Prerequisite(s): Consent of instructor; graduate standing. Course Description: Introduction to statistical learning; Bayesian paradigm; model selection; simultaneous inference; bootstrap and cross validation; classification and clustering methods; PCA; nonparametric smoothing techniques. 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Prerequisite(s): (STA130A, STA130B); (MAT067 or MAT167); or equivalent of STA130A and 130B, or equivalent of MAT167 or MAT067. Prerequisite: (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 . 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. Thu, May 11, 2023 @ 4:10pm - 5:30pm. Please check the Undergraduate Admissions website for information about admissions requirements. The students will also learn about the core mathematical constructs and optimization techniques behind the methods. Probability 4 STA 131A - Introduction to Probability Theory 4 Statistics 12 STA 108 - Applied Stat Methods . Conditional expectation. ), Statistics: Applied Statistics Track (B.S. Clients are drawn from a pool of University clients. 3 0 obj << Course Description: Programming in R; Summarization and visualization of different data types; Concepts of correlation, regression, classification and clustering. Most UC Davis transfer students come from California community colleges. Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). STA 290 Seminar: Sam Pimentel. Randomized complete and incomplete block design. 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. Topics include statistical functionals, smoothing methods and optimization techniques relevant for statistics. All rights reserved. Program in Statistics - Biostatistics Track. ), Statistics: Applied Statistics Track (B.S. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, factorial designs and analysis of covariance. 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. Applications in the social, biological, and engineering sciences. 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. Please check the Undergraduate Admissions website for information about admissions requirements. Prerequisite(s): Consent of instructor; advancement to candidacy for Ph.D. ), Statistics: Computational Statistics Track (B.S. STA 290 Seminar: Sam Pimentel. Format: Course Description: Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. ), Statistics: Machine Learning Track (B.S. Introduction to computing for data analysis and visualization, and simulation, using a high-level language (e.g., R). Course Description: Descriptive statistics, probability, sampling distributions, estimation, hypothesis testing, contingency tables, ANOVA, regression; implementation of statistical methods using computer package. 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. Concepts of correlation, regression, analysis of variance, nonparametrics. Only two units of credit for students who have previously taken ECS 171. xko{~{@ DR&{P4h`'Rw3J^809+By:q2("BY%Eam}v{Y5~~x{{Qy%qp3rT"x&vW6Y A primary emphasis will be on understanding the methodologies through numerical simulations and analysis of real-world data. endobj ), Prospective Transfer Students-Data Science, Ph.D. Prerequisite(s): STA015A C- or better or STA013 C- or better or STA032 C- or better or STA100 C- or better. 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#) Program in Statistics . In addition, ECS 171 covers both unsupervised and supervised learning methods in one course, whereas STA 142A is dedicated to supervised learning methods only. MAT 108 is recommended. Statistics: Applied Statistics Track (A.B. At minimum, calculus at the level of MAT 16C or 17C or 21C is required. Grade Mode: Letter. Prerequisite(s): STA106; STA108; STA131A; STA131B; STA131C; STA232A; MAT167. Prerequisite(s): (MAT 125B, MAT135A) or STA131A; or consent of instructor. All rights reserved. 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. Prerequisite(s): STA130A; STA130B; or equivalent of STA130A and STA130B. Copyright The Regents of the University of California, Davis campus. Course Description: Second part of a three-quarter sequence on mathematical statistics. 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. Concepts of randomness, probability models, sampling variability, hypothesis tests and confidence interval. The minor is flexible, so that students from most majors can find a path to the minor that serves their needs. Computational data workflow and best practices. . Prerequisite(s): Two years of high school algebra. Interactive data visualization with Web technologies. An Introduction to Statistical Learning, with Applications in R -- James, Witten, Hastie, Modern Multivariate Statistical Techniques, 2nd Ed. Similar topics are covered in STA 131B and 131C. Statistics: Applied Statistics Track (A.B. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, variable transformation, factorial designs and ANCOVA. One-way and two-way fixed effects analysis of variance models. 1 0 obj << Emphasizes foundations. Admissions decisions are not handled by the Department of Statistics. Roussas, Academic Press, 2007None. Course information: MAT 21D, Winter Quarter, 2021 Lectures: Online (asynchronous): lectures will be posted to Canvas on MWF before 5pm. Basic ideas of hypotheses testing, likelihood ratio tests, goodness-of- fit tests. 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. There is no significant overlap with any one of the existing courses. Course Description: Probability concepts; programming in R; exploratory data analysis; sampling distribution; estimation and inference; linear regression; simulations; resampling methods. Course Description: Seminar on advanced topics in probability and statistics. 