Course Description: First part of three-quarter sequence on mathematical statistics. Some topics covered in STA 231B are covered, at a more elementary level, in the sequence STA 131A,B,C. Prerequisite(s): Introductory, upper division statistics course; some knowledge of vectors and matrices; STA106 or STA108 or the equivalent suggested. Principles, methodologies and applications of clustering methods, dimension reduction and manifold learning techniques, graphical models and latent variables modeling. STA 290 Seminar: Sam Pimentel. First part of three-quarter sequence on mathematical statistics. /Parent 8 0 R Prerequisite(s): STA130B C- or better or STA131B C- or better. Use of statistical software. All rights reserved. Possible textbooks covering (parts) of the 231-sequence: J. Shao (2003), Mathematical Statistics, Springer; P. Bickel and K. Doksum (2001): Mathematical Statistics 2nd ed., Pearson Prentice HallPotential Course Overlap: 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. 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. Course Description: Directed reading, research and writing, culminating in the completion of a senior honors thesis or project under direction of a faculty advisor. 1 0 obj << 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: 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. /Length 2087 ), Prospective Transfer Students-Data Science, Ph.D. One-way random effects model. Course Description: Standard and advanced statistical methodology, theory, algorithms, and applications relevant to the analysis of -omics data. Inferences concerning scale. Course Description: In-depth examination of a special topic in a small group setting. Prerequisite(s): (STA222 or BST222); (STA223 or BST223). Course Description: Descriptive statistics, probability, sampling distributions, estimation, hypothesis testing, contingency tables, ANOVA, regression; implementation of statistical methods using computer package. Concepts of randomness, probability models, sampling variability, hypothesis tests and confidence interval. Only 2 units of credit allowed to students who have taken course 131A . ), Statistics: General Statistics Track (B.S. The PDF will include all information unique to this page. Prerequisite: (STA 130B C- or better or STA 131B C- or better); (MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better). Location. ), Statistics: Machine Learning Track (B.S. ), Statistics: Applied Statistics Track (B.S. Course Description: Advanced programming and data manipulation in R. Principles of data visualization. Restrictions:Not open for credit to students who have completed Mathematics 135A. Prerequisite(s): MAT016B C- or better or MAT017B C- or better or MAT021B C- or better. Principles, methodologies and applications of parametric and nonparametric regression, classification, resampling and model selection techniques. ), Statistics: Computational Statistics Track (B.S. 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) 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: Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, variable transformation, factorial designs and ANCOVA. Course Description: Basic statistical principles of clinical designs, including bias, randomization, blocking, and masking. Prerequisite(s): (STA130B or STA131B) or (STA106, STA108). Prospective Transfer Students-Statistics, A.B. Program in Statistics - Biostatistics Track. Prerequisite:STA 131A C- or better or MAT 135A C- or better; consent of instructor. Discussion: 1 hour. Course Description: Principles and practice of interdisciplinary collaboration in statistics, statistical consulting, ethical aspects, and basics of data analysis and study design. STA 130B - Mathematical Statistics: Brief Course STA 130A or 131A or MAT 135A : Winter, Spring . The deadline to file your minor petition may vary by College. Course Description: Incomplete data; life tables; nonparametric methods; parametric methods; accelerated failure time models; proportional hazards models; partial likelihood; advanced topics. . Untis: 4.0 Prerequisite: STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better. The minor is flexible, so that students from most majors can find a path to the minor that serves their needs. @tG 0e&N,2@'7V:98-(sU|[ *e$k8 N4i|CS9,w"YrIiWP6s%u Emphasizes: hyposthesis testing (including multiple testing) as well as theory for linear models. 