ding: Letter grade. The goal of

2021/05/21

Grading: Letter grade. 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. 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 dinh Introduction to Probability and Statistics. Alternative to final exam. Introduction to Statistical Computing: Read Less [-], Terms offered: Spring 2011, Spring 2010, Spring 2009 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. 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 Repeat rules: Course may be repeated for credit without restriction. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets.

Applications may vary by term. Berkeley seminars are offered in all campus departments, and topics vary from department to department and semester to semester. Selected topics in quantitative/statistical methods of research in the social sciences and particularly in sociology. Terms offered: Spring 2022, Fall 2021, Spring 2021 Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory. Understand in depth and make use of principles of numerical linear algebra, optimization, and simulation for statistics-related research. Course Objectives: The emphasis on simulation and the bootstrap in Data 8 gives students a concrete sense of randomness and sampling variability. Applications are drawn from a variety of fields including political science, economics, sociology, public health and medicine.

Program effectively in languages including R and Python with an advanced knowledge of language functionality and an understanding of general programming concepts. Linear Modelling: Theory and Applications: Terms offered: Spring 2020, Spring 2019, Spring 2018, Modern Statistical Prediction and Machine Learning. Introductory Probability and Statistics for Business: Read More [+].

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. Use of numerical computation, graphics, simulation, and computer algebra. Credit Restrictions: Course does not satisfy unit or residence requirements for doctoral degree.

Join the online learning revolution!

Students will be exposed to statistical questions that are relevant to decision and policy making. Topics include marginal estimation of a survival function, estimation of a generalized multivariate linear regression model (allowing missing covariates and/or outcomes), estimation of a multiplicative intensity model (such as Cox proportional hazards model) and estimation of causal parameters assuming marginal structural models.

Statistical Computing: Read More [+], Prerequisites: Knowledge of a higher level programming language, Terms offered: Spring 2022, Spring 2021, Fall 2019 Effects of departures from the underlying assumptions.

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 Game Theory: Read More [+], Summer: 8 weeks - 6 hours of lecture per week, Terms offered: Fall 2022, Fall 2021, Fall 2020

The Statistics of Causal Inference in the Social Science.

Topics include numerical and graphical data summaries, loss-based estimation (regression, classification, density estimation), smoothing, EM algorithm, Markov chain Monte-Carlo, clustering, multiple testing, resampling, hidden Markov models, in silico experiments. Grading/Final exam status: Letter grade. This is part two of a year long series course. Advanced Topics in Probability and Stochastic Process: Advanced Topics in Probability and Stochastic Processes. Approaches to causal inference using the potential outcomes framework. Two and higher way layouts, residual analysis. This is part one of a year long series course.

Summer: 8 weeks - 6 hours of lecture and 3 hours of laboratory per week, Introduction to Probability and Statistics: Read Less [-], Terms offered: Summer 2022 8 Week Session, Fall 2016, Fall 2015 Grading: Offered for satisfactory/unsatisfactory grade only. Final exam required. Characteristic function methods. Field Study in Statistics: Read More [+]. An important focus of the course is on statistical computing and reproducible statistical analysis. Prerequisite or corequisite: Foundations of Data Science (COMPSCIC8 / INFOC8 / STATC8), Linear Algebra for Data Science: Read Less [-], Terms offered: Fall 2015 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. experience in analyzing real world data from the social, life, and physical sciences. Final exam required.

The R statistical language is used. Societal Risks and the Law: Read More [+], Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week. A deficient grade in DATAC8\COMPSCIC8\INFOC8\STATC8 may be removed by taking COMPSCI 8, COMPSCI 8, or DATA 8. 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. A project-based introduction to statistical data analysis. Terms offered: Fall 2022, Spring 2022, Fall 2021

Data, Inference, and Decisions: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Strongly recommended corequisite: Statistics 33A or Statistics 133, Statistical Methods for Data Science: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 Research term project.

This course teaches a broad range of statistical methods that are used to solve data problems.