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. Prospective Transfer Students-Statistics, A.B. ECS 117. Most UC Davis transfer students come from California community colleges. Basics of text mining. Scraping Web pages and using Web services/APIs. Prerequisite(s): Introductory statistics course; some knowledge of vectors and matrices. Prentice Hall, Upper Saddle River, N.J. Instructor: Prof. Peter Hall Lecture times: 11.00 am Mondays, Wednesdays and Fridays, in Olson 223. Course Description: Fundamental concepts and methods in statistical learning with emphasis on supervised learning. Course Description: Descriptive statistics; basic probability concepts; binomial, normal, Student's t, and chi-square distributions. ), Statistics: General Statistics Track (B.S. Prerequisite(s): STA141A C- or better; (STA130A C- or better or STA131A C- or better or MAT135A C- or better); STA131A or MAT135A preferred. One Introductory Statistics Course UC Davis Course STA 13 or 32 or 100; If the courses above are completed pre-matriculation, your major course schedule at UC Davis will be similar to the one below. ), Statistics: Statistical Data Science Track (B.S. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Prerequisite(s): STA130A C- or better or STA131A C- or better or MAT135A C- or better. 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. Course Description: Advanced programming and data manipulation in R. Principles of data visualization. Analysis of variance, F-test. Models for experimental data, measures of dependence, large-sample theory, statistical estimation and inference. Potential Overlap:There is no significant overlap with any one of the existing courses. ), Prospective Transfer Students-Data Science, Ph.D. You are encouraged to contact the Statistics Department's Undergraduate Program Coordinator at. ), Statistics: General Statistics Track (B.S. including: (a) likelihood function; finding MLEs (finding a global maximum of a function) invariance of MLE; some limitations of ML-approach; exponential families; (b) Bayes approach, loss/risk functions; conjugate priors, MSE; bias-variance decomposition, unbiased estimation (2 lect) (IV) Sampling distributions: (5 lect) (a) distributions of transformed random variables; (b) t, F and chi^2 (properties:mgf, pdf, moments); (c) sampling distribution of sample variance under normality; independence of sample mean and sample variance under normality (V) Fisher information CR-lower bound efficiency (5 lect), Confidence intervals and bounds; concept of a pivot; (3 lect), Some elements of hypothesis testing: (5 lect) critical regions, level, size, power function, one-sided and two-sided tests; p-value); NP-framework, perhaps t-test. Regression and correlation, multiple regression. Course Description: Measure-theoretic foundations, abstract integration, independence, laws of large numbers, characteristic functions, central limit theorems. Measures of association. (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). 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. ,1; m"B=n /\zB1Unoj3;w4^+qQg0nS>EYOq,1q@d =_%r*tsP$gP|ar74[1GX!F V Y 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. ECS 111 or MAT 170 or STA 142A. 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. In addition to learning concepts and . Copyright The Regents of the University of California, Davis campus. /Filter /FlateDecode It is designed to continue the integration of theory and applications, and to cover hypothesis testing, and several kinds of statistical methodology. Nonparametric methods; resampling techniques; missing data. Most transfer students start UC Davis at the beginning of their junior year and are usually able to complete their major and university requirements in the next two years. Prerequisite(s): STA231C; STA235A, STA235B, STA235C desirable. ), Statistics: General Statistics Track (B.S. 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. >> endobj Kruskal-Wallis test. Prerequisite(s): MAT021A; MAT021B; MAT021C; MAT022A; consent of instructor. 11 0 obj << Prerequisite(s): MAT016B C- or better or MAT021B C- or better or MAT017B C- or better. Course Description: Principles of supervised and unsupervised statistical learning. Catalog Description:Fundamental concepts and methods in statistical learning with emphasis on supervised learning. Prerequisite(s): STA108 C- or better or STA106 C- or better. Regression. Statistics: Applied Statistics Track (A.B. The statistics undergraduate program at UC Davis offers a large and varied collection of courses in statistical theory, methodology, and application. Analysis of variance, F-test. Course Description: Basics of experimental design. Discussion: 1 hour. 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 . Course Description: Focus on linear and nonlinear statistical models. My friends refer to 131B as the hardest class in the series. Two-sample procedures. Only 2 units of credit allowed to students who have taken course 131A. endstream In addition to learning concepts and heuristics for selecting appropriate methods, the students will also gain programming skills in order to implement such methods. Course Description: Standard and advanced statistical methodology, theory, algorithms, and applications relevant to the analysis of -omics data. 