This is an archived copy of the 2019-20 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 the 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 STAT 272, 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, we also have two joint seminars with Stanford and a joint seminar with Davis.
Undergraduate Programs
Statistics: BA, Minor
Graduate Programs
Statistics: MA, PhD
Courses
Statistics
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 [+]
Rules & Requirements
Prerequisites: Consent of instructor
Hours & Format
Summer:
6 weeks - 5 hours of lecture and 4.5 hours of workshop per week
8 weeks - 5 hours of lecture and 4.5 hours of workshop per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Offered for pass/not pass grade only. Final exam required.
Instructor: Purves
Terms offered: Fall 2020, Summer 2020 8 Week Session, Summer 2020 Second 6 Week Session
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 [+]
Rules & Requirements
Credit Restrictions: Students who have taken 2X, 5, 20, 21, 21X, or 25 will receive no credit for 2.
Hours & Format
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 week
8 weeks - 5 hours of lecture and 4 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Not yet offered
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.
Introduction to Computational Thinking with Data: Read More [+]
Objectives & Outcomes
Course Objectives: C6 also includes quantitative reasoning concepts that aren’t covered in Data 8. These include certain topics in: principles of data visualization; simulation of random processes; and understanding numerical functions through their graphs. This will help prepare students for computational and quantitative courses other than Data 8.
C6 takes advantage of the complementarity of computing and quantitative reasoning to enliven abstract ideas and build students’ confidence in their ability to solve real problems with quantitative tools. Students learn computer science concepts and immediately apply them to plot functions, visualize data, and simulate random events.
Foundations of Data Science (CS/Info/Stat C8, a.k.a. Data 8) is an increasingly popular class for entering students at Berkeley. Data 8 builds students’ computing skills in the first month of the semester, and students rely on these skills as the course progresses. For some students, particularly those with little prior exposure to computing, developing these skills benefits from further time and practice. C6 is a rapid introduction to Python programming, visualization, and data analysis, which will prepare students for success in Data 8.
Student Learning Outcomes: Students will be able to perform basic computations in Python, including working with tabular data.
Students will be able to understand basic probabilistic simulations.
Students will be able to understand the syntactic structure of Python code.
Students will be able to use good practices in Python programming.
Students will be able to use visualizations to understand univariate data and to identify associations or causal relationships in bivariate data.
Rules & Requirements
Credit Restrictions: Students will receive no credit for DATA C6\COMPSCI C6\STAT C6 after completing DATA C8, or DATA 6. A deficient grade in DATA C6\COMPSCI C6\STAT C6 may be removed by taking DATA 6.
Hours & Format
Summer: 6 weeks - 4 hours of lecture, 2 hours of discussion, and 4 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Formerly known as: Computer Science C8R/Statistics C8R
Also listed as: COMPSCI C6/DATA C6
Introduction to Computational Thinking with Data: Read Less [-]
Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020, Fall 2019, Spring 2019
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.
Foundations of Data Science: Read More [+]
Rules & Requirements
Prerequisites: This course may be taken on its own, but students are encouraged to take it concurrently with a data science connector course (numbered 88 in a range of departments)
Credit Restrictions: Students will receive no credit for DATA C8\COMPSCI C8\INFO C8\STAT C8 after completing COMPSCI 8, or DATA 8. A deficient grade in DATA C8\COMPSCI C8\INFO C8\STAT C8 may be removed by taking COMPSCI 8, COMPSCI 8, or DATA 8.
Hours & Format
Fall and/or spring: 15 weeks - 3-3 hours of lecture and 2-2 hours of laboratory per week
Summer: 8 weeks - 6 hours of lecture and 4 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Formerly known as: Computer Science C8/Statistics C8/Information C8
Also listed as: COMPSCI C8/DATA C8/INFO C8
Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020
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 [+]
Rules & Requirements
Prerequisites: One semester of calculus
Credit Restrictions: Students who have taken 2, 2X, 5, 21, 21X, or 25 will receive no credit for 20.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Summer: 8 weeks - 6 hours of lecture and 3 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
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.
