Statistics

University of California, Berkeley

This is an archived copy of the 2017-18 guide. To access the most recent version of the guide, please visit http://guide.berkeley.edu.

Overview

The Department of Statistics grants BA, MA, and PhD degrees in Statistics. The undergraduate and graduate programs allow students to participate in a field that is growing in importance and breadth of application. Understanding the natural and human worlds in the Information Age increasingly requires statistical reasoning and methods, and stochastic models are essential components of research and applications across a vast spectrum of fields. The Department of Statistics provides students with world-class resources for study and research, including access to the extensive computational facilities maintained by the Statistical Computing Facility.

Facilities and Resources

The Statistical Computing Facility (SCF) is a unit of the Department of Statistics. Its mission is to provide undergraduate students, graduate students, postdocs, and faculty in the Statistics Department at Berkeley with state-of-the-art computing resources, services, and technical knowledge, supporting them in carrying out cutting-edge research activities, innovative instructional programs, and efficient day-to-day computing activities. The SCF also supports the students and faculty of the Econometrics Laboratory of the Department of Economics.

The Department of Statistics operates a consulting service in which advanced graduate students, under faculty supervision, are available as consultants during specified hours. The service is associated with the course, which may be taken for credit. Consulting is free to members of the campus community. Statistical advice can be sought at any stage of the research process. Those seeking statistical advice are encouraged to contact consultants early in the research process. Refer to the Department of Statistics website to find out which faculty member is currently coordinating this service.

Three seminars regularly take place in the department: the Neyman seminar,  the probability seminar, and the statistics and genomics seminar. Each year, the department also has two joint seminars with Stanford and a joint seminar with UC Davis.

Undergraduate Programs

Statistics: BA, Minor

Graduate Programs

Statistics: MA, PhD

Visit Department Website

Courses

Statistics

STAT 0PX Preparatory Statistics 1 Unit

Terms offered: Summer 2016 10 Week Session, Summer 2015 10 Week Session, Summer 2014 10 Week Session
This course assists entering Freshman students with basic statistical concepts and problem solving. Designed for students who do not meet the prerequisites for 2. Offered through the Student Learning Center.
Preparatory Statistics: Read More [+]

STAT 2 Introduction to Statistics 4 Units

Terms offered: Fall 2018, Summer 2018 8 Week Session, Spring 2018
Population and variables. Standard measures of location, spread and association. Normal approximation. Regression. Probability and sampling. Binomial distribution. Interval estimation. Some standard significance tests.
Introduction to Statistics: Read More [+]

STAT C8 Foundations of Data Science 4 Units

Terms offered: Fall 2018, Summer 2018 8 Week Session, Spring 2018, Fall 2017
Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? 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. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.
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STAT C8R Introduction to Computational Thinking with Data 3 Units

Terms offered: Prior to 2007
An introduction to computational thinking and quantitative reasoning, preparing students for further coursework, especially Foundations of Data Science (CS/Info/Stat C8). Emphasizes the use of computation to gain insight about quantitative problems with real data. Expressions, data types, collections, and tables in Python. Programming practices, abstraction, and iteration. Visualizing univariate and bivariate data with bar charts, histograms, plots, and maps. Introduction to statistical concepts including averages and distributions, predicting one variable from another, association and causality, probability and probabilistic simulation. Relationship between numerical functions and graphs. Sampling and introduction to inference.
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STAT 20 Introduction to Probability and Statistics 4 Units

Terms offered: Fall 2018, Summer 2018 8 Week Session, Spring 2018
For students with mathematical background who wish to acquire basic concepts. Relative frequencies, discrete probability, random variables, expectation. Testing hypotheses. Estimation. Illustrations from various fields.
Introduction to Probability and Statistics: Read More [+]

STAT 21 Introductory Probability and Statistics for Business 4 Units

Terms offered: Fall 2016, Fall 2015, Fall 2014
Descriptive statistics, probability models and related concepts, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.
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STAT W21 Introductory Probability and Statistics for Business 4 Units

Terms offered: Summer 2018 8 Week Session, Spring 2018, Summer 2017 8 Week Session
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.
Introductory Probability and Statistics for Business: Read More [+]

STAT 24 Freshman Seminars 1 Unit

Terms offered: Fall 2016, Fall 2003, Spring 2001
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. Berkeley seminars are offered in all campus departments, and topics vary from department to department and semester to semester. Enrollment limited to 15 freshmen.
Freshman Seminars: Read More [+]

