Statistics 131A and Mathematics 135A cover the topics in the first part of the course but with more in depth and theoretical orientations. PLEASE NOTE: These are only guidelines to help prepare yourself to transition to UC Davis with sufficient progress made towards your major. ), Statistics: Machine Learning Track (B.S. Clients are drawn from a pool of University clients. Discussion: 1 hour. ), Statistics: General Statistics Track (B.S. 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. Review computational tools for implementing optimization algorithms (gradient descent, stochastic gradient descent, coordinate descent, Newtons method.). 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. ), Statistics: Applied Statistics Track (B.S. Copyright The Regents of the University of California, Davis campus. Please follow the links below to find out more information about our major tracks. Practical applications of widely-used designs, including dose-finding, comparative and cluster randomization designs. 1 0 obj << Summary of course contents: . The course MAT 135A is an introduction to probability theory from purely MAT and more advanced viewpoint. ), Statistics: Statistical Data Science Track (B.S. Course Description: Estimation and testing for the general linear model, regression, analysis of designed experiments, and missing data techniques. In addition to learning concepts and . ECS 111 or MAT 170 or STA 142A. Basic ideas of hypotheses testing, likelihood ratio tests, goodness-of- fit tests. Statistical methods. Two-sample procedures. Course Description: Introductory SAS language, data management, statistical applications, methods. Computational data workflow and best practices. Catalog Description:Transformed random variables, large sample properties of estimates. *Choose one of MAT 108 or 127C. Discussion: 1 hour. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. 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. Format: Regression and correlation, multiple regression. STA 290 Seminar: Sam Pimentel. UC Davis Department of Statistics - Minor Program Roussas, Academic Press, 2007. ), Prospective Transfer Students-Data Science, Ph.D. /Filter /FlateDecode Prerequisite(s): STA206; STA207; STA135; or their equivalents. Mathematical Sciences Building 1147. . Copyright The Regents of the University of California, Davis campus. Admissions decisions are not handled by the Department of Statistics. Restrictions:Not open for credit to students who have completed Mathematics 135A. 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 STA 100 C- or better). This course is a continuations of STA 130A. 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. 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. STA 131A - Introduction to Probability Theory Please follow the links below to find out more information about our major tracks. Some topics covered in STA 231A are covered, at a more elementary level, in the sequence STA 131A,B,C. 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. You can find course articulations for California community colleges using assist.org. ), Statistics: Machine Learning Track (B.S. ~.S|d&O`S4/ COkahcoc B>8rp*OS9rb[!:D >N1*iyuS9QG(r:| 2#V`O~/ 4ClJW@+d ), Statistics: Computational 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. Course Description: Basics of experimental design. Intensive use of computer analyses and real data sets. Prerequisite(s): MAT016A (can be concurrent) or MAT017A (can be concurrent) or MAT021A (can be concurrent). All rights reserved. UC Davis Department of Statistics University of California, Davis , One Shields Avenue, Davis, CA 95616 | 530-752-1011 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) Course Description: Sign and Wilcoxon tests, Walsh averages. Course Description: Advanced programming and data manipulation in R. Principles of data visualization. Statistics: Applied Statistics Track (A.B. Principles, methodologies and applications of clustering methods, dimension reduction and manifold learning techniques, graphical models and latent variables modeling. /Length 2087 /Parent 8 0 R Prerequisite: STA 108 C- or better or STA 106 C- or better. Course Description: Directed reading, research and writing, culminating in the completion of a senior honors thesis or project under direction of a faculty advisor. ), Statistics: Applied Statistics Track (B.S. ,1; m"B=n /\zB1Unoj3;w4^+qQg0nS>EYOq,1q@d =_%r*tsP$gP|ar74[1GX!F V Y Prerequisite(s): STA106; STA108; STA131C; STA232B; MAT167. Processing data in blocks. Copyright The Regents of the University of California, Davis campus. ), Statistics: Computational Statistics Track (B.S. Course Description: Second part of a three-quarter sequence on mathematical statistics. The students will also learn about the core mathematical constructs and optimization techniques behind the methods. 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. Based on these offerings, a student can complete a Bachelor of Arts or a Bachalor of Science degree in Statistics. Regression. Weak convergence in metric spaces, Brownian motion, invariance principle. ), Statistics: Statistical Data Science Track (B.S. Course Description: Descriptive statistics; basic probability concepts; binomial, normal, Student's t, and chi-square distributions. Prerequisite(s): STA235A or MAT235A; or consent of instructor. Course Description: Descriptive statistics, probability, sampling distributions, estimation, hypothesis testing, contingency tables, ANOVA, regression; implementation of statistical methods using computer package. STA 142A Statistical Learning I - UC Davis Department of Statistics Apr 28-29, 2023. International Center, UC Davis. Prerequisite(s): MAT016B C- or better or MAT017B C- or better or MAT021B C- or better. 