Course Description: Basic statistical principles of clinical designs, including bias, randomization, blocking, and masking. /Contents 3 0 R /ProcSet [ /PDF /Text ] Course Description: Likelihood and linear regression; generalized linear model; Binomial regression; case-control studies; dose-response and bioassay; Poisson regression; Gamma regression; quasi-likelihood models; estimating equations; multivariate GLMs. The statistics undergraduate program at UC Davis offers a large and varied collection of courses in statistical theory, methodology, and application. Prerequisite(s): Senior qualifying for honors. Title: Mathematical Statistics I Potential Overlap:There is no significant overlap with any one of the existing courses. It is designed to continue the integration of theory and applications, and to cover hypothesis testing, and several kinds of statistical methodology. Overview of computer networks, TCP/IP protocol suite, computer-networking applications and protocols, transport-layer protocols, network architectures, Internet Protocol (IP), routing, link-layer protocols, local area and wireless networks, medium access control, physical aspects of data transmission, and network-performance analysis. Prerequisite(s): STA130B C- or better or STA131B C- or better. Principles, methodologies and applications of parametric and nonparametric regression, classification, resampling and model selection techniques. Course Description: Research in Statistics under the supervision of major professor. Course Description: Introduction to consulting, in-class consulting as a group, statistical consulting with clients, and in-class discussion of consulting problems. All rights reserved. >> endobj STA 231B: Mathematical Statistics II | UC Davis Department of Statistics Prerequisite(s): (STA013 C- or better or STA013Y C- or better or STA032 C- or better or STA100 C- or better); (MAT016B C- or better or MAT017B C- or better or MAT021B C- or better). STA 131B Introduction to Mathematical Statistics. ), Statistics: General Statistics Track (B.S. Prerequisite(s): (STA130B or STA131B) or (STA106, STA108). 1 0 obj << Prospective Transfer Students-Data Science, B.S. | UC Davis Department Most transfer students start UC Davis at the beginning of their junior year and are usually able to complete their major and university requirements in the next two years. Course Description: Comprehensive treatment of nonparametric statistical inference, including the most basic materials from classical nonparametrics, robustness, nonparametric estimation of a distribution function from incomplete data, curve estimation, and theory of re-sampling methodology. UC Davis Course ECS 32A or 36A (or former courses ECS 10 or 30 or 40) UC Davis Course ECS 32B (or former course ECS 60) is also strongly recommended. Prerequisite(s): (MAT 125B, MAT135A) or STA131A; or consent of instructor. Xiaodong Li - Teaching - UC Davis Processing data in blocks. Double Major MS Admissions; Ph.D. Location. >> Course Description: Basic experimental designs, two-factor ANOVA without interactions, repeated measures ANOVA, ANCOVA, random effects vs. fixed effects, multiple regression, basic model building, resampling methods, multiple comparisons, multivariate methods, generalized linear models, Monte Carlo simulations. Regression and correlation, multiple regression. Course Description: Special study for advanced undergraduates. Prerequisite(s): STA231B; or the equivalent of STA231B. Roussas, Academic Press, 2007None. Transformed random variables, large sample properties of estimates. Course Description: Basics of experimental design. The PDF will include all information unique to this page. Analysis of incomplete tables. Program in Statistics - Biostatistics Track, Intro (2 lect. Please follow the links below to find out more information about our major tracks. ), Statistics: Applied Statistics Track (B.S. History: Course Description: Sign and Wilcoxon tests, Walsh averages. Please check the Undergraduate Admissions website for information about admissions requirements. Course Description: Directed group study. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, variable transformation, factorial designs and ANCOVA. There is no significant overlap with any one of the existing courses. Course Description: Numerical analysis; random number generation; computer experiments and resampling techniques (bootstrap, cross validation); numerical optimization; matrix decompositions and linear algebra computations; algorithms (markov chain monte carlo, expectation-maximization); algorithm design and efficiency; parallel and distributed computing. Analysis of variance, F-test. Packaged computer programs, analysis of real data. Basics of Probability Theory, Multivariate normal Basics of Decision Theory (decision space, decision rule, loss, risk) Exponential families; MLE; Sufficiency, Cramer-Rao Inequality Asymptotics with application to MLEs (and generalization to M-estimation)Illustrative Reading: The 92 credit major aims to provide a foundation in the theory and methodology behind data science, and to prepare students for more advanced studies. If you want to have completion of a minor certified on your transcript, you must submit an online Minor Declaration Form by the 10th day of instruction of the quarter that you are graduating. Prerequisite(s): STA207 or STA232B; working knowledge of advanced statistical software and the equivalent of