Course Title and Description Fall Winter Spring Summer Ecampus ST 201 PRINCIPLES OF STATISTICS (4) Study design, descriptive statistics, the use of probability in statistical arguments, sampling, hypothesis tests and confidence intervals for means and proportions. Lec/rec. PREREQS: High school algebra. ST 201 and ST 202 must be taken in order. X X X X X - F, W, Sp, Su ST 202 PRINCIPLES OF STATISTICS (4) Comparisons of means and proportions between two populations (t-tests, chi-square tests, nonparametric tests), simple linear regression, correlation. Lec/rec. PREREQS: ST 201 [D-] X X - F, W, Sp, Su ST 314 INTRODUCTION TO STATISTICS FOR ENGINEERS (3) Probability, common probability distributions, sampling distributions, estimation, hypothesis testing, control charts, regression analysis, experimental design. PREREQS: (MTH 252 [D-] or MTH 252H [D-] ) X X X X X - F, W, Sp, Su ST 351 INTRODUCTION TO STATISTICAL METHODS (4) Study designs, descriptive statistics, collecting and recording data, probability distributions, sampling distributions for means and proportions, hypothesis testing and confidence intervals for means and proportions in one- and two-sample inference, and chi-square tests. Lec/lab. PREREQS: High school algebra with statistics. ST 351 and ST 352 must be taken in order. X X X X X - F, W, Sp, Su ST 352 INTRODUCTION TO STATISTICAL METHODS (4) Randomization tests and other nonparametric tests for one- and two-sample inference, simple and multiple linear regression, correlation, one- and two-way analysis of variance, logistic regression. Lec/lab. PREREQS: (ST 351 [D-] or ST 351H [D-] ) and ST 351 and ST 352 must be taken in order. X X X X X - F, W, Sp, Su ST 406 PROJECTS (1-16) Section 1: Projects, graded P/N. Section 2: Teaching Experience, graded P/N. Section 3: Directed Work, graded P/N. This course is repeatable for a maximum of 16 credits. X X X X ST 407 SEMINAR (1) Attendance at consulting practicum. Graded P/N. X X X ST 410 INTERNSHIP (1-16) Graded P/N. This course is repeatable for a maximum of 16 credits. X X X X ST 411 METHODS OF DATA ANALYSIS (4) Graphical, parametric and nonparametric methods for comparing two samples; one-way and two-way analysis of variance; simple linear regression. Lec/lab. PREREQS: ST 209 or ST 351 or the equivalent. ST 411, ST 412 and ST 413 must be taken in order. X X X ST 412 METHODS OF DATA ANALYSIS (4) Multiple linear regression, including model checking, dummy variables, using regression to fit analysis of variance models, analysis of covariance, variable selection methods. Lec/lab. PREREQS: ST 411 [D-] and ST 209 or ST 351 or the equivalent. X X X ST 413 METHODS OF DATA ANALYSIS (4) Principles of experimental design; randomized block and factorial designs; repeated measures; categorical data analysis, including comparison of proportions, tests of homogeneity and independence in cross-classified frequency tables, Mantel-Haenszel test, logistic regression, log-linear regression. Introduction to multivariate statistics. Lec/lab. PREREQS: ST 412 [D-] and ST 351 or the equivalent. X ST 415 DESIGN AND ANALYSIS OF PLANNED EXPERIMENTS (3) Principles of experimental design; uses, construction and analysis of completely randomized, randomized block and Latin square designs; covariates; factorial treatments, split plotting; random effects and variance components. PREREQS: (ST 352 [D-] or ST 411 [D-] or ST 511 [D-] ) X ST 421 INTRODUCTION TO MATHEMATICAL STATISTICS (4) Probability, random variables, expectation, discrete and continuous distributions, multivariate distributions. PREREQS: MTH 253. ST 421 and ST 422 must be taken in order. X ST 422 INTRODUCTION TO MATHEMATICAL STATISTICS (4) Sampling distributions, Central Limit Theorem, estimation, confidence intervals, properties of estimators, and hypothesis testing. PREREQS: ST 421 [D-] and MTH 253 X X ST 431 SAMPLING METHODS (3) Estimation of means, totals and proportions; sampling designs including simple random, stratified, cluster, systematic, multistage and double sampling; ratio and regression estimators; sources of errors in surveys; capture-recapture methods. PREREQS: ST 411 or ST 511 X ST 441 PROBABILITY, COMPUTING, AND SIMULATION IN STATISTICS (4) Review of probability, including univariate distributions and limit theorems. Random-number