STAT 28 Statistical Methods for Data Science 4 Units

Terms offered: Spring 2018, Spring 2017
This is a lower-division course that is a follow-up to STAT8/CS8 (Foundations of Data Science). The course will teach a broad range of statistical methods that are used to solve data problems. Topics will include group comparisons and ANOVA, standard parametric statistical models, multivariate data visualization, multiple linear regression and classification, classification and regression trees and random forests. An important focus of the course will be on statistical computing and reproducible statistical analysis. The students will be introduced to the widely used R statistical language and they will obtain hands-on experience in implementing a range of commonly used statistical methods on numerous real world datasets.
Statistical Methods for Data Science: Read More [+]

STAT 39D Freshman/Sophomore Seminar 2 - 4 Units

Terms offered: Fall 2008, Fall 2007
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. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester.
Freshman/Sophomore Seminar: Read More [+]

STAT C79 Societal Risks and the Law 3 Units

Terms offered: Spring 2013
Defining, perceiving, quantifying and measuring risk; identifying risks and estimating their importance; determining whether laws and regulations can protect us from these risks; examining how well existing laws work and how they could be improved; evaluting costs and benefits. Applications may vary by term. This course cannot be used to complete engineering unit or technical elective requirements for students in the College of Engineering.
Societal Risks and the Law: Read More [+]

STAT 88 Probability and Mathematical Statistics in Data Science 2 Units

Terms offered: Fall 2018, Spring 2018, Fall 2017
In this connector course we will state precisely and prove results discovered in the foundational data science course through working with data. Topics include: total variation distance between discrete distributions; the mean, standard deviation, and tail bounds; correlation, and the derivation of the regression equation; probabilities, random variables, and the Central Limit Theorem; probabilistic models; symmetries in random permutations; prior and posterior distributions, and Bayes’ rule.
Probability and Mathematical Statistics in Data Science: Read More [+]

STAT 89A Linear Algebra for Data Science 4 Units

Terms offered: Spring 2018, Spring 2017, Spring 2016
An introduction to linear algebra for data science. The course will cover introductory topics in linear algebra, starting with the basics; discrete probability and how prob- ability can be used to understand high-dimensional vector spaces; matrices and graphs as popular mathematical structures with which to model data (e.g., as models for term-document corpora, high-dimensional regression problems, ranking/classification of web data, adjacency properties of social network data, etc.); and geometric approaches to eigendecompositions, least-squares, principal components analysis, etc.
Linear Algebra for Data Science: Read More [+]

STAT 94 Special Topics in Probability and Statistics 1 - 4 Units

Terms offered: Fall 2015
Topics will vary semester to semester.
Special Topics in Probability and Statistics: Read More [+]

STAT 97 Field Study in Statistics 1 - 3 Units

Terms offered: Fall 2015, Spring 2012
Supervised experience relevant to specific aspects of statistics in off-campus settings. Individual and/or group meetings with faculty.
Field Study in Statistics: Read More [+]

STAT 98 Directed Group Study 1 - 3 Units

Terms offered: Fall 2014, Fall 2013, Spring 2013
Must be taken at the same time as either Statistics 2 or 21. This course assists lower division statistics students with structured problem solving, interpretation and making conclusions.
Directed Group Study: Read More [+]

STAT C100 Principles & Techniques of Data Science 4 Units

Terms offered: Fall 2018, Spring 2018, Fall 2017
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. 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.
Principles & Techniques of Data Science: Read More [+]

STAT 131A Introduction to Probability and Statistics for Life Scientists 4 Units

Terms offered: Fall 2018, Spring 2018, Fall 2017
Ideas for estimation and hypothesis testing basic to applications, including an introduction to probability. Linear estimation and normal regression theory.
Introduction to Probability and Statistics for Life Scientists: Read More [+]

STAT 133 Concepts in Computing with Data 3 Units

Terms offered: Fall 2018, Spring 2018, Fall 2017
An introduction to computationally intensive applied statistics. Topics will include organization and use of databases, visualization and graphics, statistical learning and data mining, model validation procedures, and the presentation of results.
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STAT 134 Concepts of Probability 4 Units

Terms offered: Fall 2018, Summer 2018 8 Week Session, Spring 2018
An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.
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STAT 135 Concepts of Statistics 4 Units

Terms offered: Fall 2018, Summer 2018 8 Week Session, Spring 2018
A comprehensive survey course in statistical theory and methodology. 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. The laboratory includes computer-based data-analytic applications to science and engineering.
Concepts of Statistics: Read More [+]

