Prerequisite(s): STA131A C- or better or MAT135A C- or better; consent of instructor. 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. Logit models, linear logistic models. The computational component has some overlap with STA 141B, where the emphasis is more on data visualization and data preprocessing. Univariate and multivariate spectral analysis, regression, ARIMA models, state-space models, Kalman filtering. Prerequisite(s): MAT016B C- or better or MAT021B C- or better or MAT017B C- or better. ), Statistics: Computational Statistics Track (B.S. Prerequisite(s): STA106; STA108; STA131A; STA131B; STA131C; MAT167. Prerequisite(s): STA200A; or consent of instructor. 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. Scraping Web pages and using Web services/APIs. STA 290 Seminar: Sam Pimentel | UC Davis Department of Statistics Goals: Please be sure to check the minor declaration deadline with your College. Prerequisite(s): STA130A; STA130B; or equivalent of STA130A and STA130B. Statistics: Applied Statistics Track (A.B. Topics include resampling methods, regularization techniques in regression and modern classification, cluster analysis and dimension reduction techniques. Statistics: Applied Statistics Track (A.B. Prerequisite: STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better. Program in Statistics - Biostatistics Track, Large sample distribution theory for MLE's and method of moments estimators, Basic ideas of hypotheses testing and significance levels, Testing hypotheses for means, proportions and variances, Tests of independence and homogeneity (contingency tables), The general linear model with and without normality, Analysis of variance: one-way and randomized blocks, Derivation and distribution theory for sums of square, Estimation and testing for simple linear regression. The minor is designed to provide students in other disciplines with opportunities for exposure and skill development in advanced . 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: History: Emphasis on concepts, method and data analysis. All rights reserved. UC Davis Department of Statistics. ): 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.) Statistics 131A and Mathematics 135A cover the topics in the first part of the course but with more in depth and theoretical orientations. ), Statistics: Machine Learning Track (B.S. Course Description: Principles and practice of interdisciplinary, collaborative data analysis; complete case study review and team data analysis project. Most UC Davis transfer students come from California community colleges. ), Statistics: Applied Statistics Track (B.S. Scraping Web pages and using Web services/APIs. ), Statistics: Applied Statistics Track (B.S. 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. Emphasis on concepts, methods and data analysis using SAS. ), Statistics: Computational Statistics Track (B.S. Advanced statistical procedures for analysis of data collected in clinical trials. Multiple comparisons procedures. ), Statistics: Machine Learning Track (B.S. Goals:Students learn how to use a variety of supervised statistical learning methods, and gain an understanding of their relative advantages and limitations. Discussion: 1 hour. Principles, methodologies and applications of clustering methods, dimension reduction and manifold learning techniques, graphical models and latent variables modeling. ), Statistics: Statistical Data Science Track (B.S. STA 131A - Introduction to Probability Theory Statistical methods. 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. Examines principles of collecting, presenting and interpreting data in order to critically assess results reported in the media; emphasis is on understanding polls, unemployment rates, health studies; understanding probability, risk and odds. Course Description: Special study for undergraduates. In contrast, STA 142A focuses more on issues of statistical principles and algorithms inherent in the formulation of the methods, their advantages and limitations, and their actual performance, as evidenced by numerical simulations and data analysis. STA 108 ECS 17. Course Description: Subjective probability, Bayes Theorem, conjugate priors, non-informative priors, estimation, testing, prediction, empirical Bayes methods, properties of Bayesian procedures, comparisons with classical procedures, approximation techniques, Gibbs sampling, hierarchical Bayesian analysis, applications, computer implemented data analysis. Prerequisite(s): STA130A C- or better or STA131A C- or better or MAT135A C- or better. Admissions decisions are not handled by the Department of Statistics. Kruskal-Wallis test. Prerequisite(s): STA131B; STA237A; or the equivalent of STA131B. Processing data in blocks. Questions or comments? endstream 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. Emphasis on practical consulting and collaboration of statisticians with clients and scientists under instructor supervision. & B.S. Not open for credit to students who have completed Mathematics 135A. Copyright The Regents of the University of California, Davis campus. Prerequisite:STA 141A C- or better; (STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better); STA 131A or MAT 135A preferred. STA 141A Fundamentals of Statistical Data Science. 