Course information: MAT 21D, Winter Quarter, 2021 Lectures: Online (asynchronous): lectures will be posted to Canvas on MWF before 5pm. Prerequisite(s): STA015C C- or better or STA106 C- or better or STA108 C- or better. Prerequisite(s): Senior qualifying for honors. UC Davis Peter Hall Conference: Advances in Statistical Data Science. . At minimum, calculus at the level of MAT 16C or 17C or 21C is required. Prerequisite(s): STA131A; STA131B; STA131C; MAT 025; MAT 125A; or equivalent of MAT 025 and MAT 125A. Prerequisite(s): (MAT 125B, MAT135A) or STA131A; or consent of instructor. Units: 4. ), Statistics: Machine Learning Track (B.S. Discussion: 1 hour. STA 108 - Regression Analysis . 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: Topics from balanced and partially balanced incomplete block designs, fractional factorials, and response surfaces. Apr 28-29, 2023. International Center, UC Davis. UC Davis Peter Hall Conference: Advances in Statistical Data Science. ), Statistics: Computational Statistics Track (B.S. Prerequisite(s): MAT021A; MAT021B; MAT021C; MAT022A; consent of instructor. /Type /Page Prerequisite(s): MAT021C C- or better; (MAT022A C- or better or MAT027A C- or better or MAT067 C- or better); MAT021D strongly recommended. Test heavy Caring. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, factorial designs and analysis of covariance. ), Statistics: General Statistics 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. Catalog Description: Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. Basic ideas of hypotheses testing, likelihood ratio tests, goodness-of-fit tests. ), Statistics: Statistical Data Science Track (B.S. Admissions to UC Davis is managed by the Undergraduate Admissions Office. Prerequisite(s): STA206; knowledge of vectors and matrices. Weak convergence in metric spaces, Brownian motion, invariance principle. Introduction to Probability, G.G. Prerequisite(s): Two years of high school algebra. ), Statistics: General Statistics Track (B.S. Prerequisite(s): (STA035A C- or better or STA032 C- or better or STA100 C- or better); (MAT016B (can be concurrent) or MAT017B (can be concurrent) or MAT021B (can be concurrent)). ), Statistics: Machine Learning Track (B.S. Prerequisite: (STA 130B C- or better or STA 131B C- or better); (MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better). Copyright The Regents of the University of California, Davis campus. Prerequisite(s): MAT016B C- or better or MAT017B C- or better or MAT021B C- or better. Prerequisite(s): STA231C; STA235A, STA235B, STA235C recommended. Program in Statistics - Biostatistics Track, Supervised methods versus unsupervised methods, Linear and quadratic discriminant analysis, Variable selection - AIC and BIC criteria. Goals:Students learn how to use a variety of supervised statistical learning methods, and gain an understanding of their relative advantages and limitations. I'm taking 130B and find the material a bit more intuitive than 131A. STA 130B - Mathematical Statistics: Brief Course STA 130A or 131A or MAT 135A : Winter, Spring . 3 0 obj << J} \Ne8pAu~q"AqD2z LjEwD69(-NI3#W3wJ|XRM4l$.z?^YU.*$zIy0IZ5 /H]) G3[LO<=>S#%Ce8g'd/Q-jYY~b}}Dr_9-Me^MnZ(,{[1seh:/$( w*c\SE3kJ_47q(kQP3p FnMP.B\g4hpwsZ4 XMd1vyv@m_gt ,h+3gU *vGoJYO9 T z-7] x Some topics covered in STA 231B are covered, at a more elementary level, in the sequence STA 131A,B,C. Prerequisite(s): STA142A C- or better; (STA130B C- or better or STA131B C- or better); STA131B preferred. These requirements were put into effect Fall 2022. Format: Statistics: Applied Statistics Track (A.B. Regularization and cross validation; classification, clustering and dimension reduction techniques; nonparametric smoothing methods. Course Description: Likelihood and linear regression; generalized linear model; Binomial regression; case-control studies; dose-response and bioassay; Poisson regression; Gamma regression; quasi-likelihood models; estimating equations; multivariate GLMs. Prerequisite(s): STA200A; or consent of instructor. k#wm/~Aq& >_{cX!Q9J"F\PDk:~y^ y Ei Aw6SWb#(#aBDNe]6_hsqh)X~X2% %af`@H]m6h4 SUxS%l 6j:whN_EGa5=OTkB0a%in=p(4y2(rxX#z"h!hOgoa'j%[c$r=ikV In addition to learning concepts and heuristics for selecting appropriate methods, the students will also gain programming skills in order to implement such methods. Catalog Description: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. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Please check the Undergraduate Admissions website for information about admissions requirements. ), Prospective Transfer Students-Data Science, Ph.D. STA 13 or 32 or 100 : Fall, Winter, Spring . University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Prerequisite(s): STA231B; or the equivalent of STA231B. Xiaodong Li. In order to ensure that you are able to transfer to UC Davis with sufficient progress made towards your major, b, Statistics: Applied Statistics Track (A.B. Prerequisite:MAT 021C C- or better; (MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better); MAT 021D strongly recommended. Double Major MS Admissions; Ph.D. Course Description: Special study for advanced undergraduates. Some topics covered in STA 231A are covered, at a more elementary level, in the sequence STA 131A,B,C. ), Statistics: General Statistics Track (B.S. Prerequisite: STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better. Course Description: Probability concepts; programming in R; exploratory data analysis; sampling distribution; estimation and inference; linear regression; simulations; resampling methods. Prerequisite(s): STA206; STA207; STA135; or their equivalents. Regression and correlation, multiple regression. Basic probability, densities and distributions, mean, variance, covariance, Chebyshev's inequality, some special distributions, sampling distributions, central limit theorem and law of large numbers, point estimation, some methods of estimation, interval estimation, confidence intervals for certain quantities, computing sample sizes. STA 130B Mathematical Statistics: Brief Course. Untis: 4.0 endobj One-way and two-way fixed effects analysis of variance models. Restrictions: (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). ), Prospective Transfer Students-Data Science, Ph.D. Mathematical Sciences Building 1147. . Course Description: Topics may include Bayesian analysis, nonparametric and semiparametric regression, sequential analysis, bootstrap, statistical methods in high dimensions, reliability, spatial processes, inference for stochastic process, stochastic methods in finance, empirical processes, change-point problems, asymptotics for parametric, nonparametric and semiparametric models, nonlinear time series, robustness. Prerequisite(s): (STA130A, STA130B); (MAT067 or MAT167); or equivalent of STA130A and 130B, or equivalent of MAT167 or MAT067. You are encouraged to contact the Statistics Department's Undergraduate Program Coordinator at. May be taught abroad. An Introduction to Statistical Learning, with Applications in R -- James, Witten, Hastie, Modern Multivariate Statistical Techniques, 2nd Ed. 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Principles, methodologies and applications of clustering methods, dimension reduction and manifold learning techniques, graphical models and latent variables modeling. Program in Statistics - Biostatistics Track. Lecture: 3 hours Lecturing techniques, analysis of tests and supporting material, preparation and grading of examinations, and use of statistical software. :Z Statistics: Applied Statistics Track (A.B. School: College of Letters and Science LS Catalog Description:Fundamental concepts and methods in statistical learning with emphasis on supervised learning. Prerequisite(s): (MAT016C C- or better or MAT017C C- or better or MAT021C C- or better); (STA013 C- or better or STA013Y C- or better or STA032 C- or better or STA100 C- or better). Course Description: Multivariate analysis: multivariate distributions, multivariate linear models, data analytic methods including principal component, factor, discriminant, canonical correlation and cluster analysis. Please utilize their website for information about admissions requirements and transferring. /Font << /F24 4 0 R /F34 5 0 R /F1 6 0 R /F13 7 0 R >> Admissions decisions are not handled by the Department of Statistics. 