STA207 or STA232B. Admissions to UC Davis is managed by the Undergraduate Admissions Office. Topics include algorithms; design; debugging and efficiency; object-oriented concepts; model specification and fitting; statistical visualization; data and text processing; databases; computer systems and platforms; comparison of scientific programming languages. Test heavy Caring. /Parent 8 0 R Why Choose UC Davis? ), Statistics: General Statistics Track (B.S. Statistics 131A and Mathematics 135A cover the topics in the first part of the course but with more in depth and theoretical orientations. including: (a) likelihood function; finding MLEs (finding a global maximum of a function) invariance of MLE; some limitations of ML-approach; exponential families; (b) Bayes approach, loss/risk functions; conjugate priors, MSE; bias-variance decomposition, unbiased estimation (2 lect) (IV) Sampling distributions: (5 lect) (a) distributions of transformed random variables; (b) t, F and chi^2 (properties:mgf, pdf, moments); (c) sampling distribution of sample variance under normality; independence of sample mean and sample variance under normality (V) Fisher information CR-lower bound efficiency (5 lect), Confidence intervals and bounds; concept of a pivot; (3 lect), Some elements of hypothesis testing: (5 lect) critical regions, level, size, power function, one-sided and two-sided tests; p-value); NP-framework, perhaps t-test. In addition to learning concepts and heuristics for selecting appropriate methods, the students will also gain programming skills in order to implement such methods. /Filter /FlateDecode Probability and Statistics by Mark J. Schervish, Morris H. DeGroot 4th Edition 2014, Pearson, University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Possible textbooks covering (parts) of the 231-sequence: J. Shao (2003), Mathematical Statistics, Springer; P. Bickel and K. Doksum (2001): Mathematical Statistics 2nd ed., Pearson Prentice HallPotential Course Overlap: ), Prospective Transfer Students-Data Science, Ph.D. Pass One restricted to Statistics majors. Prerequisite:STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better. UC Davis 2022-2023 General Catalog. Use professional level software. Apr 28-29, 2023. International Center, UC Davis. Only two units of credit for students who have previously taken ECS 171. Copyright The Regents of the University of California, Davis campus. Grade Mode: Letter. Course Description: Advanced topics in time series analysis and applications. STA 130B - Mathematical Statistics: Brief Course STA 130A or 131A or MAT 135A : Winter, Spring . Course Description: In-depth examination of a special topic in a small group setting. Course Description: Seminar on advanced topics in probability and statistics. STA 131A is an introductory course for probability. Prerequisite(s): MAT016A (can be concurrent) or MAT017A (can be concurrent) or MAT021A (can be concurrent). ), Prospective Transfer Students-Data Science, Ph.D. Admissions to UC Davis is managed by the Undergraduate Admissions Office. Regression and correlation, multiple regression. M.S. Lecture: 3 hours You are encouraged to contact the Statistics Department's Undergraduate Program Coordinator atstat-advising@ucdavis.eduif you have any questions about the statistics major tracks. /Resources 1 0 R Not open for credit to students who have completed Mathematics 135A. Prerequisite(s): MAT021A; MAT021B; MAT021C; MAT022A; consent of instructor. The course STA 130A with which it is somewhat related, is the first part of a two part course, STA 130A,B covering both probability and statistical inference. Restrictions:Not open for credit to students who have completed Mathematics 135A. ), Statistics: General Statistics Track (B.S. I am aware of how Puckett is as a professor because I had friends who took him for MAT 22A Spring Quarter of Freshman year . University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. All rights reserved. STA 130A Mathematical Statistics: Brief Course. ), Statistics: Machine Learning Track (B.S. Models for experimental data, measures of dependence, large-sample theory, statistical estimation and inference. Course Description: Descriptive statistics; probability; random variables; expectation; binomial, normal, Poisson, other univariate distributions; joint distributions; sampling distributions, central limit theorem; properties of estimators; linear combinations of random variables; testing and estimation; Minitab computing package. Principles, methodologies and applications of parametric and nonparametric regression, classification, resampling and model selection techniques. Prerequisite:(MAT 016C C- or better or MAT 017C C- or better or MAT 021C C- or better); (STA 013 C- or better or STA 013Y C- or better or STA 032 C- or better or STA 100 C- or better). General linear model, least squares estimates, Gauss-Markov theorem. Copyright The Regents of the University of California, Davis campus. MAT 108 is recommended. Computational data workflow and best practices. ), Statistics: Statistical Data Science Track (B.S. A high level programming language like R or Python will be used for the computation, and students will become familiar with using existing packages for implementing specific methods. Prerequisite(s): Consent of instructor; high school algebra. Prospective Transfer Students-Statistics, A.B. ), Statistics: Applied Statistics Track (B.S. Description. Catalog