Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. Prerequisite(s): STA235A or MAT235A; or consent of instructor. These methods are useful for conducting research in applied subjects, and they are appealing to employees and graduate schools seeking students with quantitative skills. /Contents 3 0 R Course Description: Introductory SAS language, data management, statistical applications, methods. 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 . Packaged computer programs, analysis of real data. ), Statistics: Machine Learning Track (B.S. -- A. J. Izenman. Logit models, linear logistic models. Please be sure to check the minor declaration deadline with your College. Emphasis on concepts, method and data analysis. 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. 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, . Prerequisite(s): MAT016B C- or better or MAT017B C- or better or MAT021B C- or better. Some topics covered in STA 231A are covered, at a more elementary level, in the sequence STA 131A,B,C. 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. %PDF-1.5 Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, factorial designs and analysis of covariance. ), Statistics: Applied Statistics Track (B.S. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, variable transformation, factorial designs and ANCOVA. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. An Introduction to Statistical Learning, with Applications in R -- James, Witten, Hastie, Modern Multivariate Statistical Techniques, 2nd Ed. All rights reserved. Regression and correlation, multiple regression. Course Description: Essentials of using relational databases and SQL. Scraping Web pages and using Web services/APIs. Course Description: Principles of descriptive statistics; basic R programming; probability models; sampling variability; hypothesis tests; confidence intervals; statistical simulation. 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. Prerequisite(s): STA200B; or consent of instructor. At most, one course used in satisfaction of your minor may be applied to your major. ), Statistics: General Statistics Track (B.S. STA 231A: Mathematical Statistics I - UC Davis Topics include statistical functionals, smoothing methods and optimization techniques relevant for statistics. Topics include resampling methods, regularization techniques in regression and modern classification, cluster analysis and dimension reduction techniques. Prerequisite(s): STA130A C- or better or STA131A C- or better or MAT135A C- or better. Prerequisite(s): STA131A C- or better or MAT135A C- or better; consent of instructor. Apr 28-29, 2023. International Center, UC Davis. Both courses cover the fundamentals of the various methods and techniques, their implementation and applications. UC Davis Department of Statistics - Information for Prospective Course Description: Advanced programming and data manipulation in R. Principles of data visualization. STA 231B: Mathematical Statistics II | UC Davis Department of Statistics STA 13 or 32 or 100 : Fall, Winter, Spring . ), Statistics: Machine Learning Track (B.S. In addition to learning concepts and heuristics for selecting appropriate methods, the students will also gain programming skills in order to implement such methods. STA 108 ECS 17. Emphasis on practical consulting and collaboration of statisticians with clients and scientists under instructor supervision. 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Course Description: Principles and practice of interdisciplinary collaboration in statistics, statistical consulting, ethical aspects, and basics of data analysis and study design. STA 131A is an introductory course for probability. Prerequisite(s): STA200A; or consent of instructor. Basics of text mining. Admissions to UC Davis is managed by the Undergraduate Admissions Office. Only 2 units of credit allowed to students who have taken course 131A. Copyright The Regents of the University of California, Davis campus. Statistics: Applied Statistics Track (A.B. Course Description: Principles of supervised and unsupervised statistical learning. Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). STA 290 Seminar: Aidan Miliff Event Date. You are encouraged to contact the Statistics Department's Undergraduate Program Coordinator at. PDF STATISTICS COURSE PREREQUISITES & TENTATIVE SCHEDULE - UC Davis Prerequisite(s): STA131C; or consent of instructor; data analysis experience recommended. endstream Description. Transformed random variables, large sample properties of estimates. 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. All rights reserved. Goals: Students learn how to use a variety of supervised statistical learning methods, and gain an understanding of their relative advantages and limitations. 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. ), Statistics: General Statistics Track (B.S. >> 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. 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. 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). STA 290 Seminar: Sam Pimentel. ), Statistics: Machine Learning 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. Course Description: Topics from balanced and partially balanced incomplete block designs, fractional factorials, and response surfaces. B.S. in Data Science: Foundations Track - UC Davis Department of Statistics Course Description: Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. /ProcSet [ /PDF /Text ] Course Description: Research in Statistics under the supervision of major professor. Course Description: Standard and advanced statistical methodology, theory, algorithms, and applications relevant to the analysis of -omics data. Prerequisite:STA 131A C- or better or MAT 135A C- or better; consent of instructor. 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 . STA 131A; STA 131B; STA 131C; MAT 025; MAT 125A; Or equivalent of MAT 025 and MAT 125A. ), Statistics: Applied Statistics Track (B.S. The statistics undergraduate program at UC Davis offers a large and varied collection of courses in statistical theory, methodology, and application. All rights reserved. ~.S|d&O`S4/ COkahcoc B>8rp*OS9rb[!:D >N1*iyuS9QG(r:| 2#V`O~/ 4ClJW@+d Copyright The Regents of the University of California, Davis campus. ), Statistics: General Statistics Track (B.S. The course MAT 135A is an introduction to probability theory from purely MAT and more advanced viewpoint. 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. 