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. 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: Transformed random variables, large sample properties of estimates. Format: Units: 4. Scraping Web pages and using Web services/APIs. Goals:Students learn how to use a variety of supervised statistical learning methods, and gain an understanding of their relative advantages and limitations. 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 UC Davis Department of Statistics University of California, Davis , One Shields Avenue, Davis, CA 95616 | 530-752-1011 11 0 obj << ), Statistics: Applied Statistics Track (B.S. Only 2 units of credit allowed to students who have taken course 131A . Emphasizes foundations. Illustrative reading: ), Prospective Transfer Students-Data Science, Ph.D. All rights reserved. STA 130B - Mathematical Statistics: Brief Course STA 130A or 131A or MAT 135A : Winter, Spring . STA 131A is an introductory course for probability. First part of three-quarter sequence on mathematical statistics. PDF STATISTICS 131A | Probability Theory - UC Davis Prerequisite(s): Introductory statistics course; some knowledge of vectors and matrices. Course Description: Essentials of statistical computing using a general-purpose statistical language. Instructor O ce hours: 12.00{2.00 pm Friday TA O ce hours: 12{1 pm Tuesday, 1{2 pm Thursday, 1117 MSB 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. Pass One restricted to Statistics majors. Lecture: 3 hours 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 . :Z Prerequisite(s): STA231C; STA235A, STA235B, STA235C recommended. Spring STA 141A. Copyright The Regents of the University of California, Davis campus. Course Description: Research in Statistics under the supervision of major professor. General linear model, least squares estimates, Gauss-Markov theorem. /ProcSet [ /PDF /Text ] Packaged computer programs, analysis of real data. Prerequisite(s): STA015A C- or better or STA013 C- or better or STA032 C- or better or STA100 C- or better. ), Statistics: General Statistics Track (B.S. Course Description: Focus on linear statistical models widely used in scientific research. Prospective Transfer Students-Statistics, A.B. Conditional expectation. Graduate standing. ), Statistics: Machine Learning Track (B.S. Univariate and multivariate spectral analysis, regression, ARIMA models, state-space models, Kalman filtering. 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. 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. Course Description: High-performance computing in high-level data analysis languages; different computational approaches and paradigms for efficient analysis of big data; interfaces to compiled languages; R and Python programming languages; high-level parallel computing; MapReduce; parallel algorithms and reasoning. Please utilize their website for information about admissions requirements and transferring. STA 130A addresses itself to a different audience, and contains a brief introduction to probabilistic concepts at a less sophisticated level. STA 290 Seminar: Aidan Miliff Event Date. Program in Statistics - Biostatistics Track, Supervised methods versus unsupervised methods, Linear and quadratic discriminant analysis, Variable selection - AIC and BIC criteria. Discussion: 1 hour. How hard is the STA 131 and STA 141 series? : UCDavis - Reddit PDF STATISTICS COURSE PREREQUISITES & TENTATIVE SCHEDULE - UC Davis ), Statistics: Computational Statistics Track (B.S. 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:/$( w*c\SE3kJ_47q(kQP3p FnMP.B\g4hpwsZ4 XMd1vyv@m_gt ,h+3gU *vGoJYO9 T z-7] x Topics include statistical functionals, smoothing methods and optimization techniques relevant for statistics. a.Xv' 7j\>aVyS7w=S\cTWkb'(0-ge$W&x\'V4_9rirLrFgyLb0gPT%x bK.JG&0s3Mv[\TmiaC021hjXS_/`X2%9Sd1 Q6O L/KZX^kK`"HE5E?HWbGJn R-$Sr(8~* tKIVq{>|@GN]22HE2LtQ-r ku0 WuPtOD^Um\HMyDBwTb_ZgMFkQBax?`HfmC?t"= r;dAjkF@zuw\ .TqKx2XsHGSsoiTYM{?.9b_;j"LY,G >Fz}/cC'H]{V Principles, methodologies and applications of parametric and nonparametric regression, classification, resampling and model selection techniques. 3 0 obj << Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. xX[o[~}&15]`'RB6V m3j.|C%`!O_"-Qp.bY}p+cg Kviwv{?Y`o=Oif@#0B=jJ__2n_@z[hw\/:I,UG6{swMQYq:KkVn ES|RJ+HVluV/$fwN_nw2ZMK$46Rx zl""lUn#) Mathematical Statistics and Data Analysis -- by J. RiceMathematical Statistics: A Text for Statisticians and Quantitative Scientists -- by F. J. Samaniego. Course Description: Probability concepts; programming in R; exploratory data analysis; sampling distribution; estimation and inference; linear regression; simulations; resampling methods. Double Major MS Admissions; Ph.D. Prerequisite(s): (STA130B or STA131B) or (STA106, STA108). General Catalog - Statistics (STA) - UC Davis Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. Prerequisite(s): STA131A; STA232A recommended, not required. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Some topics covered in STA 231B are covered, at a more elementary level, in the sequence STA 131A,B,C. ), Prospective Transfer Students-Data Science, Ph.D. Multidimensional tables and log-linear models, maximum likelihood estimation; tests of goodness-of-fit. 