Introductory Probability and Statistics for Business: Read More [+]
Rules & Requirements
Prerequisites: One semester of calculus
Credit Restrictions: Students will receive no credit for Statistics 21 after completing Statistics 2, 2X, 5, 20, 21X, N21, W21 or 25 . A deficiency in Statistics 21 may be moved by taking W21.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Summer: 8 weeks - 5 hours of lecture and 4 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Introductory Probability and Statistics for Business: Read Less [-]
Terms offered: Summer 2020 8 Week Session, Summer 2019 8 Week Session, Spring 2019
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 [+]
Rules & Requirements
Prerequisites: One semester of calculus
Credit Restrictions: Students will receive no credit for Statistics W21 after completing Statistics 2, 20, 21, N21 or 25. A deficient grade in Statistics 21, N21 maybe removed by taking Statistics W21.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of web-based lecture per week
Summer: 8 weeks - 7.5 hours of web-based lecture per week
Online: This is an online course.
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Formerly known as: N21
Introductory Probability and Statistics for Business: Read Less [-]
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 [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit when topic changes.
Hours & Format
Fall and/or spring: 15 weeks - 1 hour of seminar per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.
Terms offered: Fall 2020, Spring 2020, Fall 2019
An introduction to the R statistical software for students with minimal prior experience with programming. 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. 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. Tools for reading, analyzing, and plotting data are covered, such as data input/output, reshaping data, the formula language, and graphics models.
Introduction to Programming in R: Read More [+]
Rules & Requirements
Credit Restrictions: Students will receive no credit for STAT 33A after completing STAT 33B, or STAT 133. A deficient grade in STAT 33A may be removed by taking STAT 33B, or STAT 133.
Hours & Format
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
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Spring 2020, Fall 2019
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 focus is on the underlying paradigms in R, such as functional programming, atomic vectors, complex data structures, environments, and object systems. 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.
Introduction to Advanced Programming in R: Read More [+]
Rules & Requirements
Prerequisites: Compsci 61A or equivalent programming background
Credit Restrictions: Students will receive no credit for STAT 33B after completing STAT 133. A deficient grade in STAT 33B may be removed by taking STAT 133.
Hours & Format
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
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
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 [+]
Rules & Requirements
Prerequisites: Priority given to freshmen and sophomores
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 2-4 hours of seminar per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam not required.
Also listed as: COMPSCI C79/POL SCI C79
Terms offered: Fall 2020, Spring 2020, Fall 2019
In this connector course we will state precisely and prove results discovered while exploring data in Data 8. 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.
Probability and Mathematical Statistics in Data Science: Read More [+]
Rules & Requirements
Prerequisites: Prerequisite: one semester of calculus at the level of Math 16A, Math 10A, or Math 1A. Corequisite or Prerequisite: Foundations of Data Science (COMPSCI C8 / DATASCI C8 / INFO C8 / STAT C8)
Credit Restrictions: Students will receive no credit for STAT 88 after completing STAT 134, STAT 140, STAT 135, or STAT 102.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Probability and Mathematical Statistics in Data Science: Read Less [-]
Terms offered: Spring 2020, Spring 2019, Spring 2018
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 [+]
Rules & Requirements
Prerequisites: One year of calculus. Prerequisite or corequisite: Foundations of Data Science (COMPSCI C8 / INFO C8 / STAT C8)
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2015
Topics will vary semester to semester.
Special Topics in Probability and Statistics: Read More [+]
Rules & Requirements
Prerequisites: Consent of instructor
Repeat rules: Course may be repeated for credit when topic changes.
Hours & Format
Fall and/or spring: 15 weeks - 1-3 hours of lecture and 0-2 hours of discussion per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
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 [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 1-3 hours of fieldwork per week
Summer:
6 weeks - 2.5-7.5 hours of fieldwork per week
8 weeks - 1.5-5.5 hours of fieldwork per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.
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 [+]
Rules & Requirements
Prerequisites: Consent of instructor
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
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
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.
Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020, Fall 2019
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 [+]
Rules & Requirements
Prerequisites: COMPSCI C8 / DATA C8 / INFO C8 / STAT C8; and COMPSCI 61A, COMPSCI 88, or ENGIN 7; Corequisite: MATH 54 or EECS 16A
Credit Restrictions: Students will receive no credit for DATA C100\STAT C100\COMPSCI C100 after completing DATA 100. A deficient grade in DATA C100\STAT C100\COMPSCI C100 may be removed by taking DATA 100.