STAT 140 Probability for Data Science 4 Units

Terms offered: Fall 2018, Spring 2018, Spring 2017
An introduction to probability, emphasizing the combined use of mathematics and programming to solve problems. Random variables, discrete and continuous families of distributions. Bounds and approximations. Dependence, conditioning, Bayes methods. Convergence, Markov chains. Least squares prediction. Random permutations, symmetry, order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
Probability for Data Science: Read More [+]

STAT 150 Stochastic Processes 3 Units

Terms offered: Fall 2018, Spring 2018, Fall 2017
Random walks, discrete time Markov chains, Poisson processes. Further topics such as: continuous time Markov chains, queueing theory, point processes, branching processes, renewal theory, stationary processes, Gaussian processes.
Stochastic Processes: Read More [+]

STAT 151A Linear Modelling: Theory and Applications 4 Units

Terms offered: Fall 2018, Spring 2018, Fall 2017
A coordinated treatment of linear and generalized linear models and their application. 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.
Linear Modelling: Theory and Applications: Read More [+]

STAT 152 Sampling Surveys 4 Units

Terms offered: Spring 2018, Spring 2017, Spring 2016
Theory and practice of sampling from finite populations. Simple random, stratified, cluster, and double sampling. Sampling with unequal probabilities. Properties of various estimators including ratio, regression, and difference estimators. Error estimation for complex samples.
Sampling Surveys: Read More [+]

STAT 153 Introduction to Time Series 4 Units

Terms offered: Fall 2018, Spring 2018, Fall 2017
An introduction to time series analysis in the time domain and spectral domain. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra.
Introduction to Time Series: Read More [+]

STAT 154 Modern Statistical Prediction and Machine Learning 4 Units

Terms offered: Fall 2018, Spring 2018, Fall 2017
Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.
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STAT 155 Game Theory 3 Units

Terms offered: Fall 2018, Summer 2018 8 Week Session, Spring 2018
General theory of zero-sum, two-person games, including games in extensive form and continuous games, and illustrated by detailed study of examples.
Game Theory: Read More [+]

STAT 157 Seminar on Topics in Probability and Statistics 3 Units

Terms offered: Fall 2017, Fall 2016, Spring 2016
Substantial student participation required. The topics to be covered each semester that the course may be offered will be announced by the middle of the preceding semester; see departmental bulletins. Recent topics include: Bayesian statistics, statistics and finance, random matrix theory, high-dimensional statistics.
Seminar on Topics in Probability and Statistics: Read More [+]

STAT 158 The Design and Analysis of Experiments 4 Units

Terms offered: Spring 2018, Spring 2016, Spring 2015
An introduction to the design and analysis of experiments. This course covers planning, conducting, and analyzing statistically designed experiments with an emphasis on hands-on experience. Standard designs studied include factorial designs, block designs, latin square designs, and repeated measures designs. Other topics covered include the principles of design, randomization, ANOVA, response surface methodoloy, and computer experiments.
The Design and Analysis of Experiments: Read More [+]

STAT 159 Reproducible and Collaborative Statistical Data Science 4 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016
A project-based introduction to statistical data analysis. 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. Course emphasizes version control, testing, process automation, code review, and collaborative programming. Software tools may include Bash, Git, Python, and LaTeX.
Reproducible and Collaborative Statistical Data Science: Read More [+]

STAT H195 Special Study for Honors Candidates 1 - 4 Units

Terms offered: Spring 2015, Fall 2014, Fall 2010

Special Study for Honors Candidates: Read More [+]

STAT 197 Field Study in Statistics 1 - 3 Units

Terms offered: Spring 2017, Fall 2015, Summer 2015 10 Week Session
Supervised experience relevant to specific aspects of statistics in off-campus settings. Individual and/or group meetings with faculty.
Field Study in Statistics: Read More [+]

STAT 198 Directed Study for Undergraduates 1 - 3 Units

Terms offered: Spring 2018, Spring 2016, Fall 2015
Special tutorial or seminar on selected topics.
Directed Study for Undergraduates: Read More [+]

STAT 199 Supervised Independent Study and Research 1 - 3 Units

Terms offered: Spring 2017, Fall 2015, Spring 2015

Supervised Independent Study and Research: Read More [+]

STAT 200A Introduction to Probability and Statistics at an Advanced Level 4 Units

Terms offered: Fall 2018, Fall 2011, Fall 2010
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.
Introduction to Probability and Statistics at an Advanced Level: Read More [+]

STAT 200B Introduction to Probability and Statistics at an Advanced Level 4 Units