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. ), Statistics: General Statistics Track (B.S. Relation to other probability courses provided by the statistics department at Davis STA 130A: Basic probability concepts/results and estimation theory; STA 200A: More serious in the mathematics of . ), Statistics: Applied Statistics Track (B.S. Use professional level software. STA 131A - Introduction to Probability Theory & B.S. Course Description: Introduction to consulting, in-class consulting as a group, statistical consulting with clients, and in-class discussion of consulting problems. Pass One restricted to Statistics majors. Copyright The Regents of the University of California, Davis campus. Instructor O ce hours: 12.00{2.00 pm Friday TA O ce hours: 12{1 pm Tuesday, 1{2 pm Thursday, 1117 MSB Winter. UC Davis Department of Statistics. >> All rights reserved. Format: Lecture: 3 hours. 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. 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. Prerequisite(s): STA223 or BST223; or consent of instructor. ), Statistics: General Statistics Track (B.S. STA 108 - Regression Analysis . Emphasis on concepts, method and data analysis. Course Description: Classical and Bayesian inference procedures in parametric statistical models. Statistics: Applied Statistics Track (A.B. You can find course articulations for California community colleges using assist.org. 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). Emphasis on practical training. . :Z Program in Statistics - Biostatistics Track, Supervised methods versus unsupervised methods, Linear and quadratic discriminant analysis, Variable selection - AIC and BIC criteria. Xiaodong Li. STA 131A Introduction to Probability Theory. /MediaBox [0 0 662.399 899.999] Most transfer students start UC Davis at the beginning of their junior year and are usually able to complete their major and university requirements in the next two years. Location. 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 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) Emphasizes foundations. 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. All rights reserved. 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. Emphasis on practical consulting and collaboration of statisticians with clients and scientists under instructor supervision. The course material for STA 200A is the same as for STA 131A with the exception that students in STA 200A are given additional advanced reading material and additional homework assignments. Catalog Description: Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. STA 290 Seminar: Aidan Miliff Event Date. 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). This track emphasizes the underlying computer science, engineering, mathematics and statistics methodology and theory, and is especially recommended as preparation for graduate study in data science or related fields. Lecture: 3 hours Course Description: Practical experience in methods/problems of teaching statistics at university undergraduate level. Regularization and cross validation; classification, clustering and dimension reduction techniques; nonparametric smoothing methods. Course Description: Focus on linear statistical models widely used in scientific research. I'm taking 130B and find the material a bit more intuitive than 131A. STA 35C STS 101 2nd Year: Fall. ), Statistics: Machine Learning Track (B.S. ( ), Statistics: General Statistics Track (B.S. Prerequisite: STA 108 C- or better or STA 106 C- or better. Statistical methods. 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 follow the links below to find out more information about our major tracks. 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. Interactive data visualization with Web technologies. Emphasis on concepts, methods and data analysis using SAS. ), Statistics: Machine Learning Track (B.S. A First Course in Probability, 8th Edn. Summary of Course Content: Prerequisite(s): STA131B; or the equivalent of STA131B. It's definitely hard, but so far I'm having a better time with the material than I did with 131A. /Filter /FlateDecode ), Prospective Transfer Students-Data Science, Ph.D. ), Statistics: Machine Learning Track (B.S. Mathematical Statistics and Data Analysis -- by J. RiceMathematical Statistics: A Text for Statisticians and Quantitative Scientists -- by F. J. Samaniego. You are encouraged to contact the Statistics Department's Undergraduate Program Coordinator at. Elective MAT 135A or STA 131A. The course MAT 135A is an introduction to probability theory from purely MAT and more advanced viewpoint. 3rd Year: ), Prospective Transfer Students-Data Science, Ph.D. ), Statistics: Computational Statistics Track (B.S. Please follow the links below to find out more information about our major tracks. The Bachelor of Science has fiveemphases call tracks. Analysis of variance, F-test. Potential Overlap:Similar topics are covered in STA 131B and 131C. You must have a grade point average of 2.0 in all courses required for the minor. Program in Statistics - Biostatistics Track. 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. Program in Statistics - Biostatistics Track. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Prerequisite:STA 131A C- or better or MAT 135A C- or better; consent of instructor. Use of statistical software. -- A. J. Izenman. Course Description: Directed group study. Please note that the courses below have additional prerequisites. Practical applications of widely-used designs, including dose-finding, comparative and cluster randomization designs. Units: 4. 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). Computational reasoning, computationally intensive statistical methods, reading tabular & non-standard data. 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 re-sampling methodology. % 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.
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