An introduction to time series analysis in the time domain and spectral domain. Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week, Summer: 6 weeks - 7.5 hours of lecture and 5 hours of laboratory per week8 weeks - 5 hours of lecture and 4 hours of laboratory per week. , 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. with real data and assessing statistical assumptions. 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. Course Objectives: The emphasis on simulation and the bootstrap in Data 8 gives students a concrete sense of randomness and sampling variability. 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. 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. Brownian motion. This is part one of a year long series course. Least squares prediction. Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Introduction to Probability and Statistics: Introductory Probability and Statistics for Business, Terms offered: Summer 2022 8 Week Session, Fall 2016, Fall 2015. Biostatistical Methods: Survival Analysis and Causality: Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine. For students with mathematical background who wish to acquire basic concepts. Credit Restrictions: Course does not satisfy unit or residence requirements for doctoral degree. 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 Credit Restrictions: Enrollment is restricted; see the Introduction to Courses and Curricula section of this catalog. 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. Probability and sampling. Data, Inference, and Decisions: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 A deficient grade in STAT33A may be removed by taking STAT33B, or STAT133. The Statistics of Causal Inference in the Social Science: Read Less [-], Terms offered: Spring 2016, Spring 2015 Tools for reading, analyzing, and plotting data are covered, such as data input/output, reshaping data, the formula language, and graphics models. Introduction to Probability and Statistics at an Advanced Level: Read More [+]. Societal Risks and the Law: Read Less [-], Terms offered: Fall 2022 Convergence, Markov chains. Grading/Final exam status: Offered for pass/not pass grade only. The course is designed as a sequence with Statistics C205B/Mathematics C218B with the following combined syllabus. Probability spaces, random variables, distributions in probability and statistics, central limit theorem, Poisson processes, transformations involving random variables, estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory. 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 Advanced Topics in Probability and Stochastic Processes: Terms offered: Spring 2021, Fall 2015, Fall 2012, Statistical Models: Theory and Application. Repeat rules: Course may be repeated for credit when topic changes. The course is designed primarily for those who are already familiar with programming in another language, such as python, and want to understand how R works, and for those who already know the basics of R programming and want to gain a more in-depth understanding of the language in order to improve their coding. The main topics are classical randomized experiments, observational studies, instrumental variables, principal stratification and mediation analysis. Repeat rules: Course may be repeated for credit without restriction. Primary focus is from the analysis side. Credit Restrictions: Students will receive no credit for Statistics 200A-200B after completing Statistics 201A-201B. This course introduces the student to topics of current research interest in theoretical statistics. Discussion, problem review and development, guidance of laboratory classes, course development, supervised practice teaching. Statistics Research Seminar: Read More [+], Fall and/or spring: 15 weeks - 0 hours of seminar per week, Statistics Research Seminar: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Introduction to Probability and Statistics at an Advanced Level: Read More [+], Prerequisites: Multivariable calculus and one semester of linear algebra. This is the second course, which focuses on sequence analysis, phylogenetics, and high-throughput microarray and sequencing gene expression experiments. Field Study in Statistics: Read More [+], Fall and/or spring: 15 weeks - 1-3 hours of fieldwork per week, Summer: 6 weeks - 2.5-7.5 hours of fieldwork per week8 weeks - 1.5-5.5 hours of fieldwork per week. Introduction to Probability at an Advanced Level: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020 Quantitative/Statistical Research Methods in Social Sciences: Read More [+], Fall and/or spring: 15 weeks - 2 hours of lecture per week, Quantitative/Statistical Research Methods in Social Sciences: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Conditional expectation, independence, laws of large numbers. 