Terms offered: Spring 2012, Spring 2011, Spring 2010
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.
Introduction to Probability and Statistics at an Advanced Level: Read More [+]

STAT C200C Principles and Techniques of Data Science 4 Units

Terms offered: Not yet offered
Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Research term project.,Terms offered: Fall 2018
Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Research term project.
Principles and Techniques of Data Science: Read More [+]

STAT C200C Principles and Techniques of Data Science 4 Units

Terms offered: Not yet offered
Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Research term project.,Terms offered: Fall 2018
Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Research term project.
Principles and Techniques of Data Science: Read More [+]

STAT 201A Introduction to Probability at an Advanced Level 4 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016
Distributions in probability and statistics, central limit theorem, Poisson processes, modes of convergence, transformations involving random variables.
Introduction to Probability at an Advanced Level: Read More [+]

STAT 201B Introduction to Statistics at an Advanced Level 4 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016
Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.
Introduction to Statistics at an Advanced Level: Read More [+]

STAT 204 Probability for Applications 4 Units

Terms offered: Spring 2017, Spring 2015, Fall 2012
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.
Probability for Applications: Read More [+]

STAT C205A Probability Theory 4 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016
The course is designed as a sequence with Statistics C205B/Mathematics C218B with the following combined syllabus. Measure theory concepts needed for probability. Expection, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations, martingales and martingale convergence theorems. Markov chains. Stationary processes. Brownian motion.
Probability Theory: Read More [+]

STAT C205B Probability Theory 4 Units

Terms offered: Spring 2018, Spring 2017, Spring 2016
The course is designed as a sequence with with Statistics C205A/Mathematics C218A with the following combined syllabus. Measure theory concepts needed for probability. Expection, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations, martingales and martingale convergence theorems. Markov chains. Stationary processes. Brownian motion.
Probability Theory: Read More [+]

STAT C206A Advanced Topics in Probability and Stochastic Process 3 Units

Terms offered: Fall 2016, Fall 2015, Fall 2014, Fall 2013
The topics of this course change each semester, and multiple sections may be offered. Advanced topics in probability offered according to students demand and faculty availability.
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STAT C206B Advanced Topics in Probability and Stochastic Processes 3 Units

Terms offered: Spring 2018, Spring 2017, Spring 2016
The topics of this course change each semester, and multiple sections may be offered. Advanced topics in probability offered according to students demand and faculty availability.
Advanced Topics in Probability and Stochastic Processes: Read More [+]

STAT 210A Theoretical Statistics 4 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016
An introduction to mathematical statistics, covering both frequentist and Bayesian aspects of modeling, inference, and decision-making. 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.
Theoretical Statistics: Read More [+]

STAT 210B Theoretical Statistics 4 Units

Terms offered: Spring 2018, Spring 2017, Spring 2016
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. Topics covered may vary with instructor.
Theoretical Statistics: Read More [+]

STAT 212A Topics in Theoretical Statistics 3 Units

Terms offered: Fall 2015, Fall 2012, Fall 2011
This course introduces the student to topics of current research interest in theoretical statistics. Recent topics include information theory, multivariate analysis and random matrix theory, high-dimensional inference. 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.
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STAT 212B Topics in Theoretical Statistics 3 Units

Terms offered: Spring 2016
This course introduces the student to topics of current research interest in theoretical statistics. Recent topics include information theory, multivariate analysis and random matrix theory, high-dimensional inference. 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.
Topics in Theoretical Statistics: Read More [+]

STAT 215A Statistical Models: Theory and Application 4 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016
Applied statistics with a focus on critical thinking, reasoning skills, and techniques. 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. Exploratory data analysis (e.g., graphical data summaries, PCAs, clustering analysis). Model formulation, fitting, and validation and testing. Linear regression and generalizations (e.g., GLMs, ridge regression, lasso).
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STAT 215B Statistical Models: Theory and Application 4 Units

Terms offered: Spring 2018, Spring 2017, Spring 2016
Course builds on 215A in developing critical thinking skills and the techniques of advanced applied statistics. Particular topics vary with instructor. 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.
Statistical Models: Theory and Application: Read More [+]

STAT 222 Masters of Statistics Capstone Project 4 Units

Terms offered: Spring 2018, Spring 2017, Spring 2016
The capstone project is part of the masters degree program in statistics. Students engage in professionally-oriented group research under the supervision of a research advisor. The research synthesizes the statistical, computational, economic, and social issues involved in solving complex real-world problems.
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STAT 230A Linear Models 4 Units