Description:Transformed random variables, large sample properties of estimates. STATISTICS 131A | Probability Theory Textbook: Ross, S. (2010). /Filter /FlateDecode The midterm and final examinations will differ from those of 131A in that they will include material covered in the additional reading assignments. Prerequisite:STA 131A C- or better or MAT 135A C- or better; consent of instructor. Program in Statistics. Topics selected from: martingales, Markov chains, ergodic theory. STA 130A - Mathematical Statistics: Brief Course (MAT 16C or 17C or 21C); (STA 13 or 32 or 100) Fall, Winter . Please note that the courses below have additional prerequisites. ), Statistics: General Statistics Track (B.S. ), Statistics: Machine Learning Track (B.S. Overlap with ECS 171 is more substantial. Fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, laws of large numbers and the central limit theorem. ), Statistics: Applied Statistics Track (B.S. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. PDF STATISTICS COURSE PREREQUISITES & TENTATIVE SCHEDULE - UC Davis Course Description: Principles and practice of interdisciplinary, collaborative data analysis; complete case study review and team data analysis project. ), Statistics: Computational Statistics Track (B.S. STA 141A Fundamentals of Statistical Data Science. Computational reasoning, computationally intensive statistical methods, reading tabular & non-standard data. Alternative to STA013 for students with a background in calculus and programming. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Introduction to computing for data analysis and visualization, and simulation, using a high-level language (e.g., R). Course Description: Simple linear regression, variable selection techniques, stepwise regression, analysis of covariance, influence measures, computing packages. All rights reserved. The minor is designed to provide students in other disciplines with opportunities for exposure and skill development in advanced statistical methods. UC Davis Department of Statistics - Information for Prospective In addition to learning concepts and . Although the two courses, MAT 135A and STA 131A discuss many of the same topics, the orientation and the nature of the discussion are quite distinct. Catalog Description:Fundamental concepts and methods in statistical learning with emphasis on supervised learning. At minimum, calculus at the level of MAT 16C or 17C or 21C is required. Course Description: Measure-theoretic foundations, abstract integration, independence, laws of large numbers, characteristic functions, central limit theorems. MAT 108 is recommended. ), Statistics: Statistical Data Science Track (B.S. Prerequisite(s): STA223 or BST223; or consent of instructor. Course Description: Work experience in statistics. ), Prospective Transfer Students-Data Science, Ph.D. General Catalog - Statistics (STA) - UC Davis Prerequisite(s): STA131B; or the equivalent of STA131B. Course Description: Programming in R; Summarization and visualization of different data types; Concepts of correlation, regression, classification and clustering. Course Description: High-performance computing in high-level data analysis languages; different computational approaches and paradigms for efficient analysis of big data; interfaces to compiled languages; R and Python programming languages; high-level parallel computing; MapReduce; parallel algorithms and reasoning. STA 131B Introduction to Mathematical Statistics. Prerequisite(s): STA131B; STA237A; or the equivalent of STA131B. UC Davis Department of Statistics - STA 131A Introduction to Course Description: Examination of a special topic in a small group setting. Prerequisite(s): MAT016B C- or better or MAT017B C- or better or MAT021B C- or better. ), Statistics: Machine Learning Track (B.S. ): Concept of a statistical model; observations as random variables, definition/examples of a statistic, statistical inference and examples throughout the entire course: emphasize the difference between population quantities, random variables and observables, Methods of estimation: MLEs, Bayes, MOM (5 lect.) Questions or comments? Similar topics are covered in STA 131B and 131C. Course Description: Comprehensive treatment of nonparametric statistical inference, including the most basic materials from classical nonparametrics, robustness, nonparametric estimation of a distribution function from incomplete data, curve estimation, and theory of resampling methodology. In order to ensure that you are able to transfer to UC Davis with sufficient progress made towards your major, below is information regarding the courses you are recommended to take before transferring. Course Description: Advanced study in various fields of statistics with emphasis in applied topics, presented by members of the Graduate Group in Statistics and other guest speakers. Prerequisite(s): MAT021C C- or better; (MAT022A C- or better or MAT027A C- or better or MAT067 C- or better); MAT021D strongly recommended. ), Statistics: Machine Learning Track (B.S. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, factorial designs and analysis of covariance. Processing data in blocks. The Department offers a minor program in Statistics that consists of five upper division level courses focusing on the fundamentals of mathematical statistics and of the most widely used applied statistical methods. ECS 232: Theory of Molecular Computation | Computer Science