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. STA 130B Mathematical Statistics: Brief Course. The course MAT 135A is an introduction to probability theory from purely MAT and more advanced viewpoint. Both courses cover the fundamentals of the various methods and techniques, their implementation and applications. A high level programming language like R or Python will be used for the computation, and students will become familiar with using existing packages for implementing specific methods. STA 131A - Introduction to Probability Theory Lecture: 3 hours 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): STA231B; or the equivalent of STA231B. Course Description: Fundamental concepts and methods in statistical learning with emphasis on unsupervised learning. Models for experimental data, measures of dependence, large-sample theory, statistical estimation and inference. ), Statistics: Applied Statistics Track (B.S. Prerequisite(s): (STA035A C- or better or STA032 C- or better or STA100 C- or better); (MAT016B (can be concurrent) or MAT017B (can be concurrent) or MAT021B (can be concurrent)). 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). UC Davis 2022-2023 General Catalog. Program in Statistics - Biostatistics Track. Copyright The Regents of the University of California, Davis campus. STA 35C STS 101 2nd Year: Fall. These methods are useful for conducting research in applied subjects, and they are appealing to employees and graduate schools seeking students with quantitative skills. ~.S|d&O`S4/ COkahcoc B>8rp*OS9rb[!:D >N1*iyuS9QG(r:| 2#V`O~/ 4ClJW@+d There is no significant overlap with any one of the existing courses. An Introduction to Statistical Learning, with Applications in R -- James, Witten, Hastie, Modern Multivariate Statistical Techniques, 2nd Ed. ), Statistics: Machine Learning Track (B.S. Similar topics are covered in STA 131B and 131C. ), Statistics: Statistical Data Science Track (B.S. UC Davis Peter Hall Conference: Advances in Statistical Data Science. Emphasizes foundations. . Hypothesis testing and confidence intervals for one and two means and proportions. Please check the Undergraduate Admissions website for information about admissions requirements. Program in Statistics . Statistics 131A and Mathematics 135A cover the topics in the first part of the course but with more in depth and theoretical orientations. STA 131B Introduction to Mathematical Statistics. Course Description: Special study for advanced undergraduates. Lecture: 3 hours Emphasis on concepts, methods and data analysis using SAS. if you have any questions about the statistics major tracks. Please check the Undergraduate Admissions website for information about admissions requirements. Please be sure to check the minor declaration deadline with your College. Please follow the links below to find out more information about our major tracks. Emphasis on concepts, methods, and data analysis. Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. Some topics covered in STA 231A are covered, at a more elementary level, in the sequence STA 131A,B,C. Introduction to Probability, G.G. Course Description: Topics may include Bayesian analysis, nonparametric and semiparametric regression, sequential analysis, bootstrap, statistical methods in high dimensions, reliability, spatial processes, inference for stochastic process, stochastic methods in finance, empirical processes, change-point problems, asymptotics for parametric, nonparametric and semiparametric models, nonlinear time series, robustness. ), Statistics: Applied Statistics Track (B.S. 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. Illustrative reading: Course Description: Topics in asymptotic theory of statistics chosen from weak convergence, contiguity, empirical processes, Edgeworth expansion, and semiparametric inference. Prerequisite(s): Consent of instructor. Format: Concepts of correlation, regression, analysis of variance, nonparametrics. Basics of text mining. STA 131A Introduction to Probability Theory (4 units) Course Description: Fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, . University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Prerequisite(s): STA141A C- or better; (STA130A C- or better or STA131A C- or better or MAT135A C- or better); STA131A or MAT135A preferred. Clients are drawn from a pool of University clients. ,1; m"B=n /\zB1Unoj3;w4^+qQg0nS>EYOq,1q@d =_%r*tsP$gP|ar74[1GX!F V Y Discussion: 1 hour. UC Davis Department of Statistics University of California, Davis , One Shields Avenue, Davis, CA 95616 | 530-752-1011 Title: Mathematical Statistics I 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. 