Terms offered: Spring 2018, Spring 2017, Spring 2016
Theory of least squares estimation, interval estimation, and tests under the general linear fixed effects model with normally distributed errors. Large sample theory for non-normal linear models. Two and higher way layouts, residual analysis. Effects of departures from the underlying assumptions. Robust alternatives to least squares.
Linear Models: Read More [+]

STAT 232 Experimental Design 4 Units

Terms offered: Fall 2018, Spring 2013, Fall 2009
Randomization, blocking, factorial design, confounding, fractional replication, response surface methodology, optimal design. Applications.
Experimental Design: Read More [+]

STAT 238 Bayesian Statistics 3 Units

Terms offered: Fall 2016
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. Case studies of applied modeling. In-depth computational implementation using Markov chain Monte Carlo and other techniques. Basic theory for Bayesian methods and decision theory. The selection of topics may vary from year to year.
Bayesian Statistics: Read More [+]

STAT 239A The Statistics of Causal Inference in the Social Science 4 Units

Terms offered: Fall 2015, Fall 2014
Approaches to causal inference using the potential outcomes framework. Covers observational studies with and without ignorable treatment assignment, randomized experiments with and without noncompliance, instrumental variables, regression discontinuity, sensitivity analysis and randomization inference. Applications are drawn from a variety of fields including political science, economics, sociology, public health and medicine.
The Statistics of Causal Inference in the Social Science: Read More [+]

STAT 239B Quantitative Methodology in the Social Sciences Seminar 4 Units

Terms offered: Spring 2016, Spring 2015
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. Applications are drawn from political science, economics, sociology, and public health. Experience with R is assumed.
Quantitative Methodology in the Social Sciences Seminar: Read More [+]

STAT C239A The Statistics of Causal Inference in the Social Science 4 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016
Approaches to causal inference using the potential outcomes framework. Covers observational studies with and without ignorable treatment assignment, randomized experiments with and without noncompliance, instrumental variables, regression discontinuity, sensitivity analysis and randomization inference. Applications are drawn from a variety of fields including political science, economics, sociology, public health and medicine.
The Statistics of Causal Inference in the Social Science: Read More [+]

STAT C239B Quantitative Methodology in the Social Sciences Seminar 4 Units

Terms offered: Spring 2018, Spring 2017
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. Applications are drawn from political science, economics, sociology, and public health. Experience with R is assumed.
Quantitative Methodology in the Social Sciences Seminar: Read More [+]

STAT 240 Nonparametric and Robust Methods 4 Units

Terms offered: Fall 2017, Fall 2016, Spring 2015
Standard nonparametric tests and confidence intervals for continuous and categorical data; nonparametric estimation of quantiles; robust estimation of location and scale parameters. Efficiency comparison with the classical procedures.
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STAT C241A Statistical Learning Theory 3 Units

Terms offered: Fall 2016, Fall 2015, Fall 2014
Classification regression, clustering, dimensionality, reduction, and density estimation. 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. Ensemble methods.
Statistical Learning Theory: Read More [+]

STAT C241B Advanced Topics in Learning and Decision Making 3 Units

Terms offered: Spring 2017, Spring 2016, Spring 2014
Recent topics include: Graphical models and approximate inference algorithms. Markov chain Monte Carlo, mean field and probability propagation methods. Model selection and stochastic realization. Bayesian information theoretic and structural risk minimization approaches. Markov decision processes and partially observable Markov decision processes. Reinforcement learning.
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STAT 243 Introduction to Statistical Computing 4 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016
Concepts in statistical programming and statistical computation, including programming principles, data and text manipulation, parallel processing, simulation, numerical linear algebra, and optimization.
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STAT 244 Statistical Computing 4 Units

Terms offered: Spring 2011, Spring 2010, Spring 2009
Algorithms in statistical computing: random number generation, generating other distributions, random sampling and permutations. Matrix computations in linear models. Non-linear optimization with applications to statistical procedures. Other topics of current interest, such as issues of efficiency, and use of graphics.
Statistical Computing: Read More [+]

STAT C245A Introduction to Modern Biostatistical Theory and Practice 4 Units

Terms offered: Spring 2018, Spring 2017, Spring 2016
Course covers major topics in general statistical theory, with a focus on statistical methods in epidemiology. The course provides a broad theoretical framework for understanding the properties of commonly-used and more advanced methods. Emphasis is on estimation in nonparametric models in the context of contingency tables, regression (e.g., linear, logistic), density estimation and more. Topics include maximum likelihood and loss-based estimation, asymptotic linearity/normality, the delta method, bootstrapping, machine learning, targeted maximum likelihood estimation. Comprehension of broad concepts is the main goal, but practical implementation in R is also emphasized. Basic knowledge of probability/statistics and calculus are assume
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STAT C245B Biostatistical Methods: Survival Analysis and Causality 4 Units