General theory for developing locally efficient estimators of the parameters of interest in censored data models.

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.

Corequisites: MATH54 or EECS16A. 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.

Applications are drawn from a variety of fields including political science, economics, sociology, public health and medicine. Make a secure online gift by choosing a giving opportunity. Introduction to Probability and Statistics: Read More [+], Prerequisites: Mathematics 1A, Mathematics 16A, Mathematics 10A/10B, or consent of instructor. Topics covered may vary with instructor. 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 Stochastic Processes: Read More [+], Terms offered: Fall 2022, Fall 2021, Spring 2021 Topics in Theoretical Statistics: Read More [+], Formerly known as: 216A-216B and 217A-217B, Topics in Theoretical Statistics: Read Less [-], Terms offered: Spring 2016 Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra. Dependence, conditioning, Bayes methods. 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. Statistical Genomics: Read More [+], Terms offered: Fall 2021, Fall 2019, Fall 2018 Relative frequencies, discrete probability, random variables, expectation. Supervised experience relevant to specific aspects of statistics in on-campus or off-campus settings.

Credit Restrictions: Students will receive no credit for DATAC8\COMPSCIC8\INFOC8\STATC8 after completing COMPSCI 8, or DATA 8. 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. Concepts of Probability: Read More [+]. Sampling Surveys: Read More [+], Prerequisites: 101 or 134.

Large sample theory for non-normal linear models.

Introduction to Probability and Statistics at an Advanced Level: Read More [+], Prerequisites: Multivariable calculus and one semester of linear algebra.

Classification regression, clustering, dimensionality, reduction, and density estimation. Linear Modelling: Theory and Applications: Read More [+], Prerequisites: STAT 102 or STAT135. Statistics 133, 134, and 135 recommended, Statistical Models: Theory and Application: Read Less [-], Terms offered: Spring 2022, Spring 2021, Spring 2020 Final exam not required. Introduction to Probability and Statistics at an Advanced Level: Read More [+]. Special topics, by means of lectures and informational conferences.

Biostatistical Methods: Survival Analysis and Causality: Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine. , 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. Repeat rules: Course may be repeated for credit up to a total of 16 units. This course will focus on approaches to causal inference using the potential outcomes framework. Introduction to Statistics at an Advanced Level: Read Less [-], Terms offered: Fall 2019, Spring 2017, Spring 2015

Modern Statistical Prediction and Machine Learning: Terms offered: Fall 2022, Spring 2022, Summer 2021 8 Week Session, Terms offered: Fall 2022, Fall 2021, Fall 2020. mix of statistical theory and data analysis. Frequency-based techniques of time series analysis, spectral theory, linear filters, estimation of spectra, estimation of transfer functions, design, system identification, vector-valued stationary processes, model building.

Credit Restrictions: Students will receive no credit for Statistics 201B after completing Statistics 200B. Statistical Genomics: Read More [+], Prerequisites: Statistics 200A and 200B or equivalent (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 Analysis of survival time data using parametric and non-parametric models, hypothesis testing, and methods for analyzing censored (partially observed) data with covariates. The topics of this course change each semester, and multiple sections may be offered. Through art and film programs, collections and research resources, BAM/PFA is the visual arts center of UC Berkeley. Professional Preparation: Teaching of Probability and Statistics: Individual Study for Master's Candidates: Individual Study for Doctoral Candidates: Berkeley Berkeley Academic Guide: Academic Guide 2022-23. 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. Measure theory concepts needed for probability. Credit Restrictions: Students will receive no credit for Statistics 204 after completing Statistics 205A-205B.

Tools for reading, analyzing, and plotting data are covered, such as data input/output, reshaping data, the formula language, and graphics models. Particular topics vary with instructor. Introduction to Statistical Computing: Read More [+]. Conditional expectations, martingales and martingale convergence theorems. Terms offered: Fall 2022, Spring 2022, Fall 2021 Knowledge of scientific computing environment (R or Matlab) often required. Selected topics such as the Poisson process, Markov chains, characteristic functions.