11 0 obj << UC Davis Department of Statistics. ), Statistics: Machine Learning Track (B.S. Computational data workflow and best practices. endstream Format: Regression and correlation, multiple regression. Prerequisite(s): ((STA222, STA223) or (BST222, BST223)); STA232B; or consent of instructor. ), Statistics: General Statistics Track (B.S. ), Statistics: Computational Statistics Track (B.S. Regression. 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. ), Statistics: Machine Learning Track (B.S. Admissions to UC Davis is managed by the Undergraduate Admissions Office. Prerequisite:STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better. Most UC Davis transfer students come from California community colleges. Restrictions: Prerequisite(s): STA015C C- or better or STA106 C- or better or STA108 C- or better. 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 . Winter. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Course Description: Work experience in statistics. >> endobj Program in Statistics - Biostatistics Track. 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. /ProcSet [ /PDF /Text ] Copyright The Regents of the University of California, Davis campus. a.Xv' 7j\>aVyS7w=S\cTWkb'(0-ge$W&x\'V4_9rirLrFgyLb0gPT%x bK.JG&0s3Mv[\TmiaC021hjXS_/`X2%9Sd1 Q6O L/KZX^kK`"HE5E?HWbGJn R-$Sr(8~* tKIVq{>|@GN]22HE2LtQ-r ku0 WuPtOD^Um\HMyDBwTb_ZgMFkQBax?`HfmC?t"= r;dAjkF@zuw\ .TqKx2XsHGSsoiTYM{?.9b_;j"LY,G >Fz}/cC'H]{V University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Two-sample procedures. 2 0 obj << Mathematical Sciences Building 1147. . STA 130A addresses itself to a different audience, and contains a brief introduction to probabilistic concepts at a less sophisticated level. Copyright The Regents of the University of California, Davis campus. My friends refer to 131B as the hardest class in the series. The course MAT 135A is an introduction to probability theory from purely MAT and more advanced viewpoint. Prerequisite(s): STA141B C- or better or (STA141A C- or better, (ECS 010 C- or better or ECS032A C- or better)). Prerequisite(s): STA200A; or consent of instructor. In addition to learning concepts and . Statistics: Applied Statistics Track (A.B. Double Major MS Admissions; Ph.D. Prerequisite(s): Consent of instructor; upper division standing. Course Description: Alternative approaches to regression, model selection, nonparametric methods amenable to linear model framework and their applications. ), Statistics: General Statistics 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. Regression and correlation, multiple regression. ), Prospective Transfer Students-Data Science, Ph.D. Course Description: Topics from balanced and partially balanced incomplete block designs, fractional factorials, and response surfaces. Course information: MAT 21D, Winter Quarter, 2021 Lectures: Online (asynchronous): lectures will be posted to Canvas on MWF before 5pm. ), Statistics: General Statistics Track (B.S. Apr 28-29, 2023. International Center, UC Davis. The new Data Science major at UC Davis has been published in the general catalog! ), Prospective Transfer Students-Data Science, Ph.D. Prerequisite(s): (MAT 125B, MAT135A) or STA131A; or consent of instructor. Prerequisite(s): STA235B or MAT235B; or consent of instructor. Course Description: Resampling, nonparametric and semiparametric methods, incomplete data analysis, diagnostics, multivariate and time series analysis, applied Bayesian methods, sequential analysis and quality control, categorical data analysis, spatial and image analysis, computational biology, functional data analysis, models for correlated data, learning theory. 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. Emphasis on concepts, method and data analysis. Prerequisite(s): STA106; STA108; STA131C; STA232B; MAT167. PLEASE NOTE: These are only guidelines to help prepare yourself to transition to UC Davis with sufficient progress made towards your major. STA 130A - Mathematical Statistics: Brief Course (MAT 16C or 17C or 21C); (STA 13 or 32 or 100) Fall, Winter . Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, factorial designs and analysis of covariance. ), Statistics: Statistical Data Science Track (B.S. ), Prospective Transfer Students-Data Science, Ph.D. STA 108 - Regression Analysis . Prerequisite(s): (EPI 202 or STA 130A or STA 131A or STA 133); EPI 205; a basic epidemiology course (EPI 205 or equivalent). All rights reserved. All rights reserved. Subject: STA 231A Catalog Description:Fundamental concepts and methods in statistical learning with emphasis on supervised learning. Prerequisite(s): STA131B; or the equivalent of STA131B. Mathematical Statistics and Data Analysis -- by J. RiceMathematical Statistics: A Text for Statisticians and Quantitative Scientists -- by F. J. Samaniego. Only 2 units of credit