generation and simulation of statistical distributions. Bootstrap estimates of standard error. Variance reduction techniques. Emphasis on the use of computation in statistics using the MATLAB programming language. Lec/lab. PREREQS: (ST 422 [D-] or ST 522 [D-] ) X ST 443 APPLIED STOCHASTIC MODELS (3) Development of stochastic models commonly arising in statistics and operations research, such as Poisson processes, birth-and-death processes, discrete-time and continuous-time Markov chains, renewal and Markov renewal processes. Analysis of stochastic models by simulation and other computational techniques. PREREQS: (ST 421 [D-] or ST 521 [D-] ) and experience with a high-level programming language or mathematical computation package. X ST 499 SPECIAL TOPICS (1-4) This course is repeatable for a maximum of 8 credits. Varies ST 501 RESEARCH (1-16) This course is repeatable for a maximum of 16 credits. PREREQS: Departmental approval required. X X X X ST 503 THESIS (1-16) This course is repeatable for a maximum of 999 credits. PREREQS: Departmental approval required. X X X X ST 505 READING AND CONFERENCE (1-16) This course is repeatable for a maximum of 16 credits. PREREQS: Departmental approval required. X X X X ST 506 PROJECTS (1-16) Section 1: Projects. Section 2: Teaching Experience. Section 3: Directed Work. This course is repeatable for a maximum of 16 credits. X X X X ST 507 SEMINAR (1) Section 1: Attendance at consulting practicum, 1 credit. Section 3: Research Seminar, 1 credit. Section 4: Computing Facilities, 1 credit. All sections graded P/N. This course is repeatable for a maximum of 99 credits. X X X ST 509 CONSULTING PRACTICUM (2) The student provides statistical advice, under faculty guidance, on university-related research projects. This course is repeatable for a maximum of 99 credits. PREREQS: ST 507, section 1 and ST 553, or instructor approval required. X X X ST 510 INTERNSHIP (1-16) Graded P/N. This course is repeatable for a maximum of 16 credits. X X X X ST 511 METHODS OF DATA ANALYSIS (4) Graphical, parametric and nonparametric methods for comparing two samples; one-way and two-way analysis of variance; simple linear regression. Lec/lab. PREREQS: ST 209 or ST 351 or the equivalent. ST 511, ST 512, and ST 513 must be taken in order. X X X ST 512 METHODS OF DATA ANALYSIS (4) Multiple linear regression, including model checking, dummy variables, using regression to fit analysis of variance models, analysis of covariance, variable selection methods. Lec/lab. PREREQS: ST 511 [D-] and ST 209 or ST 351 or the equivalent. X X ST 513 METHODS OF DATA ANALYSIS (4) Principles of experimental design; randomized block and factorial designs; repeated measures; categorical data analysis, including comparison of proportions, tests of homogeneity and independence in cross-classified frequency tables, Mantel-Haenszel test, logistic regression, log-linear regression. Introduction to multivariate statistics. Lec/lab. PREREQS: ST 512 [D-] and ST 351 or the equivalent. X ST 515 DESIGN AND ANALYSIS OF PLANNED EXPERIMENTS (3) Principles of experimental design; uses, construction and analysis of completely randomized, randomized block and Latin square designs; covariates; factorial treatments, split plotting; random effects and variance components. PREREQS: ST 352 or (ST 411 or ST 511) X X Spring ST 516 FOUNDATIONS OF DATA ANALYTICS (4)   Foundations of estimation and hypothesis testing; desirable properties of estimators; maximum likelihood; one- and two-sample problems; theoretical results are explored through simulations and analysis using R. Offered via Ecampus only. X Fall ST 517 DATA ANALYTICS I (4)  Methods for modeling quantitative data andand statistical learning--simple and multiple linear regression; linear mixed effects models; data imputation; prediction and cross-validation; scaling up to large datasets. Simulations and data analysis using R. Offered via Ecampus only. Prerequisites: ST 516 with C+ or better. X Winter ST 518 DATA ANALYTICS II (4)  Statistical methods and data analysis techniques for count data. Topics include tests for tables of counts, logistic regression, log-linear regression, generalized linear mixed models, and issues for large datasets. Data analysis in R. Prerequisites: ST 517 with C+ or better. X Spring ST 521 INTRODUCTION TO MATHEMATICAL STATISTICS (4) Probability, random variables, expectation, discrete and continuous distributions, multivariate distributions. PREREQS: MTH 253. ST 521 and ST 522 must be taken in order. X X ST 522 INTRODUCTION TO MATHEMATICAL STATISTICS (4) Sampling distributions, Central Limit Theorem, estimation, confidence intervals, properties of estimators, and hypothesis testing. PREREQS: ST 521 [D-] and MTH 253 X X ST 525. APPLIED SURVIVAL ANALYSIS. (3 Credits) Statistical methods for analyzing survival data or time-to-event data, which may be censored and/or truncated. Specific topics can vary term to term, and could include Kaplan-Meier estimator; K-sample hypothesis tests for survival data; Accelerated failure time model; Cox proportional hazard regression model.Prerequisites: ST 516 with C or better and ST 517 [C] and ST 518 [C] X Fall ST 531 SAMPLING METHODS (3) Estimation of means, totals and proportions; sampling designs including simple random, stratified, cluster, systematic, multistage and double sampling; ratio and regression estimators; sources of errors in surveys; capture-recapture methods. PREREQS: ST 411 or ST 511 X ST 537. DATA VISUALIZATION. (3 Credits) Perceptual principles for displaying data; critique and improvement of data visualizations; use of color in visualization; principles of tidy data; strategies for data exploration; select special topics.Prerequisites: ST 512 with C or better or ST 517 with C or better or ST 552 with C or better X Spring ST 538. MODERN STATISTICAL METHODS FOR LARGE AND COMPLEX DATA SETS. (3 Credits)Provides students with the tools and experience to analyze big and messy data and work effectively in a data science team. Covers the tools to handle big data and answer statistical questions based on the data. Includes three big data analysis projects that students work on in groups. Focuses on proper use of modern data analysis techniques related to regression, classification and clustering for data coming from a variety of application fields. R will be the lingua franca.Prerequisites: ST 512 with C or better or ST 517 with C or better or ST 552 with C or better or ST 412 with C or better X Spring ST 541 PROBABILITY, COMPUTING, AND SIMULATION IN STATISTICS (4) Review of probability, including univariate distributions and limit theorems. Random-number generation and simulation of statistical distributions. Bootstrap estimates of standard error. Variance reduction techniques. Emphasis on the use of computation in statistics using the S-Plus or MATLAB programming language. Lec/lab. PREREQS: ST 422 or ST 522 X ST 543 APPLIED STOCHASTIC MODELS (3) Development of stochastic models commonly arising in statistics and operations research, such as Poisson processes, birth-and-death processes, discrete-time and continuous-time Markov chains, renewal and Markov renewal processes. Analysis of stochastic models by simulation and other computational techniques. PREREQS: (ST 421 or ST 521) and experience with a high-level programming language or mathematical computation package. X ST 551 STATISTICAL METHODS (4) Properties of t, chi-square and F tests; randomized experiments; sampling distributions and standard errors of estimators, delta method, comparison of several groups of measurements; two-way tables of measurements. PREREQS: ST 422 or ST 522. Should concurrently enroll in MTH 341. ST 551, ST 552 and ST 553 must be taken in order. X ST 552 STATISTICAL METHODS (4) Simple and multiple linear regression including polynomial regression, indicator variables, weighted regression, and influence statistics, nonlineral regression and linear models for binary data. PREREQS: ST 551 [D-] and ST 422 or ST 522. Concurrent enrollment in MTH 341. X ST 553 STATISTICAL METHODS (4) Principles and analysis of designed experiments, including factorial experiments, analysis of covariance, random and mixed effect models. Lec/lab. PREREQS: ST 552 [D-] and concurrent enrollment in MTH 341. X ST 558. MULTIVARIATE ANALYTICS. (3 Credits)Basics of matrix algebra, principal components analysis, cluster analysis, factor analysis, multidimensional scaling.Prerequisites: ST 518 with C- or better X Fall ST 559 BAYESIAN STATISTICS (3) Bayesian statistics for data analysis. Characterizations