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 . All rights reserved. Units: 4 Format: Lecture: 3 hours Discussion: 1 hour 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. Course Description: Basic statistical principles of clinical designs, including bias, randomization, blocking, and masking. Course Description: Estimation and testing for the general linear model, regression, analysis of designed experiments, and missing data techniques. Experiences taking MAT 167 in the Summer : r/UCDavis - Reddit UC Davis Department of Statistics University of California, Davis , One Shields Avenue, Davis, CA 95616 | 530-752-1011 Course Description: Directed reading, research and writing, culminating in the completion of a senior honors thesis or project under direction of a faculty advisor. STA 131A Introduction to Probability Theory. Topics selected from: martingales, Markov chains, ergodic theory. /Contents 3 0 R Statistics: Applied Statistics Track (A.B. ), Statistics: Statistical Data Science Track (B.S. Prerequisite(s): STA131A; STA232A recommended, not required. ), Statistics: General Statistics Track (B.S. Course Description: Biostatistical methods and models selected from the following: genetics, bioinformatics and genomics; longitudinal or functional data; clinical trials and experimental design; analysis of environmental data; dose-response, nutrition and toxicology; survival analysis; observational studies and epidemiology; computer-intensive or Bayesian methods in biostatistics. In addition to learning concepts and heuristics for selecting appropriate methods, the students will also gain programming skills in order to implement such methods. ), Statistics: General Statistics Track (B.S. STA 131A C- or better or MAT 135A C- or better; consent of instructor. Two-sample procedures. Analysis of variance, F-test. Elective MAT 135A or STA 131A. Course Description: Directed group study. Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). Double Major MS Admissions; Ph.D. STA 231A: Mathematical Statistics I - UC Davis Randomized complete and incomplete block design. ), Statistics: Computational Statistics Track (B.S. Prerequisite(s): Introductory statistics course; some knowledge of vectors and matrices. Untis: 4.0 PLEASE NOTE: These are only guidelines to help prepare yourself to transition to UC Davis with sufficient progress made towards your major. Interactive data visualization with Web technologies. Prerequisite(s): STA206; knowledge of vectors and matrices. 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. Nonparametric methods; resampling techniques; missing data. ), Statistics: Machine Learning Track (B.S. Catalog Description: Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. Restrictions:Not open for credit to students who have completed Mathematics 135A. Emphasizes: hyposthesis testing (including multiple testing) as well as theory for linear models. Topics include linear mixed models, repeated measures, generalized linear models, model selection, analysis of missing data, and multiple testing procedures. Program in Statistics - Biostatistics Track. 3 lectures per week will be posted (except for weeks with academic holidays when only 2 lectures will be posted) Program in Statistics - Biostatistics Track. Analysis of incomplete tables. Prerequisite(s): (STA130B or STA131B) or (STA106, STA108). Similar topics are covered in STA 131B and 131C. if you have any questions about the statistics major tracks. ), Statistics: Statistical Data Science Track (B.S. Course Description: Theory of chemical reaction networks, molecular circuits, DNA self-assembly, DNA sequence design and thermodynamic energy models, and connections to the field of distributed computing.This course version is effective from, and including: Summer Session 1 2023. Prerequisite(s): (EPI 202 or STA 130A or STA 131A or STA 133); EPI 205; a basic epidemiology course (EPI 205 or equivalent). Course Description: Special topics in Statistics appropriate for study at the graduate level. /Filter /FlateDecode Subject: STA 231A STATISTICS 131A | Probability Theory Textbook: Ross, S. (2010). 11 0 obj << General linear model, least squares estimates, Gauss-Markov theorem. Restrictions: Course Description: Fundamental concepts and methods in statistical learning with emphasis on unsupervised learning. Because of the large class size, lectures will be pre-recorded and posted online. ), Prospective Transfer Students-Data Science, Ph.D. STA 131A is an introductory course for probability. Prerequisite(s): STA141B C- or better or (STA141A C- or better, (ECS 010 C- or better or ECS032A C- or better)). ), Statistics: Applied Statistics Track (B.S. Course Description: Fundamental concepts and methods in statistical learning with emphasis on supervised learning. 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). Please utilize their website for information about admissions requirements and transferring. Program in Statistics - Biostatistics Track. Hypothesis testing and confidence intervals for one and two means and proportions. Admissions to UC Davis is managed by the Undergraduate Admissions Office. Discussion: 1 hour. UC Davis Department of Statistics University of California, Davis , One Shields Avenue, Davis, CA 95616 | 530-752-1011 Interactive data visualization with Web technologies. ), Statistics: Statistical Data Science Track (B.S. 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. Lecture: 3 hours The new Data Science major at UC Davis has been published in the general catalog! ), Statistics: Machine Learning Track (B.S. Emphasis on concepts, methods, and data analysis. ), Prospective Transfer Students-Data Science, Ph.D. Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. The deadline to file your minor petition may vary by College. Course Description: Topics in asymptotic theory of statistics chosen from weak convergence, contiguity, empirical processes, Edgeworth expansion, and semiparametric inference. Course Description: Seminar on advanced topics in probability and statistics. Xiaodong Li. Apr 28-29, 2023. International Center, UC Davis. Course Description: 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. ), Prospective Transfer Students-Data Science, Ph.D. 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. STA 131A; STA 131B; STA 131C; MAT 025; MAT 125A; Or equivalent of MAT 025 and MAT 125A. Course Description: Special study for advanced undergraduates. Prerequisite: STA 108 C- or better or STA 106 C- or better. stream 3rd Year: xX[o[~}&15]`'RB6V m3j.|C%`!O_"-Qp.bY}p+cg Kviwv{?Y`o=Oif@#0B=jJ__2n_@z[hw\/:I,UG6{swMQYq:KkVn ES|RJ+HVluV/$fwN_nw2ZMK$46Rx zl""lUn#) Course Description: Guided orientation to original statistical research papers, and oral presentations in class of such papers by students under the supervision of a faculty member. ), Statistics: Machine Learning Track (B.S. Practical applications of widely-used designs, including dose-finding, comparative and cluster randomization designs. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. . UC Davis 2022-2023 General Catalog. 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