11 0 obj << ), Prospective Transfer Students-Data Science, Ph.D. Prerequisite(s): (EPI 202 or STA 130A or STA 131A or STA 133); EPI 205; a basic epidemiology course (EPI 205 or equivalent). Only 2 units of credit allowed to students who have taken course 131A. Prerequisite: STA 108 C- or better or STA 106 C- or better. Prerequisite(s): Consent of instructor; upper division standing. Please follow the links below to find out more information about our major tracks. Only 2 units of credit allowed to students who have taken course 131A . Program in Statistics - Biostatistics Track. zluM;TNNEkn8>"s|yDs+YZ4A+P3+pc-gGF7Piq1.IMw[v(vFI@!oyEgK!'%d"P~}`VU?RS7N4w4Z/8M--\HE?UCt3]L3?64OE`>(x4hF"A7=L&DpufI"Q$*)H$*>BP8YkjpqMYsPBv{R* Please utilize their website for information about admissions requirements and transferring. @tG 0e&N,2@'7V:98-(sU|[ *e$k8 N4i|CS9,w"YrIiWP6s%u Prerequisite(s): ((STA222, STA223) or (BST222, BST223)); STA232B; or consent of instructor. A First Course in Probability, 8th Edn. First part of three-quarter sequence on mathematical statistics. Overview of computer networks, TCP/IP protocol suite, computer-networking applications and protocols, transport-layer protocols, network architectures, Internet Protocol (IP), routing, link-layer protocols, local area and wireless networks, medium access control, physical aspects of data transmission, and network-performance analysis. /Resources 1 0 R Catalog Description:Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. Course Description: Varieties of categorical data, cross-classifications, contingency tables, tests for independence. Lecture: 3 hours Topics include basic concepts in asymptotic theory, decision theory, and an overview of methods of point estimation. General linear model, least squares estimates, Gauss-Markov theorem. The minor is flexible, so that students from most majors can find a path to the minor that serves their needs. Emphasis on concepts, methods, and data analysis. I am aware of how Puckett is as a professor because I had friends who took him for MAT 22A Spring Quarter of Freshman year . Program in Statistics - Biostatistics Track. Prerequisite(s): STA106; STA108; STA131A; STA131B; STA131C; MAT167. Course Description: Examination of a special topic in a small group setting. Course Description: Third part of three-quarter sequence on mathematical statistics. 3 lectures per week will be posted (except for weeks with academic holidays when only 2 lectures will be posted) All rights reserved. ), Prospective Transfer Students-Data Science, Ph.D. UC Davis 2022-2023 General Catalog. Randomized complete and incomplete block design. ,1; m"B=n /\zB1Unoj3;w4^+qQg0nS>EYOq,1q@d =_%r*tsP$gP|ar74[1GX!F V Y Course Description: Sign and Wilcoxon tests, Walsh averages. Prerequisite(s): STA106; STA108; STA131A; STA131B; STA131C; STA232A; MAT167. Interactive data visualization with Web technologies. Course Description: Introductory SAS language, data management, statistical applications, methods. My friends refer to 131B as the hardest class in the series. UC Davis Department of Statistics University of California, Davis , One Shields Avenue, Davis, CA 95616 | 530-752-1011 Please check our Frequently Asked Questions page if you have any questions. 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. STA 131A; STA 131B; STA 131C; MAT 025; MAT 125A; Or equivalent of MAT 025 and MAT 125A. /Filter /FlateDecode Regression and correlation, multiple regression. All rights reserved. Computational data workflow and best practices. ), Statistics: General Statistics Track (B.S. 3rd Year: Course Description: Work experience in statistics. Inferences concerning scale. However, focus in ECS 171 is more on the optimization aspects and on neural networks, while the focus in STA 142A is more on statistical aspects such as smoothing and model selection techniques. I've looked at my friend's 131B material and it's pretty similar, I think 131B is a little bit more theoretical than . Copyright The Regents of the University of California, Davis campus. Course Description: Principles of supervised and unsupervised statistical learning. Polonik does his best to make difficult material understandable, and is a compotent and caring lecturer. 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.