of probability; comparative (Bayesian versus frequentist) inference; prior, posterior and predictive distributions; hierarchical modeling. Computational methods include Markov Chain Monte Carlo for posterior simulation. PREREQS: ST 562 X ST 561 THEORY OF STATISTICS (3) Distributions of functions of random variables, joint and conditional distributions, sampling distributions, convergence concepts, order statistics. PREREQS: (ST 422 or ST 522). ST 561, ST 562, and ST 563 must be taken in order. X ST 562 THEORY OF STATISTICS (3) Sufficiency, exponential families, location and scale families; point estimation: maximum likelihood, Bayes, and unbiased estimators; asymptotic distributions of maximum likelihood estimators; Taylor series approximations. PREREQS: ST 561 [D-] and ST 422 or ST 522 X ST 563 THEORY OF STATISTICS (3) Hypothesis testing: likelihood ratio, Bayesian, and uniformly most powerful tests; similar tests in exponential families; asymptotic distributions of likelihood ratio test statistics; confidence intervals. PREREQS: ST 562 [D-] and ST 422 or ST 522 X ST 566. TIME SERIES ANALYTICS. (3 Credits)Focuses on statistical and analytical tools for analyzing data that are observed sequentially over time. Specific topics can vary term to term, and could include methods for exploratory time series analysis, linear time series models (ARMA, ARIMA), forecasting, spectral analysis and state-space models. The focus will be on applied problems, though some mathematical statistics is necessary for a solid understanding of the statistical issues. This course is designed for students in Data Analytics MS and Certificate programs.Prerequisites: ST 516 with C or better and ST 517 [C] and ST 518 [C] X Winter ST 592. STATISTICAL METHODS FOR GENOMICS RESEARCH. (3 Credits)Lectures include an overview of statistical methods commonly applied in genomics research. Specific methods can vary term to term, and could include cluster analysis, decision trees, dimension reduction tools, regression models, multiple testing adjustment, variable selection methods, etc. Journal clubs include team-based review and presentations of landmark papers in both statistical methodology and genomics research. Research experience includes whole-term collaboration between students from statistics and other disciplines on real projects. X Winter ST 595. CAPSTONE PROJECT. (3 Credits)  Provides an opportunity for students to integrate and apply the analytics skills learned in MS in Data Analytics program to solve real-world problems and to interpret and communicate their results. Student teams will engage in the entire process of solving data science projects in realistic settings, from placing the problem into appropriate statistical framework to applying suitable analytic methods to the problem. Problem solving, written and oral communication skills will be emphasized.Prerequisites: ST 516 with C or better and ST 517 [C] and ST 518 [C] and ST 558 [C] DATA ANALYTICS MS STUDENTS ONLY X F,W,Sp ST 599 SPECIAL TOPICS (1-4) May be repeated for credit when topic varies. This course is repeatable for a maximum of 16 credits. varies ST 601 RESEARCH (1-16) This course is repeatable for a maximum of 16 credits. PREREQS: Instructor approval required. X X X ST 603 THESIS (1-16) This course is repeatable for a maximum of 999 credits. PREREQS: Instructor approval required. X X X ST 606 PROJECTS (1-16) This course is repeatable for a maximum of 16 credits. X X X ST 623 GENERALIZED REGRESSION MODELS I (3) Maximum likelihood analysis for frequency data; regression-type models for binomial and Poisson data; iterative weighted least squares and maximum likelihood; analysis of deviance and residuals; overdispersion and quasi-likelihood models; log-linear models for multidimensional contingency tables. PREREQS: (ST 553 [D-] and ST 563 [D-] ) X ST 625 GENERALIZED REGRESSION MODELS II (3) Parametric methods for the analysis of censored survival data, based mostly on large-sample likelihood theory. Specific topics include the Kaplan-Meier estimator, the log-rank test, partial likelihood, and regression models, including the Cox proportional-hazards model and its generalizations. PREREQS: (ST 553 [D-] or ST 563 [D-] ) X

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