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. There is no significant overlap with any one of the existing courses. Only 2 units of credit allowed to students who have taken course 131A. UC Davis Department of Statistics - STA 130B Mathematical Statistics May be taught abroad. The computational component has some overlap with STA 141B, where the emphasis is more on data visualization and data preprocessing. /Resources 1 0 R ), Statistics: Computational Statistics Track (B.S. Prerequisite(s): STA106; STA108; STA131A; STA131B; STA131C; MAT167. Course Description: Classical and Bayesian inference procedures in parametric statistical models. One-way and two-way fixed effects analysis of variance models. The minor is designed to provide students in other disciplines with opportunities for exposure and skill development in advanced . Potential Overlap:Similar topics are covered in STA 131B and 131C. UC Davis Department of Statistics. Course Description: Principles of supervised and unsupervised statistical learning. Admissions to UC Davis is managed by the Undergraduate Admissions Office. STATISTICS 131A | Probability Theory Textbook: Ross, S. (2010). Course Description: Focus on linear statistical models. Concepts of randomness, probability models, sampling variability, hypothesis tests and confidence interval. STA 231A: Mathematical Statistics I - UC Davis 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. 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. O?"cNlCs*/{GE>! The deadline to file your minor petition may vary by College. 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. PLEASE NOTE: These are only guidelines to help prepare yourself to transition to UC Davis with sufficient progress made towards your major. if you have any questions about the statistics major tracks. All rights reserved. Course Description: Practical experience in methods/problems of teaching statistics at university undergraduate level. 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: Statistical Methods. Prerequisite(s): STA130A; STA130B; or equivalent of STA130A and STA130B. Course Description: Directed group study. 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. /Contents 3 0 R Prerequisite(s): STA207 or STA232B; working knowledge of advanced statistical software and the equivalent of STA207 or STA232B. Prerequisite(s): Introductory, upper division statistics course; some knowledge of vectors and matrices; STA106 or STA108 or the equivalent suggested. ), Statistics: Machine Learning Track (B.S. STA 131A C- or better or MAT 135A C- or better; consent of instructor. 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. Probability 4 STA 131A - Introduction to Probability Theory 4 Statistics 12 STA 108 - Applied Stat Methods . School: College of Letters and Science LS Summary of Course Content: Prerequisite(s): Senior qualifying for honors. ), Statistics: Applied Statistics Track (B.S. Course Description: Multivariate analysis: multivariate distributions, multivariate linear models, data analytic methods including principal component, factor, discriminant, canonical correlation and cluster analysis. Course Description: Focus on linear and nonlinear statistical models. Program in Statistics - Biostatistics Track. ), Statistics: Applied Statistics Track (B.S. Copyright The Regents of the University of California, Davis campus. Prerequisite: MAT 021C C- or better; (MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better); MAT 021D . 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: Varieties of categorical data, cross-classifications, contingency tables, tests for independence. Prerequisite(s): STA108 C- or better or STA106 C- or better. Course Description: Programming in R; Summarization and visualization of different data types; Concepts of correlation, regression, classification and clustering. STA 131A; STA 131B; STA 131C; MAT 025; MAT 125A; Or equivalent of MAT 025 and MAT 125A. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Measures of association. In contrast, STA 142A focuses more on issues of statistical principles and algorithms inherent in the formulation of the methods, their advantages and limitations, and their actual performance, as evidenced by numerical simulations and data analysis. Some topics covered in STA 231A are covered, at a more elementary level, in the sequence STA 131A,B,C. PDF STA 131A: Introduction to Probability - UC Davis *Choose one of MAT 108 or 127C. Regression and correlation, multiple regression. Prerequisite(s): STA235B or MAT235B; or consent of instructor. Admissions decisions are not handled by the Department of Statistics. Principles, methodologies and applications of parametric and nonparametric regression, classification, resampling and model selection techniques. Course Description: Work experience in statistics. 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. 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. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, factorial designs and analysis of covariance. Statistics: Applied Statistics Track (A.B. Prerequisite(s): STA130B C- or better or STA131B C- or better. Prerequisite(s): Consent of instructor; graduate standing. 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. If you elect more than one minor, these minors may not have any courses in common. (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). UC Davis Department of Statistics - STA 130A Mathematical Statistics

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