Hours & Format
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
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Formerly known as: Statistics C100/Computer Science C100
Also listed as: COMPSCI C100/DATA C100
Terms offered: Fall 2020
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. 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.
Data, Inference, and Decisions: Read More [+]
Rules & Requirements
Prerequisites: Mathematics 54 or Mathematics 110 or Statistics 89A or Physics 89 or both of Electrical Engineering and Computer Science 16A and Electrical Engineering and Computer Science 16B; Statistics/Computer Science C100; and any of Electrical Engineering and Computer Science 126, Statistics 140, Statistics 134, Industrial Engineering and Operations Research 172. Statistics 140 or Electrical Engineering and Computer Science 126 are preferred
Credit Restrictions: Students will receive no credit for DATA C102 after completing STAT 102, or DATA 102. A deficient grade in DATA C102 may be removed by taking STAT 102, STAT 102, or DATA 102.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 1 hour of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Formerly known as: Statistics 102
Also listed as: DATA C102
Terms offered: Fall 2020, Spring 2020, Fall 2019
This course teaches a broad range of statistical methods that are used to solve data problems. 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. An important focus of the course is on statistical computing and reproducible statistical analysis. The course and lab include hands-on experience in analyzing real world data from the social, life, and physical sciences. The R statistical language is used.
Statistical Methods for Data Science: Read More [+]
Rules & Requirements
Prerequisites: Statistics/Computer Science/Information C8 or Statistics 20; and Mathematics 1A, Mathematics 16A, or Mathematics 10A/10B. Strongly recommended corequisite: Statistics 33A or Statistics 133
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Spring 2020, Fall 2019
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.
Concepts in Computing with Data: Read More [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Summer: 10 weeks - 4 hours of lecture and 3 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020
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.
Concepts of Probability: Read More [+]
Rules & Requirements
Prerequisites: One year of calculus
Credit Restrictions: Students will not receive credit for 134 after taking 140 or 201A.
Hours & Format
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
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020
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 [+]
Rules & Requirements
Prerequisites: STAT 134 or STAT 140; and MATH 54, EL ENG 16A, STAT 89A, MATH 110 or equivalent linear algebra. Strongly recommended corerequisite: STAT 133
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Summer: 8 weeks - 6 hours of lecture and 4 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Spring 2020, Fall 2019
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 [+]
Objectives & Outcomes
Course Objectives: The emphasis on simulation and the bootstrap in Data 8 gives students a concrete sense of randomness and sampling variability. Stat 140 will capitalize on this, abstraction and computation complementing each other throughout.
The syllabus has been designed to maintain a mathematical level at least equal to that in Stat 134. So Stat 140 will start faster than Stat 134 (due to the Data 8 prerequisite), avoid approximations that are unnecessary when SciPy is at hand, and replace some of the routine calculus by symbolic math done in SymPy. 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. Data 8), there is considerable demand for follow-on courses that build on the skills acquired in that class. 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.
Student Learning Outcomes: Understand the difference between math and simulation, and appreciate the power of both
Use a variety of approaches to problem solving
Work with probability concepts algebraically, numerically, and graphically
Rules & Requirements
Prerequisites: Statistics/Computer Science/Information C8, or Statistics/Computer Science C100, or both Stat 20 and Computer Science 61A; and one year of calculus at the level of Mathematics 1A-1B or higher. Corequisite: Mathematics 54, Electrical Engineering 16A, Statistics 89A, Mathematics 110 or equivalent linear algebra
Credit Restrictions: Students who have earned credit for Stat 134 will not receive credit for Stat 140.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture, 2 hours of discussion, and 1 hour of supplement per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Spring 2020, Fall 2019
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 [+]
Rules & Requirements
Prerequisites: 101 or 103A or 134
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Spring 2020, Fall 2019
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Spring 2020, Spring 2019, Spring 2018
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 [+]
Rules & Requirements
Prerequisites: 101 or 134. 133 and 135 recommended
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Spring 2020, Fall 2019
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 [+]
Rules & Requirements
Prerequisites: 101, 134 or consent of instructor. 133 or 135 recommended
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Spring 2020, Fall 2019
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.