Credit Restrictions: Students will receive no credit for STAT33B after completing STAT133.

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. Final exam required. This is the second course, which focuses on sequence analysis, phylogenetics, and high-throughput microarray and sequencing gene expression experiments. Laws of large numbers and central limit theorems for independent random variables. Discussion, problem review and development, guidance of laboratory classes, course development, supervised practice teaching. 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. Seminar on Topics in Probability and Statistics, Terms offered: Spring 2022, Fall 2020, Spring 2020. It will also use causal diagrams at an intuitive level.

Special topics in probability and statistics offered according to student demand and faculty availability. Exploratory data analysis (e.g., graphical data summaries, PCAs, clustering analysis). Statistics 135 may be taken concurrently. The R statistical language is used. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? Through case studies, computer laboratories, and a term project, students will learn practical techniques and tools for producing statistically sound and appropriate, reproducible, and verifiable computational answers to scientific questions. 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. Probability Theory: Read More [+], Terms offered: Fall 2020, Fall 2016, Fall 2015, Fall 2014 Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Immerse yourself in performances and programs from around the world that explore the intersections of education and the performing arts. Theory and practice of sampling from finite populations. Examples of possible topics include planning and design of experiments, ANOVA and random effects models, splines, classification, spatial statistics, categorical data analysis, survival analysis, and multivariate analysis. Error estimation for complex samples. Testing hypotheses. Credit Restrictions: Students will receive no credit for DATAC100\STATC100\COMPSCIC100 after completing DATA 100. Enrollment limited to 15 freshmen. 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. Other topics covered include the principles of design, randomization, ANOVA, response surface methodoloy, and computer experiments. 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. Conditional expectations, martingales and martingale convergence theorems. 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 Least squares prediction. Statistical Learning Theory: Read More [+], Instructors: Bartlett, Jordan, Wainwright, Statistical Learning Theory: Read Less [-], Terms offered: Spring 2022, Spring 2017, Spring 2016

Basic theory for Bayesian methods and decision theory. Hands-on-experience with solving real data problems with high-level programming languages such as R. Emphasis on examining the assumptions behind standard statistical models and methods. The topics of this course change each semester, and multiple sections may be offered.

Statistical Methods for Data Science: Read More [+], Prerequisites: Statistics/Computer Science/Information C8 or Statistics 20; and Mathematics 1A, Mathematics 16A, or Mathematics 10A/10B. Standard measures of location, spread and association. Final exam not required. Terms offered: Fall 2022, Spring 2022, Fall 2021, Spring 2021, Linear Modelling: Theory and Applications, Terms offered: Fall 2022, Fall 2021, Spring 2021. 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 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.

If you just want to print information on specific tabs, you're better off downloading a PDF of the page, opening it, and then selecting the pages you really want to print. Advanced Topics in Probability and Stochastic Processes: Read More [+], Advanced Topics in Probability and Stochastic Processes: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020 Participants will work on problems arising in the service and will discuss general ways of handling such problems. Principles and Techniques of Data Science: Introduction to Probability at an Advanced Level. General theory for developing locally efficient estimators of the parameters of interest in censored data models. Introduction to Probability and Statistics at an Advanced Level: Terms offered: Spring 2019, Spring 2012, Spring 2011, Principles and Techniques of Data Science, Terms offered: Fall 2022, Spring 2022, Fall 2021, Spring 2021, Spring 2020. visualization; measurement error and prediction; and techniques for scalable data processing. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. This course introduces the student to topics of current research interest in theoretical statistics. Terms offered: Summer 2016 10 Week Session, Summer 2015 10 Week Session, Summer 2014 10 Week Session Applications are drawn from political science, economics, sociology, and public health.

Data 8), there is considerable demand for follow-on courses that build on the skills acquired in that class. 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.