Terms offered: Fall 2018, Fall 2017, Fall 2015, Fall 2013
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 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. General theory for developing locally efficient estimators of the parameters of interest in censored data models. Computing techniques, numerical methods, simulation and general implementation of biostatistical analysis techniques with emphasis on data applications.
Biostatistical Methods: Survival Analysis and Causality: Read More [+]

STAT C245C Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine 4 Units

Terms offered: Fall 2016, Fall 2015, Fall 2014, Fall 2012
This course provides an introduction to computational statistics, with emphasis on statistical methods and software for addressing high-dimensional inference problems in biology and medicine. 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.
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STAT C245D Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine II 4 Units

Terms offered: Fall 2018, Fall 2017, Fall 2015
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. The courses also discusses statistical computing resources, with emphasis on the R language and environment (www.r-project.org). Programming topics to be discussed include: data structures, functions, statistical models, graphical procedures, designing an R package, object-oriented programming, inter-system interfaces. 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.
Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine II: Read More [+]

STAT C245E Statistical Genomics 4 Units

Terms offered: Spring 2013, Fall 2012, Fall 2010, Fall 2009
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. 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. The second course is Statistics C245F/Public Health C240F. The courses are primarily intended for graduate students and advanced undergraduate students from the mathematical sciences.
Statistical Genomics: Read More [+]

STAT C245F Statistical Genomics 4 Units

Terms offered: Spring 2018, Spring 2017, Spring 2016, Spring 2015
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. The first course in this two-semester sequence is Public Health C240E/Statistics C245E. This is the second course, which focuses on sequence analysis, phylogenetics, and high-throughput microarray and sequencing gene expression experiments. The courses are primarily intended for graduate students and advanced undergraduate students from the mathematical sciences.
Statistical Genomics: Read More [+]

STAT C247C Longitudinal Data Analysis 4 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016
The course covers the statistical issues surrounding estimation of effects using data on subjects followed through time. The course emphasizes a regression model approach and discusses disease incidence modeling and both continuous outcome data/linear models and longitudinal extensions to nonlinear models (e.g., logistic and Poisson). The primary focus is from the analysis side, but mathematical intuition behind the procedures will also be discussed. The statistical/mathematical material includes some survival analysis, linear models, logistic and Poisson regression, and matrix algebra for statistics. The course will conclude with an introduction to recently developed causal regression techniques (e.g., marginal structural models). Time permitting, serially correlated data on ecological units will also be discussed.
Longitudinal Data Analysis: Read More [+]

STAT 248 Analysis of Time Series 4 Units

Terms offered: Spring 2018, Spring 2017, Spring 2016
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.
Analysis of Time Series: Read More [+]

STAT 259 Reproducible and Collaborative Statistical Data Science 4 Units

Terms offered: Fall 2018, Fall 2017, Spring 2016
A project-based introduction to statistical data analysis. 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. Course emphasizes version control, testing, process automation, code review, and collaborative programming. Software tools may include Bash, Git, Python, and LaTeX.
Reproducible and Collaborative Statistical Data Science: Read More [+]

STAT 260 Topics in Probability and Statistics 3 Units

Terms offered: Spring 2018, Spring 2017, Spring 2016
Special topics in probability and statistics offered according to student demand and faculty availability.
Topics in Probability and Statistics: Read More [+]

STAT C261 Quantitative/Statistical Research Methods in Social Sciences 3 Units

Terms offered: Spring 2016, Spring 2015, Spring 2014
Selected topics in quantitative/statistical methods of research in the social sciences and particularly in sociology. 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.
Quantitative/Statistical Research Methods in Social Sciences: Read More [+]

STAT 272 Statistical Consulting 3 Units

Terms offered: Fall 2018, Spring 2018, Fall 2017
To be taken concurrently with service as a consultant in the department's drop-in consulting service. Participants will work on problems arising in the service and will discuss general ways of handling such problems. There will be working sessions with researchers in substantive fields and occasional lectures on consulting.
Statistical Consulting: Read More [+]

STAT 278B Statistics Research Seminar 1 - 4 Units

Terms offered: Fall 2018, Spring 2018, Fall 2017
Special topics, by means of lectures and informational conferences.
Statistics Research Seminar: Read More [+]