Modern Statistical Prediction and Machine Learning: Read More [+]
Rules & Requirements
Prerequisites: Mathematics 53 or equivalent; Mathematics 54, Electrical Engineering 16A, Statistics 89A, Mathematics 110 or equivalent linear algebra; Statistics 135 or equivalent; experience with some programming language. Recommended prerequisite: Mathematics 55 or equivalent exposure to counting arguments
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Summer: 10 weeks - 4.5 hours of lecture and 3 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Modern Statistical Prediction and Machine Learning: Read Less [-]
Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020
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 [+]
Rules & Requirements
Prerequisites: 101 or 134
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Summer: 8 weeks - 6 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Fall 2000, Spring 1997
This course will focus on approaches to causal inference using the potential outcomes framework. It will also use causal diagrams at an intuitive level. The main topics are classical randomized experiments, observational studies, instrumental variables, principal stratification and mediation analysis. Applications are drawn from a variety of fields including political science, economics, sociology, public health, and medicine. This course is a mix of statistical theory and data analysis. Students will be exposed to statistical questions that are relevant to decision and policy making.
Causal Inference: Read More [+]
Rules & Requirements
Prerequisites: Statistics 135
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Spring 2020, Fall 2019
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 [+]
Rules & Requirements
Prerequisites: Mathematics 53-54, Statistics 134, 135. Knowledge of scientific computing environment (R or Matlab) often required. Prerequisites might vary with instructor and topics
Repeat rules: Course may be repeated for credit with instructor consent.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of seminar per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Seminar on Topics in Probability and Statistics: Read Less [-]
Terms offered: Spring 2020, Spring 2019, Spring 2018
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 [+]
Rules & Requirements
Prerequisites: Statistics 134 and 135 or consent of instructor. Statistics 135 may be taken concurrently. Statistics 133 is recommended
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
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 [+]
Rules & Requirements
Prerequisites: Statistics 133, Statistics 134, and Statistics 135 (or equivalent)
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Reproducible and Collaborative Statistical Data Science: Read Less [-]
Terms offered: Spring 2015, Fall 2014, Fall 2010
Special Study for Honors Candidates: Read More [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 0 hours of independent study per week
Summer:
6 weeks - 1-5 hours of independent study per week
8 weeks - 1-4 hours of independent study per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Letter grade. Final exam not required.
Terms offered: Spring 2017, Fall 2015, Summer 2015 10 Week Session
Supervised experience relevant to specific aspects of statistics in on-campus or off-campus settings. Individual and/or group meetings with faculty.
Field Study in Statistics: Read More [+]
Rules & Requirements
Credit Restrictions: Enrollment is restricted; see the Introduction to Courses and Curricula section of this catalog.
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 2-9 hours of fieldwork per week
Summer:
6 weeks - 3-22 hours of fieldwork per week
8 weeks - 2-16 hours of fieldwork per week
10 weeks - 2-12 hours of fieldwork per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.
Terms offered: Fall 2020, Spring 2018, Spring 2016
Special tutorial or seminar on selected topics.
Directed Study for Undergraduates: Read More [+]
Rules & Requirements
Prerequisites: Consent of instructor
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 1-3 hours of directed group study per week
Summer:
6 weeks - 2.5-7.5 hours of directed group study per week
8 weeks - 1.5-5.5 hours of directed group study per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.
Terms offered: Fall 2019, Fall 2018, Spring 2017
Supervised Independent Study and Research: Read More [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 1-3 hours of independent study per week
Summer:
6 weeks - 1-4 hours of independent study per week
8 weeks - 1-3 hours of independent study per week
10 weeks - 1-3 hours of independent study per week
Additional Details
Subject/Course Level: Statistics/Undergraduate
Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.
