Dependence, conditioning, Bayes methods. Tools for reading, analyzing, and plotting data are covered, such as data input/output, reshaping data, the formula language, and graphics models. A deficient grade in DATAC100\STATC100\COMPSCIC100 may be removed by taking DATA 100. Illustrations from various fields. Terms offered: Fall 2022, Spring 2022, Fall 2021, Spring 2021, Spring 2020 Concepts in statistical programming and statistical computation, including programming principles, data and text manipulation, parallel processing, simulation, numerical linear algebra, and optimization. Linear Modelling: Theory and Applications: Terms offered: Spring 2020, Spring 2019, Spring 2018, Modern Statistical Prediction and Machine Learning. A treatment of ideas and techniques most commonly found in the applications of probability: Gaussian and Poisson processes, limit theorems, large deviation principles, information, Markov chains and Markov chain Monte Carlo, martingales, Brownian motion and diffusion. The Statistics of Causal Inference in the Social Science: Read Less [-], Terms offered: Spring 2016, Spring 2015 A deficient grade in STAT21 may be removed by taking STAT20, STATW21, or STAT N21. Research term project. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies. Statistical Genomics: Read More [+], Prerequisites: Statistics 200A and 200B or equivalent (may be taken concurrently). Topics include: probability, conditioning, and independence; random variables; distributions and joint distributions; expectation, variance, tail bounds; Central Limit Theorem; symmetries in random permutations; prior and posterior distributions; probabilistic models; bias-variance tradeoff; testing hypotheses; correlation and the regression model. Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Introduction to modern theory of statistics; empirical processes, influence functions, M-estimation, U and V statistics and associated stochastic decompositions; non-parametric function estimation and associated minimax theory; semiparametric models; Monte Carlo methods and bootstrap methods; distributionfree and equivariant procedures; topics in machine learning. Measure theory concepts needed for probability. Interval estimation. Statistics 140 or Electrical Engineering and Computer Science 126 are preferred. Topics include descriptive statistics, maximum likelihood estimation, non-parametric methods, introduction to optimality, goodness-of-fit tests, analysis of variance, bootstrap and computer-intensive methods and least squares estimation. Measure theory concepts needed for probability. Terms offered: Summer 2016 10 Week Session, Summer 2015 10 Week Session, Summer 2014 10 Week Session, Terms offered: Fall 2022, Summer 2022 8 Week Session, Spring 2022, Terms offered: Fall 2022, Summer 2022 8 Week Session, Spring 2022, Fall 2021, Summer 2021 8 Week Session, Fall 2020. of real-world datasets, including economic data, document collections, geographical data, and social networks. Probability Theory: Read More [+], Terms offered: Spring 2022, Spring 2021, Spring 2020 Fall and/or spring: 15 weeks - 1 hour of lecture and 1 hour of laboratory per week, Summer: 6 weeks - 2 hours of lecture and 3 hours of laboratory per week, Introduction to Programming in R: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? Summer: 8 weeks - 6 hours of lecture and 4 hours of laboratory per week, Formerly known as: Computer Science C8/Statistics C8/Information C8, Also listed as: COMPSCIC8/DATAC8/INFOC8, Foundations of Data Science: Read Less [-], Terms offered: Fall 2022, Summer 2022 8 Week Session, Spring 2022 Introduction to Statistics at an Advanced Level: Read More [+]. Student Learning Outcomes: Understand the difference between math and simulation, and appreciate the power of both Statistical Consulting: Read More [+], Prerequisites: Some course work in applied statistics and permission of instructor, Fall and/or spring: 15 weeks - 2 hours of session per week. As a member of the UC Berkeley community, I act with honesty, integrity, and respect for others.. Error estimation for complex samples. Fall and/or spring: 15 weeks - 3 hours of lecture per week, Summer: 8 weeks - 7.5 hours of lecture per week, Introductory Probability and Statistics for Business: Read Less [-], Terms offered: Summer 2021 8 Week Session, Summer 2020 8 Week Session, Summer 2019 8 Week Session Recent topics include: Graphical models and approximate inference algorithms. Bayesian methods and concepts: conditional probability, one-parameter and multiparameter models, prior distributions, hierarchical and multi-level models, predictive checking and sensitivity analysis, model selection, linear and generalized linear models, multiple testing and high-dimensional data, mixtures, non-parametric methods. A seminar on successful research designs and a forum for students to discuss the research methods needed in their own work, supplemented by lectures on relevant statistical and computational topics such as matching methods, instrumental variables, regression discontinuity, and Bayesian, maximum likelihood and robust estimation. Copyright 2022-23, UC Regents; all rights reserved. Longitudinal Data Analysis: Read More [+], Course Objectives: After successfully completing the