sta 131a uc davis
sta 131a uc davis
An Introduction to Statistical Learning, with Applications in R -- James, Witten, Hastie, Modern Multivariate Statistical Techniques, 2nd Ed. Course Description: Introduction to statistical learning; Bayesian paradigm; model selection; simultaneous inference; bootstrap and cross validation; classification and clustering methods; PCA; nonparametric smoothing techniques. Applications in the social, biological, and engineering sciences. Statistics 131A and Mathematics 135A cover the topics in the first part of the course but with more in depth and theoretical orientations. ): 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: Applied Statistics Track (B.S. Course Description: Multivariate normal and Wishart distributions, Hotellings T-Squared, simultaneous inference, likelihood ratio and union intersection tests, Bayesian methods, discriminant analysis, principal component and factor analysis, multivariate clustering, multivariate regression and analysis of variance, application to data. Restrictions: Spring STA 141A. Topics selected from: martingales, Markov chains, ergodic theory. 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. Based on these offerings, a student can complete a Bachelor of Arts or a Bachalor of Science degree in Statistics. Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). Grade Mode: Letter. Roussas, Academic Press, 2007None. At most, one course used in satisfaction of your minor may be applied to your major. Prerequisite(s): STA206; knowledge of vectors and matrices. Format: Prerequisite(s): (STA222 or BST222); (STA223 or BST223). ), Prospective Transfer Students-Data Science, Ph.D. ), Statistics: Machine Learning Track (B.S. ), Statistics: Statistical Data Science Track (B.S. 3 lectures per week will be posted (except for weeks with academic holidays when only 2 lectures will be posted) 3rd Year: Models for experimental data, measures of dependence, large-sample theory, statistical estimation and inference. Admissions to UC Davis is managed by the Undergraduate Admissions Office. It is designed to continue the integration of theory and applications, and to cover hypothesis testing, and several kinds of statistical methodology. Catalog Description:Fundamental concepts and methods in statistical learning with emphasis on supervised learning. ), Statistics: Applied Statistics Track (B.S. Prerequisite(s): STA013 or STA013Y or STA032 or STA100 or STA103. Prepare SAS base programmer certification exam. 130A and STA 130B Mathematical Statistics: Brief Course, dvanced Applied Statistics for the Biological Sciences, Statistics: Applied Statistics Track (A.B. Prerequisite(s): MAT021A; MAT021B; MAT021C; MAT022A; consent of instructor. M.S. STA 290 Seminar: Sam Pimentel. ), Statistics: Applied Statistics Track (B.S. 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. Prerequisite(s): STA106; STA108; STA131C; STA232B; MAT167. Statistics: Applied Statistics Track (A.B. Interactive data visualization with Web technologies. Course Description: Fundamental concepts and methods in statistical learning with emphasis on supervised learning. Course Description: Transformed random variables, large sample properties of estimates. Prerequisite(s): STA106; STA108; STA131A; STA131B; STA131C; STA232A; MAT167. stream Prerequisite: STA 108 C- or better or STA 106 C- or better. Roussas, Academic Press, 2007. 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: Essentials of using relational databases and SQL. These methods are useful for conducting research in applied subjects, and they are appealing to employees and graduate schools seeking students with quantitative skills. 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. Basics of text mining. Discussion: 1 hour. ), Statistics: General Statistics Track (B.S. Prerequisite(s): (EPI 202 or STA 130A or STA 131A or STA 133); EPI 205; a basic epidemiology course (EPI 205 or equivalent). Copyright The Regents of the University of California, Davis campus. Course Description: Multivariate analysis: multivariate distributions, multivariate linear models, data analytic methods including principal component, factor, discriminant, canonical correlation and cluster analysis. Catalog 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. Principles, methodologies and applications of parametric and nonparametric regression, classification, resampling and model selection techniques. 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. Course Description: Programming in R; Summarization and visualization of different data types; Concepts of correlation, regression, classification and clustering. Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. Course Description: Introduction to consulting, in-class consulting as a group, statistical consulting with clients, and in-class discussion of consulting problems. Course Description: Advanced programming and data manipulation in R. Principles of data visualization. Course Description: Linear and nonlinear statistical models emphasis on concepts, methods/data analysis using professional level software. It is designed to continue the integration of theory and applications, and to cover hypothesis testing, and several kinds of statistical methodology. ), Statistics: Applied Statistics Track (B.S. The new Data Science major at UC Davis has been published in the general catalog! Apr 28-29, 2023. International Center, UC Davis. Course Description: Standard and advanced statistical methodology, theory, algorithms, and applications relevant to the analysis of -omics data. Prerequisite(s): STA141B C- or better or (STA141A C- or better, (ECS 010 C- or better or ECS032A C- or better)). 