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 [+]
Rules & Requirements
Prerequisites: Multivariable calculus and one semester of linear algebra
Credit Restrictions: Students will receive no credit for Statistics 200A after completing Statistics 201A-201B.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Introduction to Probability and Statistics at an Advanced Level: Read Less [-]
Terms offered: Spring 2019, Spring 2012, Spring 2011
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 [+]
Rules & Requirements
Prerequisites: Multivariable calculus and one semester of linear algebra
Credit Restrictions: Students will receive no credit for Statistics 200A-200B after completing Statistics 201A-201B.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Introduction to Probability and Statistics at an Advanced Level: Read Less [-]
Terms offered: Fall 2020, Spring 2020, Fall 2019, Spring 2019
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 [+]
Rules & Requirements
Prerequisites: Computer Science/Information/Statistics C8 or Engineering 7; and either Computer Science 61A or Computer Science 88. Corequisite: Mathematics 54 or Electrical Engineering 16A
Credit Restrictions: Students will receive no credit for STAT C200C\COMPSCI C200A\DATA C200 after completing DATA C100, or STAT 200C. A deficient grade in STAT C200C\COMPSCI C200A\DATA C200 may be removed by taking STAT 200C.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 1 hour of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Formerly known as: Statistics C200C/Computer Science C200A
Also listed as: COMPSCI C200A/DATA C200
Terms offered: Fall 2020, Fall 2019, Fall 2018
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 [+]
Rules & Requirements
Prerequisites: Undergraduate probability at the level of Statistics 134, multivariable calculus (at the level of Berkeley’s Mathematics 53) and linear algebra (at the level of Berkeley’s Mathematics 54)
Credit Restrictions: Students will receive no credit for STAT 201A after completing STAT 200A.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Introduction to Probability at an Advanced Level: Read Less [-]
Terms offered: Fall 2020, Fall 2019, Fall 2018
Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.
Introduction to Statistics at an Advanced Level: Read More [+]
Rules & Requirements
Prerequisites: Undergraduate probability at the level of Statistics 134, multivariable calculus (at the level of Berkeley’s Mathematics 53) and linear algebra (at the level of Berkeley’s Mathematics 54)
Credit Restrictions: Students will receive no credit for Statistics 201B after completing Statistics 200B.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Introduction to Statistics at an Advanced Level: Read Less [-]
Terms offered: Fall 2019, Spring 2017, Spring 2015
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 [+]
Rules & Requirements
Credit Restrictions: Students will receive no credit for Statistics 204 after completing Statistics 205A-205B.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Instructor: Evans
Terms offered: Fall 2020, Fall 2019, Fall 2018, Fall 2017
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Also listed as: MATH C218A
Terms offered: Spring 2020, Spring 2019, Spring 2018
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Also listed as: MATH C218B
Terms offered: Fall 2020, Fall 2016, Fall 2015, Fall 2014
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 Process: Read More [+]
Rules & Requirements
Prerequisites: Statistics C205A-C205B or consent of instructor
Repeat rules: Course may be repeated for credit with instructor consent.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Also listed as: MATH C223A
Advanced Topics in Probability and Stochastic Process: Read Less [-]
Terms offered: Spring 2020, Spring 2019, Spring 2018
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 [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit with instructor consent.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Also listed as: MATH C223B
Advanced Topics in Probability and Stochastic Processes: Read Less [-]
Terms offered: Fall 2020, Fall 2019, Fall 2018
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 [+]
Rules & Requirements
Prerequisites: Linear algebra, real analysis, and a year of upper division probability and statistics
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Terms offered: Spring 2020, Spring 2019, Spring 2018
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 [+]
Rules & Requirements
Prerequisites: Statistics 210A and a graduate level probability course; a good understanding of various notions of stochastic convergence
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
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.
Topics in Theoretical Statistics: Read More [+]
Rules & Requirements
Prerequisites: 210 or 205 and 215
Repeat rules: Course may be repeated for credit with instructor consent.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Formerly known as: 216A-216B and 217A-217B
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 [+]
Rules & Requirements
Prerequisites: 210 or 205 and 215
Repeat rules: Course may be repeated for credit with instructor consent.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Formerly known as: 216A-216B and 217A-217B
Terms offered: Fall 2020, Fall 2019, Fall 2018
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).
Statistical Models: Theory and Application: Read More [+]
Rules & Requirements
Prerequisites: Linear algebra, calculus, upper division probability and statistics, and familiarity with high-level programming languages. Statistics 133, 134, and 135 recommended
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Terms offered: Spring 2020, Spring 2019, Spring 2018
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 [+]
Rules & Requirements
Prerequisites: Statistics 215A or consent of instructor
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Terms offered: Spring 2020, Spring 2019, Spring 2018
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.
