sta 131a uc davis

Course Description: Transformed random variables, large sample properties of estimates. Copyright The Regents of the University of California, Davis campus. Computational data workflow and best practices. ), Statistics: Applied Statistics Track (B.S. 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. UC Davis Department of Statistics. Effective Term: 2008 Summer Session I. PDF STATISTICS 131A | Probability Theory - UC Davis Course Description: Practical experience in methods/problems of teaching statistics at university undergraduate level. 2 0 obj << Prerequisite(s): STA141A C- or better; (STA130A C- or better or STA131A C- or better or MAT135A C- or better); STA131A or MAT135A preferred. May be taught abroad. Course Description: Statistics and probability in daily life. :Z Statistical Methods. Copyright The Regents of the University of California, Davis campus. Grade Mode: Letter. Thu, May 11, 2023 @ 4:10pm - 5:30pm. . 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: STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better. & B.S. 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. STATISTICS 131A | Probability Theory Textbook: Ross, S. (2010). ), Statistics: General Statistics Track (B.S. ECS 117. Lecture: 3 hours Prerequisite(s): (EPI 202 or STA 130A or STA 131A or STA 133); EPI 205; a basic epidemiology course (EPI 205 or equivalent). Prerequisite(s): STA106 C- or better; STA108 C- or better; (STA130B C- or better or STA131B C- or better); STA141A C- or better. ), Statistics: Computational Statistics Track (B.S. 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. Format: Prospective Transfer Students-Statistics, A.B. Course Description: Focus on linear and nonlinear statistical models. 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. UC Davis Department of Statistics - Minor Program Why Choose UC Davis? Both courses cover the fundamentals of the various methods and techniques, their implementation and applications. Course Description: Descriptive statistics; basic probability concepts; binomial, normal, Student's t, and chi-square distributions. ), Statistics: Machine Learning Track (B.S. Some of the broad topics, such as classification and regression overlap with STA 135. Course Description: Essentials of using relational databases and SQL. ), Statistics: Machine Learning Track (B.S. ), Statistics: Applied Statistics Track (B.S.

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