course, you will be able to: Stochastic Analysis with Applications to Mathematical Finance: Read More [+], Prerequisites: 205A or consent of instructor, Stochastic Analysis with Applications to Mathematical Finance: Read Less [-], Prerequisites: Statistics 201B or Statistics 210A. Fall and/or spring: 15 weeks - 0.5-8 hours of independent study per week, Summer: 6 weeks - 1.5-20 hours of independent study per week8 weeks - 1-15 hours of independent study per week10 weeks - 1-12 hours of independent study per week, Subject/Course Level: Statistics/Graduate examination preparation, Individual Study for Master's Candidates: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Examples will be taken from the Black-Scholes-Merton theory of pricing and hedging contingent claims such as options, foreign market derivatives, and interest rate related contracts. The goal of this course is to better understand programming principles in general and to write better R code that capitalizes on the language's design. Algorithms in statistical computing: random number generation, generating other distributions, random sampling and permutations. Probability for Applications: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020, Fall 2019, Fall 2018 A deficient grade in STAT33A may be removed by taking STAT33B, or STAT133. Introduction to Probability and Statistics: Introductory Probability and Statistics for Business, Terms offered: Summer 2022 8 Week Session, Fall 2016, Fall 2015. This is part one of a year long series course. So Stat 140 will start faster than Stat 134 (due to the Data 8 prerequisite), avoid approximations that are unnecessary when SciPy is at hand, and replace some of the routine calculus by symbolic math done in SymPy. Credit Restrictions: Students will receive no credit for Statistics 201B after completing Statistics 200B. Make a secure online gift by choosing a giving opportunity. Particular topics vary with instructor. The course provides a broad theoretical framework for understanding the properties of commonly-used and more advanced methods. Introduction to Probability and Statistics at an Advanced Level: Read More [+], Prerequisites: Multivariable calculus and one semester of linear algebra. Special topics in probability and statistics offered according to student demand and faculty availability. Credit Restrictions: Students will receive no credit for STAT 88 after completing STAT134, STATC140, STAT135, or STAT 102. Introduction to Probability at an Advanced Level: Read More [+], Prerequisites: Undergraduate probability at the level of Statistics 134, multivariable calculus (at the level of Berkeleys Mathematics 53) and linear algebra (at the level of Berkeleys Mathematics 54). Terms offered: Fall 2022, Summer 2022 8 Week Session, Spring 2022 Advanced topics in probability offered according to students demand and faculty availability. Individual study in consultation with the graduate adviser, intended to provide an opportunity for qualified students to prepare themselves for the master's comprehensive examinations. Applied statistics with a focus on critical thinking, reasoning skills, and techniques. The research synthesizes the statistical, computational, economic, and social issues involved in solving complex real-world problems. Theory and practice of sampling from finite populations.
Students engage in professionally-oriented group research under the supervision of a research advisor. Introduction to Modern Biostatistical Theory and Practice: Biostatistical Methods: Survival Analysis and Causality, Terms offered: Fall 2022, Fall 2021, Fall 2020, Fall 2019. of causal parameters assuming marginal structural models. Selected topics in quantitative/statistical methods of research in the social sciences and particularly in sociology. Enrollment limited to 15 freshmen. Applications are drawn from a variety of fields including political science, economics, sociology, public health, and medicine. Discussion, problem review and development, guidance of laboratory classes, course development, supervised practice teaching. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Probability Theory: Read More [+], Terms offered: Fall 2020, Fall 2016, Fall 2015, Fall 2014 Introduction to Probability and Statistics: Read More [+], Prerequisites: Mathematics 1A, Mathematics 16A, Mathematics 10A/10B, or consent of instructor. Course covers major topics in general statistical theory, with a focus on statistical methods in epidemiology. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership. Individual Study for Master's Candidates: Read More [+]. The Statistics Colloquium is a forum for talks on the theory and applications of Statistics to be given to the faculty and graduate students of the Statistics Department and other interested parties. Ensemble methods. Reasoning and fallacies, descriptive statistics, probability models and related concepts, combinatorics, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression. Tools for reading, analyzing, and plotting data are covered, such as data input/output, reshaping data, the formula language, and graphics models. An introduction to the R statistical software for students with minimal prior experience with programming. Topics include data types and structures, such as vectors, data frames and lists; the REPL evaluation model; function calls, argument matching, and environments; writing simple functions and control flow. Designed for students who do not meet the prerequisites for 2. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, Thompson sampling, optimal control, Q-learning, differential privacy, clustering algorithms, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods. Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week, Summer: 8 weeks - 6 hours of lecture and 4 hours of discussion per week, Probability and Mathematical Statistics in Data Science: Read Less [-], Terms offered: Spring 2022, Spring 2021, Spring 2020 Emphasis is on estimation in nonparametric models in the context of contingency tables, regression (e.g., linear, logistic), density estimation and more. Corequisites: MATH54 or EECS16A. The course is designed as a sequence with with Statistics C205A/Mathematics C218A with the following combined syllabus. Brownian motion. Units may not be used to meet either unit or residence requirements for a master's degree. Credit Restrictions: Students will receive no credit for STAT20 after completing STATW21, STAT2, STAT 5, STAT21, STAT N21, STAT 2X, STAT S20, STAT 21X, or STAT 25. Introduction to Probability at an Advanced Level: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020 Topics covered may vary with instructor. Experience with R is assumed. An introduction to mathematical statistics, covering both frequentist and Bayesian aspects of modeling, inference, and decision-making. Sampling Surveys: Read More [+], Prerequisites: 101 or 134. Introduction to Statistical Computing: Read More [+]. A deficient grade in STAT20 may be removed by taking STATW21, STAT21, or STAT N21. This is part two of a year long series course. Randomization, blocking, factorial design, confounding, fractional replication, response surface methodology, optimal design. Stationary processes. Restricted to students who have been admitted to the one-year Masters Program in Statistics beginning fall 2012 or later, Fall and/or spring: 15 weeks - 3 hours of seminar and 1 hour of laboratory per week, Masters of Statistics Capstone Project: Read Less [-], Terms offered: Spring 2022, Spring 2021, Spring 2020 The selection of topics may vary from year to year. Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine II: Terms offered: Spring 2013, Fall 2012, Fall 2010, Fall 2009. intended for graduate students and advanced undergraduate students from the mathematical sciences. Credit Restrictions: Students will receive no credit for STAT201A after completing STAT200A. Individual and/or group meetings with faculty. derive consistent statistical inference in the presence of correlated, repeated measures data using likelihood-based mixed models and estimating equation approaches (generalized estimating equations; GEE), Understand in depth and make use of principles of numerical linear algebra, optimization, and simulation for statistics-related research. Subject/Course Level: Statistics/Graduate, Introduction to Probability and Statistics at an Advanced Level: Read Less [-], Terms offered: Spring 2019, Spring 2012, Spring 2011 Repeat rules: Course may be repeated for credit up to a total of 16 units. Directed Group Study: Read More [+], Fall and/or spring: 15 weeks - 2-3 hours of directed group study per week, Summer: 8 weeks - 4-6 hours of directed group study per week, Terms offered: Fall 2022, Summer 2022 8 Week Session, Spring 2022, Fall 2021, Summer 2021 8 Week Session, Fall 2020 Introduction to Statistical Computing: Read Less [-], Terms offered: Spring 2011, Spring 2010, Spring 2009 Stat 140 will capitalize on this, abstraction and computation complementing each other throughout. This will create time for a unit on the convergence and reversibility of Markov Chains as well as added focus on conditioning and Bayes methods.With about a thousand students a year taking Foundations of Data Science (Stat/CS/Info C8, a.k.a. Statistics 135 may be taken concurrently. Bayesian Statistics: Read More [+], Course Objectives: develop Bayesian models for new types of dataimplement Bayesian models and interpret the resultsread and discuss Bayesian methods in the literatureselect and build appropriate Bayesian models for data to answer research questionsunderstand and describe the Bayesian perspective and its advantages and disadvantages compared to classical methods, Prerequisites: Probability and mathematical statistics at the level of Stat 134 and Stat 135 or, ideally, Stat 201A and Stat 201B, Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of laboratory per week, Terms offered: Fall 2015, Fall 2014 ); and geometric approaches to eigendecompositions, least-squares, principal components analysis, etc. A deficient grade in STATC140 may be removed by taking STAT134. Societal Risks and the Law: Read More [+], Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week. Special Topics in Probability and Statistics: Read More [+], Fall and/or spring: 15 weeks - 1-3 hours of lecture and 0-2 hours of discussion per week, Special Topics in Probability and Statistics: Read Less [-], Terms offered: Fall 2015, Spring 2012
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