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). Introduction to Advanced Programming in R: Read Less [-], Terms offered: Fall 2008, Fall 2007 Normal approximation. Introductory Probability and Statistics for Business: Read More [+]. implement the relevant methods using R. The Statistics of Causal Inference in the Social Science: Read More [+], Prerequisites: At least one graduate matrix based multivariate regression course in addition to introductory statistics and probability, Fall and/or spring: 15 weeks - 3-3 hours of lecture and 1-2 hours of discussion per week. Grading: Letter grade. Offered through the Student Learning Center. Grading: Letter grade. frame data science questions relevant to longitudinal studies as the estimation of statistical parameters generated from regression, Fall and/or spring: 15 weeks - 3 hours of seminar per week, Seminar on Topics in Probability and Statistics: Read Less [-], Terms offered: Spring 2021, Spring 2020, Spring 2019 The courses are primarily intended for graduate students and advanced undergraduate students from the mathematical sciences. 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. Software tools may include Bash, Git, Python, and LaTeX. Final exam required. 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 The selection of topics may vary from year to year. Classification regression, clustering, dimensionality, reduction, and density estimation. The Statistics of Causal Inference in the Social Science: Quantitative Methodology in the Social Sciences Seminar. Professional Preparation: Teaching of Probability and Statistics: Read More [+], Prerequisites: Graduate standing and appointment as a graduate student instructor, Fall and/or spring: 15 weeks - 2 hours of lecture and 4 hours of laboratory per week, Subject/Course Level: Statistics/Professional course for teachers or prospective teachers, Professional Preparation: Teaching of Probability and Statistics: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Computing techniques, numerical methods, simulation and general implementation of biostatistical analysis techniques with emphasis on data applications. Recent topics include: Bayesian statistics, statistics and finance, random matrix theory, high-dimensional statistics. Fall and/or spring: 15 weeks - 3 hours of lecture, 2 hours of discussion, and 1 hour of supplement per week, Probability for Data Science: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 A deficient grade in DATAC100\STATC100\COMPSCIC100 may be removed by taking DATA 100. Statistics Colloquium: Read More [+], Fall and/or spring: 15 weeks - 1-2 hours of colloquium per week. 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. The Berkeley Seminar Program has been designed to provide new students with the opportunity to explore an intellectual topic with a faculty member in a small-seminar setting. Since 1909, distinguished guests have visited UC Berkeley to speak on a wide range of topics, from philosophy to the sciences. Relative frequencies, discrete probability, random variables, expectation. Most time is spent on 2 approaches: mixed models based upon explicit (latent variable) maximum likelihood estimation of the sources of the dependence, versus empirical estimating equation approaches (generalized estimating equations). Credit Restrictions: Students will receive no credit for DATAC8\COMPSCIC8\INFOC8\STATC8 after completing COMPSCI 8, or DATA 8. An introduction to probability, emphasizing the combined use of mathematics and programming to solve problems. 
Stochastic Analysis with Applications to Mathematical Finance, Terms offered: Spring 2008, Spring 2006, Spring 2005. Topics include maximum likelihood and loss-based estimation, asymptotic linearity/normality, the delta method, bootstrapping, machine learning, targeted maximum likelihood estimation. Terms offered: Spring 2022, Fall 2021, Spring 2021 Participants will work on problems arising in the service and will discuss general ways of handling such problems. Freshman/Sophomore Seminar: Read More [+], Prerequisites: Priority given to freshmen and sophomores. Discrete and continuous random variables. 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. Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. Individual Study Leading to Higher Degrees: Read More [+], Fall and/or spring: 15 weeks - 2-36 hours of independent study per week, Summer: 6 weeks - 4-45 hours of independent study per week8 weeks - 3-36 hours of independent study per week10 weeks - 2.5-27 hours of independent study per week, Individual Study Leading to Higher Degrees: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 The course is designed as a sequence with with Statistics C205A/Mathematics C218A with the following combined syllabus. An introduction to linear algebra for data science. Stochastic Analysis with Applications to Mathematical Finance: Quantitative/Statistical Research Methods in Social Sciences, Terms offered: Spring 2016, Spring 2015, Spring 2014. 