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. bs*dtfh # PzC?nv(G6HuN@ sq7$. ), Statistics: Applied Statistics Track (B.S. ), Statistics: General Statistics Track (B.S. Basic ideas of hypotheses testing, likelihood ratio tests, goodness-of- fit tests. Course Description: Focus on linear and nonlinear statistical models. k#wm/~Aq& >_{cX!Q9J"F\PDk:~y^ y Ei Aw6SWb#(#aBDNe]6_hsqh)X~X2% %af`@H]m6h4 SUxS%l 6j:whN_EGa5=OTkB0a%in=p(4y2(rxX#z"h!hOgoa'j%[c$r=ikV Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, factorial designs and analysis of covariance. Prerequisite(s): STA207 or STA232B; working knowledge of advanced statistical software and the equivalent of STA207 or STA232B. STA 108 ECS 17. UC Davis 2022-2023 General Catalog. ), Statistics: Machine Learning Track (B.S. Nonparametric methods; resampling techniques; missing data. Discussion: 1 hour. Prerequisite(s): MAT021C C- or better; (MAT022A C- or better or MAT027A C- or better or MAT067 C- or better); MAT021D strongly recommended. . Statistics: Applied Statistics Track (A.B. Randomized complete and incomplete block design. & B.S. Selected topics. The computational component has some overlap with STA 141B, where the emphasis is more on data visualization and data preprocessing. Thu, May 11, 2023 @ 4:10pm - 5:30pm. Overlap with ECS 171 is more substantial. Catalog Description: Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. Copyright The Regents of the University of California, Davis campus. 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: Practical experience in methods/problems of teaching statistics at university undergraduate level. At minimum, calculus at the level of MAT 16C or 17C or 21C is required. Copyright The Regents of the University of California, Davis campus. Lecture: 3 hours Course Description: Focus on linear statistical models. 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. Intensive use of computer analyses and real data sets. Course Description: Sign and Wilcoxon tests, Walsh averages. Some topics covered in STA 231A are covered, at a more elementary level, in the sequence STA 131A,B,C. Prerequisite(s): STA131B; STA237A; or the equivalent of STA131B. Prerequisite(s): STA131B; or the equivalent of STA131B. Course Description: Optimization algorithms for solving problems in statistics, machine learning, data analytics. Winter. . University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. One-way and two-way fixed effects analysis of variance models. Lecture: 3 hours Not open for credit to students who have completed Mathematics 135A. Course Description: Alternative approaches to regression, model selection, nonparametric methods amenable to linear model framework and their applications. 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. All rights reserved. ), Statistics: Machine Learning Track (B.S. Both courses cover the fundamentals of the various methods and techniques, their implementation and applications. Pre-Matriculation Course Recommendations: If the courses above are completed pre-matriculation, your major course schedule at UC Davis will be similar to the one below. Analysis of variance, F-test. ), Prospective Transfer Students-Data Science, Ph.D. ), 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. History: Course Description: Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. Prerequisite(s): STA141A C- or better; (STA130A C- or better or STA131A C- or better or MAT135A C- or better); STA131A or MAT135A preferred. 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. School: College of Letters and Science LS Prerequisite(s): STA200A; or consent of instructor. The statistics undergraduate program at UC Davis offers a large and varied collection of courses in statistical theory, methodology, and application. Course Description: Principles and practice of interdisciplinary, collaborative data analysis; complete case study review and team data analysis project. You can find course articulations for California community colleges using assist.org. Prerequisite(s): ((STA222, STA223) or (BST222, BST223)); STA232B; or consent of instructor. ), Statistics: Statistical Data Science Track (B.S. Review computational tools for implementing optimization algorithms (gradient descent, stochastic gradient descent, coordinate descent, Newtons method.). First part of three-quarter sequence on mathematical statistics. 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. Format: Basic ideas of hypotheses testing, likelihood ratio tests, goodness-of- fit tests. Most UC Davis transfer students come from California community colleges. 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. ), Statistics: Applied Statistics Track (B.S. In addition to learning concepts and . ), Prospective Transfer Students-Data Science, Ph.D. Interactive data visualization with Web technologies. Copyright The Regents of the University of California, Davis campus. 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. ~.S|d&O`S4/ COkahcoc B>8rp*OS9rb[!:D >N1*iyuS9QG(r:| 2#V`O~/ 4ClJW@+d Course Description: Multivariate normal distribution; Mahalanobis distance; sampling distributions of the mean vector and covariance matrix; Hotellings T2; simultaneous inference; one-way MANOVA; discriminant analysis; principal components; canonical correlation; factor analysis. STA 131A - Introduction to Probability Theory 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. Course Description: Classical and Bayesian inference procedures in parametric statistical models. The midterm and final examinations will differ from those of 131A in that they will include material covered in the additional reading assignments. Conditional expectation. MAT 108 is recommended. 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. Admissions decisions are not handled by the Department of Statistics. 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. Hypothesis testing and confidence intervals for one and two means and proportions. However, the emphasis in STA 135 is on understanding methods within the context of a statistical model, and their mathematical derivations and broad application domains.