STAT 298 Directed Study for Graduate Students 1 - 12 Units

Terms offered: Fall 2018, Spring 2018, Fall 2017
Special tutorial or seminar on selected topics.
Directed Study for Graduate Students: Read More [+]

STAT 299 Individual Study Leading to Higher Degrees 1 - 12 Units

Terms offered: Fall 2018, Spring 2018, Fall 2017
Individual study
Individual Study Leading to Higher Degrees: Read More [+]

STAT 375 Professional Preparation: Teaching of Probability and Statistics 2 - 4 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016
Discussion, problem review and development, guidance of laboratory classes, course development, supervised practice teaching.
Professional Preparation: Teaching of Probability and Statistics: Read More [+]

STAT 601 Individual Study for Master's Candidates 1 - 8 Units

Terms offered: Fall 2018, Summer 2018 8 Week Session, Spring 2018
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. Units may not be used to meet either unit or residence requirements for a master's degree.
Individual Study for Master's Candidates: Read More [+]

STAT 602 Individual Study for Doctoral Candidates 1 - 8 Units

Terms offered: Fall 2018, Spring 2018, Fall 2017
Individual study in consultation with the graduate adviser, intended to provide an opportunity for qualified students to prepare themselves for certain examinations required of candidates for the Ph.D. degree.
Individual Study for Doctoral Candidates: Read More [+]

STAT 700 Statistics Colloquium 0.0 Units

Terms offered: Prior to 2007
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.
Statistics Colloquium: Read More [+]

Faculty and Instructors

+ Indicates this faculty member is the recipient of the Distinguished Teaching Award.

Faculty

David Aldous, Professor. Mathematical probability, applied probability, analysis of algorithms, phylogenetic trees, complex networks, random networks, entropy, spatial networks.
Research Profile

Peter L. Bartlett, Professor. Statistics, machine learning, statistical learning theory, adaptive control.
Research Profile

David R. Brillinger, Professor. Risk analysis, statistical methods, data analysis, animal and fish motion trajectories, statistical applications in engineering and science, sports statistics.
Research Profile

James Bentley Brown, Assistant Adjunct Professor.

Joan Bruna Estrach, Assistant Professor.
Research Profile

Peng Ding, Assistant Professor.
Research Profile

Sandrine Dudoit, Professor. Genomics, classification, statistical computing, biostatistics, cross-validation, density estimation, genetic mapping, high-throughput sequencing, loss-based estimation, microarray, model selection, multiple hypothesis testing, prediction, RNA-Seq.
Research Profile

Noureddine El Karoui, Associate Professor. Applied statistics, theory and applications of random matrices, large dimensional covariance estimation and properties of covariance matrices, connections with mathematical finance.
Research Profile

Steven N. Evans, Professor. Genetics, random matrices, superprocesses & other measure-valued processes, probability on algebraic structures -particularly local fields, applications of stochastic processes to biodemography, mathematical finance, phylogenetics & historical linguistics.
Research Profile

Will Fithian, Assistant Professor.
Research Profile

Lisa Goldberg, Adjunct Professor.

Adityanand Guntuboyina, Assistant Professor.

Alan Hammond, Associate Professor.

Haiyan Huang, Associate Professor. Applied statistics, functional genomics, translational bioinformatics, high dimensional and integrative genomic/genetic data analysis, network modeling, hierarchical multi-lable classification.
Research Profile

Nicholas P. Jewell, Professor. AIDS, statistics, epidemiology, infectious diseases, Ebola Virus Disease, SARS, H1N1 influenza, adverse cardiovascular effects of pharmaceuticals, counting civilian casualties during conflicts.
Research Profile

Michael I. Jordan, Professor. Computer science, artificial intelligence, bioinformatics, statistics, machine learning, electrical engineering, applied statistics, optimization.
Research Profile

Michael J. Klass, Professor. Statistics, mathematics, probability theory, combinatorics independent random variables, iterated logarithm, tail probabilities, functions of sums.
Research Profile

Michael William Mahoney, Associate Adjunct Professor.

Jon Mcauliffe, Associate Adjunct Professor. Bioinformatics, machine learning, nonparametrics, convex optimization, statistical computing, prediction, supervised learning.
Research Profile

Elchanan Mossel, Professor. Applied probability, statistics, mathematics, finite markov chains, markov random fields, phlylogeny.
Research Profile

Rasmus Nielsen, Professor. Statistical and computational aspects of evolutionary theory and genetics.
Research Profile

+ Deborah Nolan, Professor. Statistics, empirical process, high-dimensional modeling, technology in education.
Research Profile

James W. Pitman, Professor. Fragmentation, statistics, mathematics, Brownian motion, distribution theory, path transformations, stochastic processes, local time, excursions, random trees, random partitions, processes of coalescence.
Research Profile

Elizabeth Purdom, Assistant Professor. Computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics.
Research Profile

Benjamin Recht, Associate Professor.

