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/Filter /FlateDecode STA 135 Multivariate Data Analysis - UC Davis Department of Statistics Format: %PDF-1.5 1 0 obj << All rights reserved. ), Statistics: Machine Learning Track (B.S. You are encouraged to contact the Statistics Department's Undergraduate Program Coordinator at. >> endobj 3 lectures per week will be posted (except for weeks with academic holidays when only 2 lectures will be posted) Prerequisite(s): STA106; STA108; STA131A; STA131B; STA131C; STA232A; MAT167. Prerequisite(s): STA142A C- or better; (STA130B C- or better or STA131B C- or better); STA131B preferred. Review computational tools for implementing optimization algorithms (gradient descent, stochastic gradient descent, coordinate descent, Newtons method.). ), Statistics: Machine Learning Track (B.S. Copyright The Regents of the University of California, Davis campus. ), Statistics: General Statistics Track (B.S. UC Davis Department of Statistics - STA 130B Mathematical Statistics -- A. J. Izenman. Prerequisite(s): STA015A C- or better or STA013 C- or better or STA032 C- or better or STA100 C- or better. Potential Overlap:Statistics 131A and Mathematics 135A cover the topics in the first part of the course but with more in depth and theoretical orientations. 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. ), Prospective Transfer Students-Data Science, Ph.D. Instructor O ce hours: 12.00{2.00 pm Friday TA O ce hours: 12{1 pm Tuesday, 1{2 pm Thursday, 1117 MSB The statistics undergraduate program at UC Davis offers a large and varied collection of courses in statistical theory, methodology, and application. Packaged computer programs, analysis of real data. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, variable transformation, factorial designs and ANCOVA. 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: Practical experience in methods/problems of teaching statistics at university undergraduate level. Course Description: Simple random, stratified random, cluster, and systematic sampling plans; mean, proportion, total, ratio, and regression estimators for these plans; sample survey design, absolute and relative error, sample size selection, strata construction; sampling and nonsampling sources of error. History: Thu, May 4, 2023 @ 4:10pm - 5:30pm. 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. Use of statistical software. 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. Course Description: Time series relationships; univariate time series models: trend, seasonality, correlated errors; regression with correlated errors; autoregressive models; autoregressive moving average models; spectral analysis: cyclical behavior and periodicity, measures of periodicity, periodogram; linear filtering; prediction of time series; transfer function models. Summary of course contents: . ), Statistics: Machine Learning Track (B.S. Copyright The Regents of the University of California, Davis campus. 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. Statistics: Applied Statistics Track (A.B. 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. & B.S. Topics include linear mixed models, repeated measures, generalized linear models, model selection, analysis of missing data, and multiple testing procedures. Prerequisite(s): STA106 C- or better; STA108 C- or better; (STA130B C- or better or STA131B C- or better); STA141A C- or better. All rights reserved. *Choose one of MAT 108 or 127C. Because of the large class size, lectures will be pre-recorded and posted online. Course 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: Special topics in Statistics appropriate for study at the graduate level. Discussion: 1 hour. Course Description: Basics of experimental design. 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): STA141B C- or better or (STA141A C- or better, (ECS 010 C- or better or ECS032A C- or better)). 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. This course is a continuations of STA 130A. Prerequisite(s): MAT021A; MAT021B; MAT021C; MAT022A; consent of instructor. STA 130A Mathematical Statistics: Brief Course (Fall 2016) STA 131A Introduction to Probability Theory (Fall 2017) STA 135 Multivariate Data Analysis (Spring 2016, Spring 2017, Spring 2018, Winter 2019, Spring 2019, Winter 2020, Spring 2020, Winter 2021) Most UC Davis transfer students come from California community colleges. ~.S|d&O`S4/ COkahcoc B>8rp*OS9rb[!:D >N1*iyuS9QG(r:| 2#V`O~/ 4ClJW@+d PDF STA 131A: Introduction to Probability - UC Davis Statistics 131A and Mathematics 135A cover the topics in the first part of the course but with more in depth and theoretical orientations. STA 130B Mathematical Statistics: Brief Course. ), Statistics: Statistical Data Science Track (B.S. Copyright The Regents of the University of California, Davis campus. Prerequisite(s): STA108 C- or better or STA106 C- or better. ), Statistics: Computational Statistics Track (B.S. STA 130B - Mathematical Statistics: Brief Course STA 130A or 131A or MAT 135A : Winter, Spring . UC Davis Department of Statistics - Prospective Transfer Students ), Statistics: Machine Learning Track (B.S. Mathematical Sciences Building 1147. . Course Description: Simple linear regression, variable selection techniques, stepwise regression, analysis of covariance, influence measures, computing packages. STA 108 - Regression Analysis . PLEASE NOTE: These are only guidelines to help prepare yourself to transition to UC Davis with sufficient progress made towards your major. Course Description: Multivariate analysis: multivariate distributions, multivariate linear models, data analytic methods including principal component, factor, discriminant, canonical correlation and cluster analysis. STA 130A Mathematical Statistics: Brief Course. STATISTICS 131A | Probability Theory Textbook: Ross, S. (2010). Computational data workflow and best practices. Both courses cover the fundamentals of the various methods and techniques, their implementation and applications. ECS 116. Prerequisite: STA 108 C- or better or STA 106 C- or better. 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). STA 131B Introduction to Mathematical Statistics. Prospective Transfer Students-Data Science, B.S. | UC Davis Department 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. Basics of text mining. 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. if you have any questions about the statistics major tracks. Course Description: Fundamental concepts and methods in statistical learning with emphasis on supervised learning. Not open for credit to students who have completed Mathematics 135A. Prerequisite: STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better. Format: Transformed random variables, large sample properties of estimates. UC Davis Department of Statistics - Information for Prospective Double Major MS Admissions; Ph.D. Alternative to STA013 for students with a background in calculus and programming. All rights reserved. Course Description: Descriptive statistics, probability, sampling distributions, estimation, hypothesis testing, contingency tables, ANOVA, regression; implementation of statistical methods using computer package. ), Statistics: Statistical Data Science Track (B.S. Emphasizes foundations. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. 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. Discussion: 1 hour. STA 131A C- or better or MAT 135A C- or better; consent of instructor. Prerequisite(s): (STA130B C- or better or STA131B C- or better); (MAT022A C- or better or MAT027A C- or better or MAT067 C- or better). Oh ok. Thing is that MAT 22A is a prereq for STA 131A and the STA 131 series is far from easy, so I would rather play it safe on this one. Admissions to UC Davis is managed by the Undergraduate Admissions Office. /ProcSet [ /PDF /Text ] Goals: This course is a continuations of STA 130A. Intensive use of computer analyses and real data sets. Prerequisite(s): STA206; STA207; STA135; or their equivalents. ), Statistics: Computational Statistics Track (B.S. STA 131A; STA 131B; STA 131C; MAT 025; MAT 125A; Or equivalent of MAT 025 and MAT 125A. Some topics covered in STA 231B are covered, at a more elementary level, in the sequence STA 131A,B,C. The minor is flexible, so that students from most majors can find a path to the minor that serves their needs. Course Description: Introduction to statistical learning; Bayesian paradigm; model selection; simultaneous inference; bootstrap and cross validation; classification and clustering methods; PCA; nonparametric smoothing techniques. Conditional expectation. Multiple comparisons procedures. STA 290 Seminar: Sam Pimentel. Program in Statistics - Biostatistics Track. Prerequisite(s): STA235B or MAT235B; or consent of instructor.