allowed to students who have taken course 131A. All rights reserved. Course Description: Introductory SAS language, data management, statistical applications, methods. Based on these offerings, a student can complete a Bachelor of Arts or a Bachalor of Science degree in Statistics. You can find course articulations for California community colleges using assist.org. Mathematical Sciences Building 1147. . Course Description: Introduction to consulting, in-class consulting as a group, statistical consulting with clients, and in-class discussion of consulting problems. UC Davis 2022-2023 General Catalog. Prerequisite(s): STA013 or STA013Y or STA032 or STA100 or STA103. Analysis of variance, F-test. Prepare SAS base programmer certification exam. Analysis of variance, F-test. Topics include basic concepts in asymptotic theory, decision theory, and an overview of methods of point estimation. Prerequisite(s): MAT021C C- or better; (MAT022A C- or better or MAT027A C- or better or MAT067 C- or better); MAT021D strongly recommended. ( University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Program in Statistics. 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. Course Description: Programming in R; Summarization and visualization of different data types; Concepts of correlation, regression, classification and clustering. Course Description: Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. ), Statistics: General Statistics Track (B.S. Questions or comments? Prerequisite(s): Consent of instructor; graduate standing. Prerequisite(s): STA206; knowledge of vectors and matrices. Program in Statistics - Biostatistics Track, Supervised methods versus unsupervised methods, Linear and quadratic discriminant analysis, Variable selection - AIC and BIC criteria. Course Description: Focus on linear and nonlinear statistical models. Advanced statistical procedures for analysis of data collected in clinical trials. All rights reserved. Prerequisite(s): Two years of high school algebra or Mathematics D. Course Description: Principles of descriptive statistics. Summary of course contents: . Prerequisite(s): An introductory upper division statistics course and some knowledge of vectors and matrices; STA100, or STA 102, or STA103 suggested or the equivalent. ), Statistics: General Statistics Track (B.S. Interactive data visualization with Web technologies. 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. Topics include algorithms; design; debugging and efficiency; object-oriented concepts; model specification and fitting; statistical visualization; data and text processing; databases; computer systems and platforms; comparison of scientific programming languages. J} \Ne8pAu~q"AqD2z LjEwD69(-NI3#W3wJ|XRM4l$.z?^YU.*$zIy0IZ5 /H]) G3[LO<=>S#%Ce8g'd/Q-jYY~b}}Dr_9-Me^MnZ(,{[1seh:/$( w*c\SE3kJ_47q(kQP3p FnMP.B\g4hpwsZ4 XMd1vyv@m_gt ,h+3gU *vGoJYO9 T z-7] x Prospective Transfer Students-Statistics, A.B. 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. Prerequisite(s): STA035B C- or better; (MAT016B C- or better or MAT017B C- or better or MAT021B C- or better). Statistical methods. General linear model, least squares estimates, Gauss-Markov theorem. 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. Course Description: Multivariate analysis: multivariate distributions, multivariate linear models, data analytic methods including principal component, factor, discriminant, canonical correlation and cluster analysis. The midterm and final examinations will differ from those of 131A in that they will include material covered in the additional reading assignments. Principles, methodologies and applications of parametric and nonparametric regression, classification, resampling and model selection techniques. viuw>M4$5`>1q|uw:m7XPvon?^ t Fhzr^r .p@K>1L&|wb5|MP$\y~0 BjX_5)u]" gXr%]`.|V>* Qr4 T *6812A|=&e#l%}XQJQoacIwf>u );7XvOxl tMJkRJkC)M)n)MW i6y&3) %5U:W;]UNGeY4_s\rAz\0$T_T=%UWm)GYemYt)2,s/Xo^lX#J5Nj^cX1JJBj8DP}}K(aRj!84,Mdmx0TPu^Cs$8unRweNF3L|Qeg'qvF!TdTfS67e]Cm.Y]{gA0 (C Hny[Ul?C?v8 Lecture: 3 hours Description. Course Description: Essentials of using relational databases and SQL. ), Statistics: Computational Statistics Track (B.S. Course Description: Special study for undergraduates. 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. Review computational tools for implementing optimization algorithms (gradient descent, stochastic gradient descent, coordinate descent, Newtons method.). Course Description: Special topics in Statistics appropriate for study at the graduate level. ), Statistics: Applied Statistics Track (B.S.
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