Jasjeet S. Sekhon, Professor. Program evaluation, statistical and computational methods, causal inference, elections, public opinion, American politics .

Alistair Sinclair, Professor. Algorithms, applied probability, statistics, random walks, Markov chains, computational applications of randomness, Markov chain Monte Carlo, statistical physics, combinatorial optimization.
Research Profile

Allan M. Sly, Associate Professor.
Research Profile

Yun Song, Associate Professor. Computational biology, population genomics, applied probability and statistics.
Research Profile

Philip B. Stark, Professor. Astrophysics, law, statistics, litigation, causal inference, inverse problems, geophysics, elections, uncertainty quantification, educational technology.
Research Profile

Bernd Sturmfels, Professor. Mathematics, combinatorics, computational algebraic geometry.
Research Profile

Nike Sun, Assistant Professor.
Research Profile

Mark J. Van Der Laan, Professor. Statistics, computational biology and genomics, censored data and survival analysis, medical research, inference in longitudinal studies.
Research Profile

Martin Wainwright, Professor. Statistical machine learning, High-dimensional statistics, information theory, Optimization and algorithmss.
Research Profile

Bin Yu, Professor. Neuroscience, remote sensing, networks, statistical machine learning, high-dimensional inference, massive data problems, document summarization.
Research Profile

Lecturers

+ Ani Adhikari, Senior Lecturer SOE.

Fletcher H. Ibser, Lecturer.

Adam R. Lucas, Lecturer.

Christopher Paciorek, Lecturer.

Nusrat Rabbee, Lecturer.

Gaston Sanchez Trujillo, Lecturer.

Shobhana Stoyanov, Lecturer.

Visiting Faculty

Hermann Helmut Pitters, Visiting Assistant Professor.

Yuekai Sun, Visiting Assistant Professor.

Emeritus Faculty

Peter J. Bickel, Professor Emeritus. Statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology.
Research Profile

Ching-Shui Cheng, Professor Emeritus. Statistics, statistical design of experiments, combinatorial problems, efficient experimental design.
Research Profile

Kjell A. Doksum, Professor Emeritus. Statistics, curve estimation, nonparametric regression, correlation curves, survival analysis, semiparametric, nonparametric settings, regression quantiles, analysis of financial data.
Research Profile

Leo Goodman, Professor Emeritus. Sociology, statistics, log-linear models, correspondence analysis models, mathematical demography, categorical data analysis, survey data analysis, logit models, log-bilinear models, association models.
Research Profile

Pressley W. Millar, Professor Emeritus. Statistics, Martingales, Markov processes, Gaussian processes, excursion theory, asymptotic statistical decision theory, nonparametrics, robustness, stochastic procedures, asymptotic minimas theory, bootstrap theory.
Research Profile

Roger A. Purves, Professor Emeritus. Statistics, foundations of probability, measurability.
Research Profile

John A. Rice, Professor Emeritus. Transportation, astronomy, statistics, functional data analysis, time series analysis.
Research Profile

Terence P. Speed, Professor Emeritus. Genomics, statistics, genetics and molecular biology, protein sequences.
Research Profile

Charles J. Stone, Professor Emeritus. Statistical modeling with splines, statistical education.
Research Profile

Kenneth Wachter, Professor Emeritus. Mathematical demography stochastic models, simulation, biodemography, federal statistical system.
Research Profile

Contact Information

Department of Statistics

367 Evans Hall

Phone: 510-642-2781

Fax: 510-641-7892

Visit Department Website

Department Chair

Deborah Nolan, PhD

395 Evans Hall

chair@stat.berkeley.edu

MA Program Committee Chair

Adityanand Guntoboyina, PhD

423 Evans Hall

agun@berkeley.edu

PhD Program Committee Chair

Steven Evans, PhD

329 Evans Hall

evans@stat.berkeley.edu

PhD Program Coordinator

La Shana Porlaris

373 Evans Hall

Phone: 510-642-5361

lashana@berkeley.edu

Master's Program Coordinator and Assistant Director of Industry Relations

Erin Blanton

375 Evans Hall

Phone: 510-643-0589

eblanton@berkeley.edu

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