The Design and Analysis of Experiments: Read More [+], Prerequisites: Statistics 134 and 135 or consent of instructor. 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), 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 Genomics is one of the fundamental areas of research in the biological sciences and is rapidly becoming one of the most important application areas in statistics. Statistics 133, 134, and 135 recommended, Statistical Models: Theory and Application: Read Less [-], Terms offered: Spring 2022, Spring 2021, Spring 2020 Introduction to Probability and Statistics at an Advanced Level: Read More [+]. Bounds and approximations. 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. Computing techniques, numerical methods, simulation and general implementation of biostatistical analysis techniques with emphasis on data applications. Brownian motion. Seminar on Topics in Probability and Statistics: Terms offered: Spring 2021, Spring 2020, Spring 2019, Reproducible and Collaborative Statistical Data Science, Terms offered: Spring 2022, Spring 2021, Fall 2018. Characteristic function methods. Share an intellectual experience with faculty and students by reading "Interior Chinatown" over the summer, attending author Charles Yu's live event on August 26, signing up for L&S 10: The On the Same Page Course, and participating in fall program activities. Final exam required. Expection, distributions. This course is a mix of statistical theory and data analysis. Further topics such as: continuous time Markov chains, queueing theory, point processes, branching processes, renewal theory, stationary processes, Gaussian processes. 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. Credit Restrictions: Students will receive no credit for Statistics 200A-200B after completing Statistics 201A-201B. Principles & Techniques of Data Science: Read More [+], Prerequisites: COMPSCIC8 / DATAC8 / INFOC8 / STATC8; and COMPSCI61A, COMPSCI 88, or ENGIN7; Corequisite: MATH54 or EECS16A. Efficiency comparison with the classical procedures. An introduction to computationally intensive applied statistics. Fall and/or spring: 15 weeks - 1 hour of seminar per week. Course covers major topics in general statistical theory, with a focus on statistical methods in epidemiology. The Statistics of Causal Inference in the Social Science: Quantitative Methodology in the Social Sciences Seminar. Regression. Upon completion, the final grade will be applied to both parts of the series. Topics in Theoretical Statistics: Read More [+], Formerly known as: 216A-216B and 217A-217B, Topics in Theoretical Statistics: Read Less [-], Terms offered: Spring 2016 Corequisite or Prerequisite: Foundations of Data Science (COMPSCIC8 / DATAC8 / INFOC8 / STATC8). Course covers statistical issues surrounding estimation of effects using data on units followed through time. Topics covered may vary with instructor. Use of numerical computation, graphics, simulation, and computer algebra. Recent topics include information theory, multivariate analysis and random matrix theory, high-dimensional inference. A deficient grade in STAT20 may be removed by taking STATW21, STAT21, or STAT N21. BerkeleyX offers interactive online classes and MOOCs from the worlds best universities. Fall and/or spring: 15 weeks - 2-4 hours of seminar per week, Freshman/Sophomore Seminar: Read Less [-], Terms offered: Spring 2013 Quantitative Methodology in the Social Sciences Seminar: Terms offered: Fall 2018, Fall 2017, Fall 2016, Terms offered: Spring 2021, Fall 2017, Fall 2016, Terms offered: Fall 2021, Fall 2020, Fall 2019, Advanced Topics in Learning and Decision Making, Terms offered: Spring 2022, Spring 2017, Spring 2016. Grading/Final exam status: Letter grade. Experience with R is assumed. Final exam not required. 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. Individual and/or group meetings with faculty. Program effectively in languages including R and Python with an advanced knowledge of language functionality and an understanding of general programming concepts. For students with mathematical background who wish to acquire basic concepts. Introduction to Advanced Programming in R. , and object systems. Applications are drawn from a variety of fields including political science, economics, sociology, public health and medicine. Data, Inference, and Decisions: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 The course and lab include hands-on experience in analyzing real world data from the social, life, and physical sciences. Nonparametric and Robust Methods: Read More [+], Prerequisites: A year of upper division probability and statistics, Nonparametric and Robust Methods: Read Less [-], Terms offered: Fall 2021, Fall 2020, Fall 2019 Probability Theory: Read More [+], Terms offered: Spring 2022, Spring 2021, Spring 2020 Credit Restrictions: Students will receive no credit for DATAC8\COMPSCIC8\INFOC8\STATC8 after completing COMPSCI 8, or DATA 8. Special tutorial or seminar on selected topics. Ensemble methods. The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. Societal Risks and the Law: Read More [+], Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week. Simple random, stratified, cluster, and double sampling. Topics will include organization and use of databases, visualization and graphics, statistical learning and data mining, model validation procedures, and the presentation of results. Repeat rules: Course may be repeated for credit up to a total of 16 units. Individual study Principles and Techniques of Data Science: Read More [+], Prerequisites: COMPSCIC8 / INFOC8 / STATC8 or ENGIN7; and either COMPSCI61A or COMPSCI 88. Seminar on Topics in Probability and Statistics, Terms offered: Spring 2022, Fall 2020, Spring 2020. Analysis of survival time data using parametric and non-parametric models, hypothesis testing, and methods for analyzing censored (partially observed) data with covariates. Topics include statistical decision theory; point estimation; minimax and admissibility; Bayesian methods; exponential families; hypothesis testing; confidence intervals; small and large sample theory; and M-estimation. A deficient grade in STAT33A may be removed by taking STAT33B, or STAT133. Random walks, discrete time Markov chains, Poisson processes. When you print this page, you are actually printing everything within the tabs on the page you are on: this may include all the Related Courses and Faculty, in addition to the Requirements or Overview. ); and geometric approaches to eigendecompositions, least-squares, principal components analysis, etc. This course and Pb Hlth C240C/Stat C245C provide an introduction to computational statistics with emphasis on statistical methods and software for addressing high-dimensional inference problems that arise in current biological and medical research. Primary focus is from the analysis side. Descriptive statistics, probability models and related concepts, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression. Markov decision processes and partially observable Markov decision processes. It will also use causal diagrams at an intuitive level. Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 1 hour of laboratory per week, Summer: 8 weeks - 6 hours of lecture, 2 hours of discussion, and 2 hours of laboratory per week, Formerly known as: Statistics C100/Computer Science C100, Principles & Techniques of Data Science: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Robust alternatives to least squares. Grading/Final exam status: Letter grade. Biostatistical Methods: Survival Analysis and Causality: Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine. An introduction to linear algebra for data science. Individual Study for Doctoral Candidates: Read More [+], Prerequisites: One year of full-time graduate study and permission of the graduate adviser. Linear Algebra for Data Science: Read More [+], Prerequisites: One year of calculus. Freshman Seminars: Read More [+]. Special Topics in Probability and Statistics: Terms offered: Spring 2022, Fall 2021, Spring 2021. for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing. Probability and Mathematical Statistics in Data Science: Read More [+], Prerequisites: Prerequisite: one semester of calculus at the level of Math 16A, Math 10A, or Math 1A. Covers observational studies with and without ignorable treatment assignment, randomized experiments with and without noncompliance, instrumental variables, regression discontinuity, sensitivity analysis and randomization inference. Conditional expectations, martingales and martingale convergence theorems. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing. A deficient grade in STAT33B may be removed by taking STAT133. This is part one of a year long series course. Students will be exposed to statistical questions that are relevant to decision and policy making. Topics in Theoretical Statistics: Read More [+], Terms offered: Fall 2022, Fall 2021, Fall 2020 Advanced Topics in Probability and Stochastic Processes: Terms offered: Spring 2021, Fall 2015, Fall 2012, Statistical Models: Theory and Application. Stat 140 is a probability course for Data 8 graduates who have also had a year of calculus and wish to go deeper into data science. Non-linear optimization with applications to statistical procedures. Concepts in Computing with Data: Read More [+], Summer: 10 weeks - 4 hours of lecture and 3 hours of laboratory per week, Concepts in Computing with Data: Read Less [-], Terms offered: Fall 2022, Summer 2022 8 Week Session, Spring 2022 Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory. Introduction to Probability and Statistics: Read More [+], Prerequisites: Mathematics 1A, Mathematics 16A, Mathematics 10A/10B, or consent of instructor. Model formulation, fitting, and validation and testing. Research term project. The R statistical language is used. Credit Restrictions: Students will receive no credit for Statistics 204 after completing Statistics 205A-205B. Applications are drawn from political science, economics, sociology, and public health. Biostatistical Methods: Survival Analysis and Causality: Read More [+], Prerequisites: Statistics 200B (may be taken concurrently), Biostatistical Methods: Survival Analysis and Causality: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020 Prerequisites might vary with instructor and topics. Discrete and continuous random variables. This course prepares students for data analysis with R. The focus is on the computational model that underlies the R language with the goal of providing a foundation for coding. 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. Introductory Probability and Statistics for Business: Terms offered: Summer 2021 8 Week Session, Summer 2020 8 Week Session, Summer 2019 8 Week Session, Terms offered: Spring 2021, Fall 2016, Fall 2003, Terms offered: Fall 2022, Spring 2022, Fall 2021. writing simple functions and control flow. Basic knowledge of probability/statistics and calculus are assume Approaches to causal inference using the potential outcomes framework. This is the second course, which focuses on sequence analysis, phylogenetics, and high-throughput microarray and sequencing gene expression experiments. The statistical and computational methods are motivated by and illustrated on data structures that arise in current high-dimensional inference problems in biology and medicine. 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. 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. Repeat rules: Course may be repeated for credit with instructor consent. Credit Restrictions: Students will receive no credit for Statistics W21 after completing Statistics 2, 20, 21, N21 or 25. Terms offered: Spring 2022, Spring 2021, Spring 2020, Spring 2018, Spring 2017. are primarily intended for graduate students and advanced undergraduate students from the mathematical sciences. Individual Study for Doctoral Candidates: Read Less [-], Terms offered: Prior to 2007 Bayesian information theoretic and structural risk minimization approaches. 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. Primary focus is from the analysis side. Credit Restrictions: Students will receive no credit for Statistics 201B after completing Statistics 200B. Case studies of applied modeling. Applications are drawn from a variety of fields including political science, economics, sociology, public health, and medicine. This course covers planning, conducting, and analyzing statistically designed experiments with an emphasis on hands-on experience. Students will be exposed to statistical questions that are relevant to decision and policy making. Introduction to Statistics at an Advanced Level: Terms offered: Fall 2019, Spring 2017, Spring 2015, Terms offered: Fall 2022, Fall 2021, Fall 2020, Fall 2019, Fall 2018, Advanced Topics in Probability and Stochastic Process, Terms offered: Fall 2020, Fall 2016, Fall 2015, Fall 2014. Introduction to Statistics: Read Less [-], Terms offered: Fall 2022, Summer 2022 8 Week Session, Spring 2022, Fall 2021, Summer 2021 8 Week Session, Fall 2020
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