Course Description: Fundamental concepts and methods in statistical learning with emphasis on unsupervised learning. Elective MAT 135A or STA 131A. Lecture: 3 hours Course Description: Introduction to consulting, in-class consulting as a group, statistical consulting with clients, and in-class discussion of consulting problems. Based on these offerings, a student can complete a Bachelor of Arts or a Bachalor of Science degree in Statistics. Format: Lecture: 3 hours. Course Description: Focus on linear and nonlinear statistical models. Includes basics, graphics, summary statistics, data sets, variables and functions, linear models, repetitive code, simple macros, GLIM and GAM, formatting output, correspondence analysis, bootstrap. 2 0 obj << ), Prospective Transfer Students-Data Science, Ph.D. Two-sample procedures. Prerequisite:STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. 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. If you have to take sta 131a, he's not a bad choice because he is generous with his grading scheme, which makes up for the conceptual difficulty and 4 midterms + final (a midterm is dropped). Topics selected from: martingales, Markov chains, ergodic theory. Course Description: Principles and practice of interdisciplinary, collaborative data analysis; complete case study review and team data analysis project. Course Description: Multivariate analysis: multivariate distributions, multivariate linear models, data analytic methods including principal component, factor, discriminant, canonical correlation and cluster analysis. Practical applications of widely-used designs, including dose-finding, comparative and cluster randomization designs. Course Description: Second part of a three-quarter sequence on mathematical statistics. & B.S. STA 108 ECS 17. STA 130A addresses itself to a different audience, and contains a brief introduction to probabilistic concepts at a less sophisticated level. Please follow the links below to find out more information about our major tracks. It is designed to continue the integration of theory and applications, and to cover hypothesis testing, and several kinds of statistical methodology. 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Policy (TTP), Wildlife, Fish, & Conservation Biology (WFC). 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. ), Statistics: Computational Statistics Track (B.S. Most UC Davis transfer students come from California community colleges. Prerequisite(s): (EPI 202 or STA 130A or STA 131A or STA 133); EPI 205; a basic epidemiology course (EPI 205 or equivalent). STA 131A Introduction to Probability Theory. Prerequisite(s): MAT016B C- or better or MAT017B C- or better or MAT021B C- or better. /Font << /F24 4 0 R /F34 5 0 R /F1 6 0 R /F13 7 0 R >> Regularization and cross validation; classification, clustering and dimension reduction techniques; nonparametric smoothing methods. Prerequisite(s): MAT016B C- or better or MAT021B C- or better or MAT017B C- or better. Regression and correlation, multiple regression. ECS 111 or MAT 170 or STA 142A. Potential Overlap:Similar topics are covered in STA 131B and 131C. UC Davis Department of Statistics University of California, Davis , One Shields Avenue, Davis, CA 95616 | 530-752-1011 Conditional expectation. Prerequisite(s): Two years of high school algebra or Mathematics D. Course Description: Principles of descriptive statistics. . Course Description: Special topics in Statistics appropriate for study at the graduate level. Spring STA 141A. Course Description: Standard and advanced methodology, theory, algorithms, and applications relevant for analysis of repeated measurements and longitudinal data in biostatistical and statistical settings. *Choose one of MAT 108 or 127C. Course Description: Directed group study. The computational component has some overlap with STA 141B, where the emphasis is more on data visualization and data preprocessing. 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. Catalog Description:Fundamental concepts and methods in statistical learning with emphasis on supervised learning. ), Statistics: Machine Learning Track (B.S. ECS 117. All rights reserved. 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. Emphasis on practical training. Course Description: Directed group study. Course Description: Essentials of using relational databases and SQL. Prerequisite(s): (STA130A, STA130B); (MAT067 or MAT167); or equivalent of STA130A and 130B, or equivalent of MAT167 or MAT067. Course Description: Research in Statistics under the supervision of major professor. Restrictions:Not open for credit to students who have completed Mathematics 135A. Scraping Web pages and using Web services/APIs. Roussas, Academic Press, 2007. Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. ), Statistics: General Statistics Track (B.S. 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. Course Description: Advanced programming and data manipulation in R. Principles of data visualization. STA 290 Seminar: Aidan Miliff Event Date. STA 141A Fundamentals of Statistical Data Science, STA 141BData & Web Technologies for Data Analysis, STA 141CBig Data & High Performance Statistical Computing, STA 160Practice in Statistical Data Science. ), Statistics: Applied Statistics Track (B.S. /Parent 8 0 R % Questions or comments? Concepts of randomness, probability models, sampling variability, hypothesis tests and confidence interval. Principles, methodologies and applications of parametric and nonparametric regression, classification, resampling and model selection techniques. Prerequisite(s): STA130A; STA130B; or equivalent of STA130A and STA130B. The deadline to file your minor petition may vary by College. Course Description: Linear and nonlinear statistical models emphasis on concepts, methods/data analysis using professional level software. 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). ), Statistics: Applied Statistics Track (B.S. Principles, methodologies and applications of parametric and nonparametric regression, classification, resampling and model selection techniques. ), Statistics: Computational Statistics Track (B.S. Copyright The Regents of the University of California, Davis campus. Prerequisite(s): (STA130B or STA131B) or (STA106, STA108). 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: Guided orientation to original statistical research papers, and oral presentations in class of such papers by students under the supervision of a faculty member. ECS 152A: Computer Networks | Computer Science - UC Davis Prerequisite(s): Consent of instructor. 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. Course Description: Focus on linear statistical models widely used in scientific research. Course Description: Third part of three-quarter sequence on mathematical statistics. Course Description: Random experiments; countable sample spaces; elementary probability axioms; counting formulas; conditional probability; independence; Bayes theorem; expectation; gambling problems; binomial, hypergeometric, Poisson, geometric, negative binomial and multinomial models; limiting distributions; Markov chains. . Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, variable transformation, factorial designs and ANCOVA. STA 130B - Mathematical Statistics: Brief Course STA 130A or 131A or MAT 135A : Winter, Spring . Advanced statistical procedures for analysis of data collected in clinical trials. if you have any questions about the statistics major tracks. Program in Statistics - Biostatistics Track, Supervised methods versus unsupervised methods, Linear and quadratic discriminant analysis, Variable selection - AIC and BIC criteria. Program in Statistics - Biostatistics Track. ), Statistics: Machine Learning Track (B.S. /Type /Page ), Statistics: Machine Learning Track (B.S. ), Statistics: General Statistics Track (B.S. Prerequisite(s): MAT016A (can be concurrent) or MAT017A (can be concurrent) or MAT021A (can be concurrent). Prerequisite(s): STA015A C- or better or STA013 C- or better or STA032 C- or better or STA100 C- or better. 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