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. The R statistical language is used. BerkeleyX offers interactive online classes and MOOCs from the worlds best universities. Grading/Final exam status: Offered for pass/not pass grade only. Introduction to Statistical Computing: Read Less [-], Terms offered: Spring 2011, Spring 2010, Spring 2009 Fall and/or spring: 15 weeks - 1 hour of seminar per week. Individual Study for Doctoral Candidates: Read More [+], Prerequisites: One year of full-time graduate study and permission of the graduate adviser. 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. Possible topics include: analysis of qualitative/categorical data; loglinear models and latent-structure analysis; the analysis of cross-classified data having ordered and unordered categories; measure, models, and graphical displays in the analysis of cross-classified data; correspondence analysis, association analysis, and related methods of data analysis. Basic knowledge of probability/statistics and calculus are assume STAT133 recommended, Linear Modelling: Theory and Applications: Read Less [-], Terms offered: Spring 2020, Spring 2019, Spring 2018 Computational efficiency versus predictive performance. Credit Restrictions: Students will receive no credit for Statistics 204 after completing Statistics 205A-205B. Final exam not required. Topics include group comparisons and ANOVA, standard parametric statistical models, multivariate data visualization, multiple linear regression, logistic regression and classification, regression trees and random forests. Data 8), there is considerable demand for follow-on courses that build on the skills acquired in that class. Units may not be used to meet either unit or residence requirements for a master's degree. 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 An introduction to computationally intensive applied statistics. Regression. Fall and/or spring: 15 weeks - 2-9 hours of fieldwork per week, Summer: 6 weeks - 3-22 hours of fieldwork per week8 weeks - 2-16 hours of fieldwork per week10 weeks - 2-12 hours of fieldwork per week, Terms offered: Spring 2022, Fall 2021, Spring 2021 Conditional expectations, martingales and martingale convergence theorems. Probability for Applications: Read More [+]. Students engage in professionally-oriented group research under the supervision of a research advisor. Recommended prerequisite: Mathematics 55 or equivalent exposure to counting arguments, Summer: 10 weeks - 4.5 hours of lecture and 3 hours of laboratory per week, Modern Statistical Prediction and Machine Learning: Read Less [-], Terms offered: Fall 2022, Spring 2022, Summer 2021 8 Week Session The syllabus has been designed to maintain a mathematical level at least equal to that in Stat 134. Topics in Theoretical Statistics: Read More [+], Formerly known as: 216A-216B and 217A-217B, Topics in Theoretical Statistics: Read Less [-], Terms offered: Spring 2016 Credit Restrictions: Students will receive no credit for Statistics 200A after completing Statistics 201A-201B. 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. Principles and Techniques of Data Science: Introduction to Probability at an Advanced Level. 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. Approaches to causal inference using the potential outcomes framework. Quantitative/Statistical Research Methods in Social Sciences: Individual Study Leading to Higher Degrees. Corequisites: MATH54 or EECS16A. Credit Restrictions: Students will receive no credit for STAT201A after completing STAT200A. In this course, students will explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. This class will focus on quantitative critical thinking and key principles and techniques needed to carry out this cycle. Introduction to Statistics at an Advanced Level: Read Less [-], Terms offered: Fall 2019, Spring 2017, Spring 2015 The PDF will include all information unique to this page. Special Study for Honors Candidates: Read More [+], Fall and/or spring: 15 weeks - 0 hours of independent study per week, Summer: 6 weeks - 1-5 hours of independent study per week8 weeks - 1-4 hours of independent study per week, Special Study for Honors Candidates: Read Less [-], Terms offered: Fall 2021, Fall 2020, Spring 2017 Copyright 2022-23, UC Regents; all rights reserved. The course provides a broad theoretical framework for understanding the properties of commonly-used and more advanced methods. This course develops the probabilistic foundations of inference in data science, and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. The topics of this course change each semester, and multiple sections may be offered. 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 Introduction to Advanced Programming in R: Probability and Mathematical Statistics in Data Science. Special topics, by means of lectures and informational conferences. Model selection and stochastic realization. Theory and practice of statistical prediction. Through art and film programs, collections and research resources, BAM/PFA is the visual arts center of UC Berkeley.
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