvable statistical problems. Audience: Graduate 2. Develop analysis/solutions that are fast, scalable, and robust Audience: Graduate 3. Communicate solutions concisely and clearly, creating understandable and accurate visual/tabular summaries of the data analysis, and responding to audience questions clearly Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/I SY E/MATH/OTM 632 — INTRODUCTION TO STOCHASTIC PROCESSES 3 credits. Topics include discrete-time Markov chains, Poisson point processes, continuous-time Markov chains, and renewal processes. Applications to queueing, branching, and other models in science, engineering and business. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=STAT%20431" title="STAT/MATH 431" class="bubblelink code" onclick="return showCourse(this, 'STAT 431');">STAT/MATH 431</a>, <a href="/search/?P=STAT%20309" title="STAT/MATH 309" class="bubblelink code" onclick="return showCourse(this, 'STAT 309');">309</a>, <a href="/search/?P=STAT%20311" title="STAT 311" class="bubblelink code" onclick="return showCourse(this, 'STAT 311');">STAT 311</a> or <a href="/search/?P=MATH%20531" title="MATH 531" class="bubblelink code" onclick="return showCourse(this, 'MATH 531');">MATH 531</a>) and (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>, <a href="/search/?P=MATH%20421" title="MATH 421" class="bubblelink code" onclick="return showCourse(this, 'MATH 421');">421</a> or <a href="/search/?P=MATH%20531" title="MATH 531" class="bubblelink code" onclick="return showCourse(this, 'MATH 531');">531</a>) or graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Recall and state the formal definitions of the mathematical objects and their properties for stochastic processes (e.g., discrete space Markov chains, Poisson processes, renewal processes, branching processes, etc.). Audience: Both Grad & Undergrad 2. Use such definitions to argue that a mathematical object does or does not have the condition of being a particular type or having a particular property (e.g., irreducibility, aperiodicity, recurrence, transience, the Markov property, etc.). Audience: Both Grad & Undergrad 3. Recall and state the standard theorems of stochastic processes. (e.g., laws of large numbers for Markov chains, existence of limiting/stationary distributions, law of large numbers for renewal processes, etc.) and recall the arguments for these theorems and the underlying logic of their proofs. Audience: Both Grad & Undergrad 4. Construct mathematical arguments related to the above definitions, properties, and theorems, including the construction of examples and counterexamples. Audience: Both Grad & Undergrad 5. Convey arguments in oral and written forms using English and appropriate mathematical terminology, notation and grammar. Audience: Both Grad & Undergrad 6. Model simple real life situations by means of discrete-space stochastic processes and calculate probabilities associated with those processes. Audience: Both Grad & Undergrad 7. Identify applications of course content in current areas of research. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/B M I 641 — STATISTICAL METHODS FOR CLINICAL TRIALS 3 credits. Statistical issues in the design of clinical trials, basic survival analysis, data collection and sequential monitoring. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20310" title="STAT/MATH 310" class="bubblelink code" onclick="return showCourse(this, 'STAT 310');">STAT/MATH 310</a> or graduate/professional standingCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/B M I 642 — STATISTICAL METHODS FOR EPIDEMIOLOGY 3 credits. Methods for analysis of case-control, cross sectional, and cohort studies. Covers epidemiologic study design, measures of association, rates, classical contingency table methods, and logistic and Poisson regression. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20310" title="STAT/MATH 310" class="bubblelink code" onclick="return showCourse(this, 'STAT 310');">STAT/MATH 310</a> or graduate/professional standingCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2023<div class="bubblehide">Learning Outcomes: 1. Summarize key concepts of statistical methods in epidemiology study: study design, association, causation Audience: Both Grad & Undergrad 2. Build parametric or semiparametric model for analyzing categorical data and survival data Audience: Both Grad & Undergrad 3. Utilize model design tools for model performance assessment Audience: Both Grad & Undergrad 4. Build semiparametric model for analyzing categorical data and survival data Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/B M I 643 — CLINICAL TRIAL DESIGN, IMPLEMENTATION, AND ANALYSIS 3 credits. Gain an understanding of fundamental elements of clinical trials (such as objectives, endpoints, surrogate endpoints, and statistical decisions) and statistical design considerations (such as randomization and blinding). Designs of clinical trials for Phase I, II, and III studies including single-arm, two-arm, and drug combination trials. Introduction to adaptive designs for precision medicine and master protocol designs such as umbrella trials and basket trials. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20609" title="STAT 609" class="bubblelink code" onclick="return showCourse(this, 'STAT 609');">STAT 609</a>, <a href="/search/?P=STAT%20610" title="STAT 610" class="bubblelink code" onclick="return showCourse(this, 'STAT 610');">610</a>, <a href="/search/?P=B%20M%20I%20641" title="B M I/STAT 641" class="bubblelink code" onclick="return showCourse(this, 'B M I 641');">B M I/STAT 641</a>, or graduate/professional standingCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Summarize the objectives of clinical trials and key statistical design components Audience: Both Grad & Undergrad 2. Design the clinical trials and investigate the operating characteristics of the design to implement clinical trials Audience: Both Grad & Undergrad 3. Write the protocol section of statistical considerations and communicate the design of clinical trials to both statisticians and clinicians Audience: Both Grad & Undergrad 4. Build sequential and adaptive methods for clinical trials Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 678 — INTRODUCTION TO STATISTICAL CONSULTING 3 credits. Develop statistical consulting skills to be able to communicate design and analysis to non-technical research collaborators. Provides a supportive environment to experiment with statistical consulting in practice, which will sometimes be uncomfortable and strange. Consulting problems typically do not have a "right" answer, and mistakes are encouraged. Take risks in sharing developing ideas in class. Connections with external organizations, such as the private sector and government agencies, will be made through possible internship experiences. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Declared in Statistics MSCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. summarize findings with graphs and non-technical writing Audience: Graduate 2. ask questions to reveal problem design and study details Audience: Graduate 3. present design, analysis approach and findings orally in plain language Audience: Graduate 4. manage time and project workflow effectively Audience: Graduate 5. contribute as an active member of a research team Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 679 — SPECIAL TOPICS IN STATISTICS 1-3 credits. Special topics in statistics at the master's level. Subject matter varies. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standing or declared in Statistics VISPCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 681 — SENIOR HONORS THESIS 3 credits. Mentored individual study for students writing honors thesis, as arranged with a faculty member. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Honors - Honors Only Courses (H)Repeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 682 — SENIOR HONORS THESIS 3 credits. Mentored individual study for students writing honors thesis, as arranged with a faculty member. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Honors - Honors Only Courses (H)Repeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 698 — DIRECTED STUDY 1-6 credits. Directed study projects as arranged with a faculty member. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 699 — DIRECTED STUDY 1-6 credits. Directed study projects as arranged with a faculty member. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 701 — APPLIED TIME SERIES ANALYSIS, FORECASTING AND CONTROL I 3 credits. Theory and application of discrete time series models illustrated with forecasting problems. Principles of iterative model building. Representation of dynamic relations by difference equations. Autoregressive integrated Moving Average models. Identification, fitting, diagnostic checking of models. Seasonal model application to forecasting in business, economics, ecology, and engineering used at each stage, which the student analyzes using computer programs which have been specially written and extensively tested. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2023</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/MATH 709 — MATHEMATICAL STATISTICS 4 credits. Introduction to measure theoretic probability; derivation and transformation of probability distributions; generating functions and characteristic functions; conditional expectation, sufficiency, and unbiased estimation; methods of large sample theory including laws of large numbers and central limit theorems; order statistics. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Understand foundations of mathematical statistics, including key notations, important concepts, and basic definitions Audience: Graduate 2. Develop proof-based theoretical skills for analyzing statistical problems Audience: Graduate 3. Familiarize various theoretical tools relevant to statistical research, including modern probability theory, optimization, and information theory Audience: Graduate 4. Prepare for statistical research by learning recent development in high-dimensional statistics and shrinkage estimation Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/MATH 710 — MATHEMATICAL STATISTICS 4 credits. Estimation, efficiency, Neyman-Pearson theory of hypothesis testing, confidence regions, decision theory, analysis of variance, and distribution of quadratic forms. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20709" title="STAT/MATH 709" class="bubblelink code" onclick="return showCourse(this, 'STAT 709');">STAT/MATH 709</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/COMP SCI/I SY E/MATH 726 — NONLINEAR OPTIMIZATION I 3 credits. Theory and algorithms for nonlinear optimization, focusing on unconstrained optimization. Line-search and trust-region methods; quasi-Newton methods; conjugate-gradient and limited-memory methods for large-scale problems; derivative-free optimization; algorithms for least-squares problems and nonlinear equations; gradient projection algorithms for bound-constrained problems; and simple penalty methods for nonlinearly constrained optimization. Students are strongly encouraged to have knowledge of linear algebra and familiarity with basic mathematical analysis. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/B M I 727 — THEORY AND METHODS OF LONGITUDINAL DATA ANALYSIS 3 credits. Theory and methods of fundamental statistical models for the analysis of longitudinal data, including repeated measures analysis of variance, linear mixed models, generalized linear mixed models, and generalized estimating equations. Introduction of how to implement these methods in statistical softwares such as in R and/or SAS, within the context of appropriate statistical models and carry out and interpret analyses. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20610" title="STAT 610" class="bubblelink code" onclick="return showCourse(this, 'STAT 610');">STAT 610</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2023<div class="bubblehide">Learning Outcomes: 1. Analyze longitudinal data in a variety of settings and with a variety of outcome variables Audience: Graduate 2. Apply statistical methods in fitting longitudinal data models for addressing scientific questions Audience: Graduate 3. Perform longitudinal data analyses in statistical softwares such as R and/or SAS Audience: Graduate 4. Interpret and communicate the scientific meanings of the results to both statisticians and non-statisticians (such as clinicians and scientists) Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 732 — LARGE SAMPLE THEORY OF STATISTICAL INFERENCE 3 credits. Stochastic modes of convergence. Asymptotic theory of normed sums of random variables with applications to asymptotic normality of estimators. Methods for deriving limit distributions of nonlinear statistics. Asymptotic relative efficiencies. Asymptotic confidence regions and tests of hypotheses. Models of non-identically distributed or dependent random variables. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20610" title="STAT 610" class="bubblelink code" onclick="return showCourse(this, 'STAT 610');">STAT 610</a> or <a href="/search/?P=MATH%20709" title="MATH/STAT 709" class="bubblelink code" onclick="return showCourse(this, 'MATH 709');">MATH/STAT 709</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2020</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/MATH 733 — THEORY OF PROBABILITY I 3 credits. An introduction to measure theoretic probability and stochastic processes.Topics include foundations, independence, zero-one laws, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, random walks, conditional expectations. Familiarity with basic measure theory (e.g. <a href="/search/?P=MATH%20629" title="MATH 629" class="bubblelink code" onclick="return showCourse(this, 'MATH 629');">MATH 629</a> or <a href="/search/?P=MATH%20721" title="MATH 721" class="bubblelink code" onclick="return showCourse(this, 'MATH 721');">721</a>) or concurrent registration in <a href="/search/?P=MATH%20721" title="MATH 721" class="bubblelink code" onclick="return showCourse(this, 'MATH 721');">MATH 721</a> is strongly recommended. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/MATH 734 — THEORY OF PROBABILITY II 3 credits. Possible topics include martingales, weak convergence of measures, introduction to Brownian motion. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/B M I 741 — SURVIVAL ANALYSIS THEORY AND METHODS 3 credits. Theory and practice of analytic methods for censored survival data, including nonparametric and parametric methods, the proportional hazards regression model, and a review of current topics in survival analysis. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Summarize the features of censored data and their implications in drawing inference Audience: Graduate 2. Implement proper non- and semi-parametric methods for analysis of various types of data Audience: Graduate 3. Recognize and check the assumptions needed for estimation and inference Audience: Graduate 4. Implement the inference procedures to solve real-world problems using statistical packages such as R (or SAS) Audience: Graduate 5. Interpret and present the analytic results in a clear and coherent way to answer substantive questions Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 760 — MULTIVARIATE ANALYSIS I 3 credits. Multivariate normal distribution, estimation of mean and covariance matrix; Wishart distribution; distribution of partial and multiple correlation coefficients; Hotelling's T-squared, principal components. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20610" title="STAT 610" class="bubblelink code" onclick="return showCourse(this, 'STAT 610');">STAT 610</a> or <a href="/search/?P=MATH%20710" title="MATH/STAT 710" class="bubblelink code" onclick="return showCourse(this, 'MATH 710');">MATH/STAT 710</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2023</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 761 — DECISION TREES FOR MULTIVARIATE ANALYSIS 3 credits. Tree construction, including finding splits, tree-pruning and error estimation. Categorical predictor variables, missing or censored data, prior class-probabilities, and unequal misclassification costs. Selection bias. Comparison with other statistics and machine-learning methods. Extensions to piecewise linear and non-least squares regression models. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/B M I 768 — STATISTICAL METHODS FOR MEDICAL IMAGE ANALYSIS 3 credits. Introduce key statistical methods and concepts for analyzing various medical images. Analyze publicly available and student/instructor supplied imaging data using the most up-to-date methods and tools. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2023<div class="bubblehide">Learning Outcomes: 1. Implement the key concepts of biomedical image processing and analysis Audience: Graduate 2. Describe the key concepts of statistical inference procedures for single and multiple images Audience: Graduate 3. Apply scalable computation in breaking bigger imaging problems into smaller computable problems Audience: Graduate 4. Describe functional data analysis (FA), geometric data analysis (GDA) and topological data analysis (TDA) methods in analyzing biomedical images Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 771 — STATISTICAL COMPUTING 3 credits. The design of statistical software including special techniques for probability distributions, methods of simulation of random processes, numerical methods for linear models and multivariate analysis, and methods for nonlinear models. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 772 — LINEAR RANDOMIZED ALGORITHMS FOR DATA SCIENCE 3 credits. Introduce new algorithms that leverage randomization to address the scale, speed, and sensitivity needs of modern data science. Develop the mathematical foundations of such randomized algorithms. Criticize these algorithms through the lens of computational resource utilization. Implement these algorithms to address linear problems in data science. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Apply common (constructive and inductive) thinking and reasoning patterns used in randomized algorithms for deriving solutions to linear problems arising in data science Audience: Graduate 2. Transform constructive mathematical reasoning patterns into numerical algorithms, and criticize the computational resource utilization of these algorithms Audience: Graduate 3. Implement numerical algorithms using the Julia programming language, and become familiar with assistive tools such as debuggers and profilers Audience: Graduate 4. Create and implement rigorous numerical experiments to test and compare different algorithms and implementations, and draw appropriate conclusions from the results of these experiments Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/ECON/GEN BUS 775 — BAYESIAN STATISTICS 3 credits. Introduces the theory, methods, and computational procedures needed to perform advanced Bayesian data analyses. Predictive and decision-theoretic motivations including subjective probability, risk, admissibility, and exchangeability; highlights key components of Bayesian analysis (i.e., prior, likelihood, posterior, and predictive distributions) within standard parametric models and advanced hierarchical and multilevel models; demonstrates the iterative process of model specification, implementation, criticism, and revision with applied case studies; implements computational techniques (e.g., Markov chain Monte Carlo, variational inference) in modern probabilistic programming languages. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20609" title="STAT 609" class="bubblelink code" onclick="return showCourse(this, 'STAT 609');">STAT 609</a>, <a href="/search/?P=STAT%20610" title="STAT 610" class="bubblelink code" onclick="return showCourse(this, 'STAT 610');">610</a>, <a href="/search/?P=STAT%20611" title="STAT 611" class="bubblelink code" onclick="return showCourse(this, 'STAT 611');">611</a>, <a href="/search/?P=STAT%20709" title="STAT/MATH 709" class="bubblelink code" onclick="return showCourse(this, 'STAT 709');">STAT/MATH 709</a>, <a href="/search/?P=ECON%20709" title="ECON 709" class="bubblelink code" onclick="return showCourse(this, 'ECON 709');">ECON 709</a>, <a href="/search/?P=POLI%20SCI%20818" title="POLI SCI 818" class="bubblelink code" onclick="return showCourse(this, 'POLI SCI 818');">POLI SCI 818</a>, or <a href="/search/?P=COMP%20SCI%20761" title="COMP SCI/E C E 761" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 761');">COMP SCI/E C E 761</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Justify the use of probability for coherent uncertainty quantification Audience: Graduate 2. Explain how Bayesian updating occurs in conjugate models and hierarchical models Audience: Graduate 3. Compare and contrast the conceptual and practical benefits and challenges of different posterior approximation strategies like MCMC and variational inference Audience: Graduate 4. Implement posterior approximation algorithms in modern statistical and probabilistic programming languages such as R or Stan Audience: Graduate 5. Specify, fit, criticize, and revise Bayesian models in practice Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 780 — INTRODUCTION TO QUANTUM DATA SCIENCE 3 credits. Quantum computation issues, including probability, statistics, sensing, information, machine learning, and applying data science to quantum information science. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20601" title="STAT 601" class="bubblelink code" onclick="return showCourse(this, 'STAT 601');">STAT 601</a>, (<a href="/search/?P=STAT%20609" title="STAT 609" class="bubblelink code" onclick="return showCourse(this, 'STAT 609');">STAT 609</a> and <a href="/search/?P=STAT%20610" title="STAT 610" class="bubblelink code" onclick="return showCourse(this, 'STAT 610');">610</a>), or (<a href="/search/?P=STAT%20611" title="STAT 611" class="bubblelink code" onclick="return showCourse(this, 'STAT 611');">STAT 611</a> and <a href="/search/?P=STAT%20612" title="STAT 612" class="bubblelink code" onclick="return showCourse(this, 'STAT 612');">612</a>)Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: No<div class="bubblehide">Learning Outcomes: 1. Demonstrate understanding of quantum probability, including axioms, observable, outcome, expectation, and distribution. Audience: Graduate 2. Master quantum statistics methods such as likelihood, information inequality, quantum hypothesis test, and quantum tomography (quantum sensing). Audience: Graduate 3. Comprehend concept of quantum computation and quantum information, including qubit and its properties, quantum entropy, and quantum cryptography. Audience: Graduate 4. Understand essential elements of quantum algorithms and quantum machine learning. Audience: Graduate 5. Understand statistics in quantum computational advantage (supremacy) studies. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 801 — ADVANCED FINANCIAL STATISTICS 3 credits. Statistical theory and methodology for modern financial data. Topics include financial stochastic models based on time series and stochastic calculus, modern statistical inference, and statistical learning for financial data as well as their applications to financial problems. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20601" title="STAT 601" class="bubblelink code" onclick="return showCourse(this, 'STAT 601');">STAT 601</a> or <a href="/search/?P=STAT%20701" title="STAT 701" class="bubblelink code" onclick="return showCourse(this, 'STAT 701');">701</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2020</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/MATH 803 — EXPERIMENTAL DESIGN I 3 credits. Summary of matrix algebra required, theory of estimable functions, incomplete blocks, balanced incomplete block designs, partially balanced incomplete block designs. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 809 — NON PARAMETRIC STATISTICS 3 credits. Statistical procedures valid under unrestrictive assumptions; sign test; confidence intervals; efficiency comparisons; signed rank procedures; Walsh sums; point estimators; two sample rank tests; zeros, ties, and other problems of discrete data; order statistics; Winsorized and truncated point estimators and connection with gross error models; permutation procedures; combinatorial problems, and computer applications. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20610" title="STAT 610" class="bubblelink code" onclick="return showCourse(this, 'STAT 610');">STAT 610</a> or <a href="/search/?P=MATH%20710" title="MATH/STAT 710" class="bubblelink code" onclick="return showCourse(this, 'MATH 710');">MATH/STAT 710</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2019</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 811 — SAMPLE SURVEY THEORY AND METHOD 3 credits. Simple random sampling; systematic sampling; probability sampling; stratified sampling; subsampling with units of equal and unequal size; double sampling; multi-stage and multi-phase sampling; ratio and regression estimates; model-based and model-assisted approaches; variance estimation; non-response. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20610" title="STAT 610" class="bubblelink code" onclick="return showCourse(this, 'STAT 610');">STAT 610</a> or <a href="/search/?P=MATH%20710" title="MATH/STAT 710" class="bubblelink code" onclick="return showCourse(this, 'MATH 710');">MATH/STAT 710</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2017</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/B M I 828 — SEMIPARAMETRIC METHODS IN DATA SCIENCE 3 credits. Review of statistical convergence modes, M-estimation, and basics of Hilbert space. Introduction of how to derive the nuisance tangent space, its complement, and the corresponding efficient influence function, from the geometric perspective of semiparametric models. Introduction of how to estimate nuisance functions using machine learning methods, and their implementations in R and/or Python. Introduction of a variety of semiparametric models in missing data analysis, causal inference, dimension reduction, precision medicine, semi-supervised learning, transfer learning and domain adaptation. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Derive the nuisance tangent space, its complement, and the corresponding efficient influence function in semiparametric models Audience: Graduate 2. Apply a variety of semiparametric methods and models in applications ranging from biomedical studies to social sciences Audience: Graduate 3. Perform machine learning algorithms for estimating nuisance functions in software such as R and/or Python Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/MATH 833 — TOPICS IN THE THEORY OF PROBABILITY 3 credits. Advanced topics in probability and stochastic processes. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2023</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 834 — EMPIRICAL PROCESSES AND SEMIPARAMETRIC INFERENCE 1-3 credits. Empirical process methods in statistics; semiparametric models; stochastic convergence in metric spaces; Glivenko-Cantelli and Donsker theorems; entropy calculations; bootstrapped empirical processes; functional delta method; Z-estimators; M-estimators; rates of convergence; semiparametric efficiency; semiparametric estimating equations; nonparametric maximum likelihood. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20610" title="STAT 610" class="bubblelink code" onclick="return showCourse(this, 'STAT 610');">STAT 610</a> or <a href="/search/?P=MATH%20710" title="MATH/STAT 710" class="bubblelink code" onclick="return showCourse(this, 'MATH 710');">MATH/STAT 710</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2019</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 840 — STATISTICAL MODEL BUILDING AND LEARNING 3 credits. Theory of reproducing kernel Hilbert spaces in statistical model building; bounded linear functionals and representer theory; smoothing splines; ANOVA spines; degees of freedom for signal and the bias-variance tradeoff; Bayesian confidence intervals; model selection. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20610" title="STAT 610" class="bubblelink code" onclick="return showCourse(this, 'STAT 610');">STAT 610</a> or <a href="/search/?P=MATH%20710" title="MATH/STAT 710" class="bubblelink code" onclick="return showCourse(this, 'MATH 710');">MATH/STAT 710</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2015</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 841 — NONPARAMETRIC STATISTICS AND MACHINE LEARNING METHODS 3 credits. Statistical function estimation and classification; reproducing kernel machines, support vector machines; high dimensional model selection and estimation; Bayesian, empirical Bayesian interpretation of nonparametric learning methods; log density ANOVA and graphical models; tree ensemble methods including bagging, boosting, and random forest. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20610" title="STAT 610" class="bubblelink code" onclick="return showCourse(this, 'STAT 610');">STAT 610</a> or <a href="/search/?P=MATH%20710" title="MATH/STAT 710" class="bubblelink code" onclick="return showCourse(this, 'MATH 710');">MATH/STAT 710</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2019</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 849 — THEORY AND APPLICATION OF REGRESSION AND ANALYSIS OF VARIANCE I 3 credits. Theory and applications of the general linear model; graphical methods; simultaneous inference; regression diagnostics; analysis of variance of fixed, random and mixed effects models; ANCOVA: violations of assumptions. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 850 — THEORY AND APPLICATION OF REGRESSION AND ANALYSIS OF VARIANCE II 3 credits. Theory and applications of the general linear model; graphical methods; simultaneous inference; regression diagnostics; analysis of variance of fixed, random and mixed effects models; ANCOVA: violations of assumptions. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20849" title="STAT 849" class="bubblelink code" onclick="return showCourse(this, 'STAT 849');">STAT 849</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 860 — ESTIMATION OF FUNCTIONS FROM DATA 3 credits. Statistical and appoximation theoretic methods of estimating functions and values of functionals from experimental data; experimental design and data analysis problems that arise as problems in approximation theory; convergence theorems; ill-posed inverse problems; Banach and Hilbert space penalty functionals. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20610" title="STAT 610" class="bubblelink code" onclick="return showCourse(this, 'STAT 610');">STAT 610</a> or <a href="/search/?P=MATH%20710" title="MATH/STAT 710" class="bubblelink code" onclick="return showCourse(this, 'MATH 710');">MATH/STAT 710</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2016</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/COMP SCI/E C E 861 — THEORETICAL FOUNDATIONS OF MACHINE LEARNING 3 credits. Advanced mathematical theory and methods of machine learning. Statistical learning theory, Vapnik-Chevronenkis Theory, model selection, high-dimensional models, nonparametric methods, probabilistic analysis, optimization, learning paradigms. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=E%20C%20E%20761" title="E C E/COMP SCI 761" class="bubblelink code" onclick="return showCourse(this, 'E C E 761');">E C E/COMP SCI 761</a> or <a href="/search/?P=E%20C%20E%20830" title="E C E 830" class="bubblelink code" onclick="return showCourse(this, 'E C E 830');">E C E 830</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/B M I 877 — STATISTICAL METHODS FOR MOLECULAR BIOLOGY 3 credits. Statistical and computational methods in statistical genomics for human and experimental populations. Review methods for quality control, experimental design, clustering, network analysis, and other downstream analysis of next-generation sequencing studies along with methods for genome wide association studies. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Understand the statistical and computational background underlying many state-of-the-art techniques for the pre-processing and analysis of high-throughput genomics datasets Audience: Graduate 2. Identify the appropriateness and limitations of such methods in a variety of settings. Audience: Graduate 3. Discuss scientific problems and identify the statistical and computational aspects embedded in the processing and analysis of genomics datasets. Audience: Graduate 4. Become proficient in select software packages commonly used in analysis of next-generation sequencing data. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT/E C E/MATH 888 — TOPICS IN MATHEMATICAL DATA SCIENCE 1-3 credits. Advanced topics in the mathematical foundations of data science <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2023<div class="bubblehide">Learning Outcomes: 1. Apply advanced mathematical concepts to solve a variety of data science problems Audience: Graduate 2. Analyze rigorously the mathematical properties of methods used in data science Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 990 — RESEARCH 1-12 credits. Independent research and writing for graduate students under the supervision of a faculty member. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 992 — SEMINAR 1-3 credits. Special topics in statistics at the graduate level. Subject matter varies. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> STAT 998 — STATISTICAL CONSULTING 3 credits. Consulting apprenticeship. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div></div> </div> </div>  </div>  <div id="right-col"> <button class="tabs-expand" onclick="toggleTabs();"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg> Select a section…</button> <div id="print-options-box"> <button aria-expanded="false" aria-controls="print-options-list" data-toggle="#print-options-list"> <svg class="main-printer" viewBox="0 0 24 24" aria-hidden="true"> <path d="M18 3v3.984h-12v-3.984h12zM18.984 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