Introduction to Statistics: Read More [+]. Covers observational studies with and without ignorable treatment assignment, randomized experiments with and without noncompliance, instrumental variables, regression discontinuity, sensitivity analysis and randomization inference. To be taken concurrently with service as a consultant in the department's drop-in consulting service. The courses are primarily intended for graduate students and advanced undergraduate students from the mathematical sciences. The first course in this two-semester sequence is Public Health C240E/Statistics C245E. Course covers major topics in general statistical theory, with a focus on statistical methods in epidemiology. Credit Restrictions: Students will receive no credit for STAT 88 after completing STAT134, STATC140, STAT135, or STAT 102.

The Statistics of Causal Inference in the Social Science: Read More [+], Terms offered: Spring 2018, Spring 2017 Concepts in statistical programming and statistical computation, including programming principles, data and text manipulation, parallel processing, simulation, numerical linear algebra, and optimization.

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.

Individual study Individual and/or group meetings with faculty. 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 A provisional grade of IP (in progress) will be applied and later replaced with the final grade after completing part two of the series. Non-linear optimization with applications to statistical procedures. A deficient grade in STAT33B may be removed by taking STAT133.

Societal Risks and the Law: Read Less [-], Terms offered: Fall 2022 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 133 or 135 recommended, Introduction to Time Series: Read Less [-], Terms offered: Fall 2022, Spring 2022, Fall 2021 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 PDF will include all information unique to this page.

Quantitative Methodology in the Social Sciences Seminar: Read More [+], Prerequisites: Statistics 239A or equivalent. 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 STATC140 may be removed by taking STAT134. 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 Must be taken at the same time as either Statistics 2 or 21.

The course is designed as a sequence with Statistics C205B/Mathematics C218B with the following combined syllabus. Quantitative Methodology in the Social Sciences Seminar: Read More [+], Terms offered: Spring 2021, Fall 2017, Fall 2016

Repeat rules: Course may be repeated for credit with instructor consent. Applications are drawn from a variety of fields including political science, economics, sociology, public health, and medicine. Advanced topics in probability offered according to students demand and faculty availability.

Typical topics have been model selection; empirical and point processes; the bootstrap, stochastic search, and Monte Carlo integration; information theory and statistics; semi- and non-parametric modeling; time series and survival analysis. Credit Restrictions: Students will receive no credit for Statistics 200A after completing Statistics 201A-201B. Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine: Read More [+], Prerequisites: Statistics 200A or equivalent (may be taken concurrently), Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine: Read Less [-], Terms offered: Fall 2017, Fall 2015, Fall 2013

Individual Study Leading to Higher Degrees: Professional Preparation: Teaching of Probability and Statistics. This is the first course of a two-semester sequence, which provides an introduction to statistical and computational methods for the analysis of meiosis, population genetics, and genetic mapping. Typical topics have been model selection; empirical and point processes; the bootstrap, stochastic search, and Monte Carlo integration; information theory and statistics; semi- and non-parametric modeling; time series and survival analysis. 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), Advanced Topics in Learning and Decision Making: Terms offered: Spring 2011, Spring 2010, Spring 2009, Introduction to Modern Biostatistical Theory and Practice, Terms offered: Spring 2022, Spring 2021, Fall 2019, , asymptotic linearity/normality, the delta method, bootstrapping, machine learning, targeted maximum likelihood estimation. Experience with R is assumed. In this connector course we will state precisely and prove results discovered while exploring data in Data 8. Experience with R is assumed. Course emphasizes version control, testing, process automation, code review, and collaborative programming. 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. Comprehension of broad concepts is the main goal, but practical implementation in R is also emphasized. 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 Sampling with unequal probabilities.

Other topics of current interest, such as issues of efficiency, and use of graphics. Strongly recommended corerequisite: STAT133, Terms offered: Fall 2022, Spring 2022, Fall 2021, Spring 2021 Primary focus is from the analysis side. Brownian motion. Characteristic function methods. Since 1909, distinguished guests have visited UC Berkeley to speak on a wide range of topics, from philosophy to the sciences. 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