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. UC Davis Course STA 13 or STA 35A; If the courses above are completed pre-matriculation, your major course schedule at UC Davis will be similar to the one below. xko{~{@ DR&{P4h`'Rw3J^809+By:q2("BY%Eam}v{Y5~~x{{Qy%qp3rT"x&vW6Y Course Description: Fundamental concepts and methods in statistical learning with emphasis on supervised learning. Emphasizes: hyposthesis testing (including multiple testing) as well as theory for linear models. ), Statistics: Computational 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 re-sampling methodology. UC Davis Department of Statistics University of California, Davis , One Shields Avenue, Davis, CA 95616 | 530-752-1011 Advanced statistical procedures for analysis of data collected in clinical trials. General Catalog - Statistics (STA) - UC Davis Course Description: Directed group study. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. UC Davis Department of Statistics - STA 130B Mathematical Statistics Discussion: 1 hour. Mathematical Statistics and Data Analysis -- by J. RiceMathematical Statistics: A Text for Statisticians and Quantitative Scientists -- by F. J. Samaniego. Prerequisite(s): STA106 C- or better; STA108 C- or better; (STA130B C- or better or STA131B C- or better); STA141A C- or better. General linear model, least squares estimates, Gauss-Markov theorem. Copyright The Regents of the University of California, Davis campus. 1 0 obj << Computational reasoning, computationally intensive statistical methods, reading tabular & non-standard data. Because of the large class size, lectures will be pre-recorded and posted online. 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. ), Prospective Transfer Students-Data Science, Ph.D. UC Davis Peter Hall Conference: Advances in Statistical Data Science. Discussion: 1 hour. PLEASE NOTE: These are only guidelines to help prepare yourself to transition to UC Davis with sufficient progress made towards your major. 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. ), Statistics: Applied Statistics Track (B.S. My friends refer to 131B as the hardest class in the series. UC Davis 2022-2023 General Catalog. The Bachelor of Science has fiveemphases call tracks. Goals: This course is a continuations of STA 130A. Prerequisite(s): STA130B C- or better or STA131B C- or better. 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. Format: ECS 111 or MAT 170 or STA 142A. 3rd Year: Program in Statistics . Polonik does his best to make difficult material understandable, and is a compotent and caring lecturer. Xiaodong Li. Prerequisite(s): (MAT 125B, MAT135A) or STA131A; or consent of instructor. STA 290 Seminar: Sam Pimentel Event Date. Regression. UC Davis Department of Statistics - STA 141A Fundamentals of 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)). 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. Nonparametric methods; resampling techniques; missing data. 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. ), Prospective Transfer Students-Data Science, Ph.D. The minor is designed to provide students in other disciplines with opportunities for exposure and skill development in advanced statistical methods. Prerequisite(s): STA131B; STA237A; or the equivalent of STA131B. The Bachelor of Science has fiveemphases call tracks. Principles, methodologies and applications of parametric and nonparametric regression, classification, resampling and model selection techniques. 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. General Catalog - Epidemiology (EPI) - UC Davis Some topics covered in STA 231A are covered, at a more elementary level, in the sequence STA 131A,B,C. Topics include basic concepts in asymptotic theory, decision theory, and an overview of methods of point estimation. 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. Course Description: Programming in R; Summarization and visualization of different data types; Concepts of correlation, regression, classification and clustering. Probability and Statistics by Mark J. Schervish, Morris H. DeGroot 4th Edition 2014, Pearson, University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. School: College of Letters and Science LS Description. Please follow the links below to find out more information about our major tracks. You must have a grade point average of 2.0 in all courses required for the minor. . 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. /Parent 8 0 R ECS 232: Theory of Molecular Computation | Computer Science Randomized complete and incomplete block design. Review computational tools for implementing optimization algorithms (gradient descent, stochastic gradient descent, coordinate descent, Newtons method.). Grade Mode: Letter. /Filter /FlateDecode Emphasizes foundations. Prerequisite: STA 108 C- or better or STA 106 C- or better. Format: Instructor O ce hours: 12.00{2.00 pm Friday TA O ce hours: 12{1 pm Tuesday, 1{2 pm Thursday, 1117 MSB Course Description: Focus on linear statistical models. Lecture: 3 hours University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Format: Lecture: 3 hours. Copyright The Regents of the University of California, Davis campus. Course Description: Seminar on advanced topics in probability and statistics. ), Statistics: Applied Statistics Track (B.S. General linear model, least squares estimates, Gauss-Markov theorem. Includes basics, graphics, summary statistics, data sets, variables and functions, linear models, repetitive code, simple macros, GLIM and GAM, formatting output, correspondence analysis, bootstrap. Course Description: Time series relationships; univariate time series models: trend, seasonality, correlated errors; regression with correlated errors; autoregressive models; autoregressive moving average models; spectral analysis: cyclical behavior and periodicity, measures of periodicity, periodogram; linear filtering; prediction of time series; transfer function models. STA 141A Fundamentals of Statistical Data Science. Prerequisite(s): STA207 or STA232B; working knowledge of advanced statistical software and the equivalent of STA207 or STA232B. Course Description: Practical experience in methods/problems of teaching statistics at university undergraduate level. Prerequisite(s): (STA130A, STA130B); (MAT067 or MAT167); or equivalent of STA130A and 130B, or equivalent of MAT167 or MAT067. Two-sample procedures. Prerequisite(s): STA141B C- or better or (STA141A C- or better, (ECS 010 C- or better or ECS032A C- or better)). ), Statistics: Machine Learning Track (B.S. Hypothesis testing and confidence intervals for one and two means and proportions. Prerequisite(s): (STA130B or STA131B) or (STA106, STA108). Univariate and multivariate spectral analysis, regression, ARIMA models, state-space models, Kalman filtering. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Course Description: Special study for undergraduates. Catalog Description:Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. Format: Copyright The Regents of the University of California, Davis campus. Principles, methodologies and applications of parametric and nonparametric regression, classification, resampling and model selection techniques. /Filter /FlateDecode & B.S. Use of professional level software.
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