Masters of Statistics Capstone Project: Read More [+]
Rules & Requirements
Prerequisites: Statistics 201A-201B, 243. Restricted to students who have been admitted to the one-year Masters Program in Statistics beginning fall 2012 or later
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of seminar and 1 hour of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Terms offered: Spring 2020, Spring 2019, Spring 2018
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 [+]
Rules & Requirements
Prerequisites: Matrix algebra, a year of calculus, two semesters of upper division or graduate probability and statistics
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
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 [+]
Rules & Requirements
Prerequisites: 200B or equivalent
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
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 [+]
Objectives & Outcomes
Course Objectives: develop Bayesian models for new types of data
implement Bayesian models and interpret the results
read and discuss Bayesian methods in the literature
select and build appropriate Bayesian models for data to answer research questions
understand and describe the Bayesian perspective and its advantages and disadvantages compared to classical methods
Rules & Requirements
Prerequisites: Probability and mathematical statistics at the level of Stat 134 and Stat 135 or, ideally, Stat 201A and Stat 201B
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
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 [+]
Rules & Requirements
Prerequisites: At least one graduate matrix based multivariate regression course in addition to introductory statistics and probability
Hours & Format
Fall and/or spring: 15 weeks - 3-3 hours of lecture and 1-2 hours of discussion per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade. This is part one of a year long series course. A provisional grade of IP (in progress) will be applied and later replaced with the final grade after completing part two of the series.
Instructor: Sekhon
The Statistics of Causal Inference in the Social Science: Read Less [-]
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 [+]
Rules & Requirements
Prerequisites: Statistics 239A or equivalent
Hours & Format
Fall and/or spring: 15 weeks - 3-3 hours of lecture and 1-2 hours of discussion per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade. This is part two of a year long series course. Upon completion, the final grade will be applied to both parts of the series.
Quantitative Methodology in the Social Sciences Seminar: Read Less [-]
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Also listed as: POL SCI C236A
The Statistics of Causal Inference in the Social Science: Read Less [-]
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Also listed as: POL SCI C236B
Quantitative Methodology in the Social Sciences Seminar: Read Less [-]
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.
Nonparametric and Robust Methods: Read More [+]
Rules & Requirements
Prerequisites: A year of upper division probability and statistics
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Terms offered: Fall 2020, Fall 2019, Fall 2016
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Instructors: Bartlett, Jordan, Wainwright
Also listed as: COMPSCI C281A
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.
Advanced Topics in Learning and Decision Making: Read More [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Instructors: Bartlett, Jordan, Wainwright
Also listed as: COMPSCI C281B
Advanced Topics in Learning and Decision Making: Read Less [-]
Terms offered: Fall 2020, Fall 2019, Fall 2018
Concepts in statistical programming and statistical computation, including programming principles, data and text manipulation, parallel processing, simulation, numerical linear algebra, and optimization.
Introduction to Statistical Computing: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Become familiar with concepts and tools for reproducible research and good scientific computing practices.
Operate effectively in a UNIX environment and on remote servers.
Program effectively in languages including R and Python with an advanced knowledge of language functionality and an understanding of general programming concepts.
Understand in depth and make use of principles of numerical linear algebra, optimization, and simulation for statistics-related research.
Rules & Requirements
Prerequisites: Graduate standing
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
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 [+]
Rules & Requirements
Prerequisites: Knowledge of a higher level programming language
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Terms offered: Fall 2019, Spring 2019, Spring 2018
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
Introduction to Modern Biostatistical Theory and Practice: Read More [+]
Rules & Requirements
Prerequisites: Statistics 200A (may be taken concurrently)
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Instructor: Hubbard
Also listed as: PB HLTH C240A
Introduction to Modern Biostatistical Theory and Practice: Read Less [-]
Terms offered: Fall 2020, Fall 2019, Fall 2017
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 [+]
Rules & Requirements
Prerequisites: Statistics 200B (may be taken concurrently)
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Instructor: van der Laan
Also listed as: PB HLTH C240B
Biostatistical Methods: Survival Analysis and Causality: Read Less [-]
Terms offered: Fall 2020, Fall 2018, Fall 2016
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.
Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine: Read More [+]
Rules & Requirements
Prerequisites: Statistics 200A or equivalent (may be taken concurrently)
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Instructor: Dudoit
Also listed as: PB HLTH C240C
Terms offered: Fall 2017, Fall 2015, Fall 2013
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 [+]
Rules & Requirements
Prerequisites: Statistics 200A-200B or Statistics 201A-201B (may be taken concurrently) or consent of instructor
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Instructor: Dudoit
Also listed as: PB HLTH C240D
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 [+]
Rules & Requirements
Prerequisites: Statistics 200A and 200B or equivalent (may be taken concurrently). A course in algorithms and knowledge of at least one computing language (e.g., R, matlab) is recommended
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Instructors: Dudoit, Huang, Nielsen, Song
Also listed as: PB HLTH C240E
Terms offered: Spring 2020, Spring 2018, Spring 2017, Spring 2016
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Instructors: Dudoit, Huang, Nielsen, Song
Also listed as: PB HLTH C240F
Terms offered: Fall 2019, Fall 2018, Fall 2017
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 [+]
Rules & Requirements
Prerequisites: 142, 145, 241 or equivalent courses in basic statistics, linear and logistic regression
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Instructors: Hubbard, Jewell
Also listed as: PB HLTH C242C
Terms offered: Spring 2020, Spring 2019, Spring 2018
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 [+]
Rules & Requirements
Prerequisites: 102 or equivalent
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Terms offered: Fall 2020
This course will focus on approaches to causal inference using the potential outcomes framework. It will also use causal diagrams at an intuitive level. The main topics are classical randomized experiments, observational studies, instrumental variables, principal stratification and mediation analysis. Applications are drawn from a variety of fields including political science, economics, sociology, public health, and medicine. This course is a mix of statistical theory and data analysis. Students will be exposed to statistical questions that are relevant to decision and policy making.
Causal Inference: Read More [+]
Rules & Requirements
Prerequisites: Statistics 201B or Statistics 210A
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
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 [+]
Rules & Requirements
Prerequisites: Statistics 133, Statistics 134, and Statistics 135 (or equivalent)
Credit Restrictions: Students will receive no credit for Statistics 259 after taking Statistics 159.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Reproducible and Collaborative Statistical Data Science: Read Less [-]
Terms offered: Fall 2020, Spring 2020, Fall 2019
Special topics in probability and statistics offered according to student demand and faculty availability.
Topics in Probability and Statistics: Read More [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
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 [+]
Rules & Requirements
Prerequisites: Consent of instructor
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Also listed as: SOCIOL C271D
Quantitative/Statistical Research Methods in Social Sciences: Read Less [-]
Terms offered: Fall 2020, Spring 2020, Fall 2019
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 [+]
Rules & Requirements
Prerequisites: Some course work in applied statistics and permission of instructor
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of session per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Offered for satisfactory/unsatisfactory grade only.
Terms offered: Fall 2020, Spring 2020, Fall 2019
Special topics, by means of lectures and informational conferences.
Statistics Research Seminar: Read More [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 0 hours of seminar per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Offered for satisfactory/unsatisfactory grade only.
Terms offered: Fall 2020, Spring 2020, Fall 2019
Special tutorial or seminar on selected topics.
Directed Study for Graduate Students: Read More [+]
Rules & Requirements
Prerequisites: Consent of instructor
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 0 hours of independent study per week
Summer:
6 weeks - 1-16 hours of independent study per week
8 weeks - 1-12 hours of independent study per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Terms offered: Fall 2020, Spring 2020, Fall 2019
Individual study
Individual Study Leading to Higher Degrees: Read More [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 2-36 hours of independent study per week
Summer:
6 weeks - 4-45 hours of independent study per week
8 weeks - 3-36 hours of independent study per week
10 weeks - 2.5-27 hours of independent study per week
Additional Details
Subject/Course Level: Statistics/Graduate
Grading: Letter grade.
Terms offered: Fall 2020, Spring 2020, Fall 2019
Discussion, problem review and development, guidance of laboratory classes, course development, supervised practice teaching.
Professional Preparation: Teaching of Probability and Statistics: Read More [+]
Rules & Requirements
Prerequisites: Graduate standing and appointment as a graduate student instructor
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 4 hours of laboratory per week
Additional Details
Subject/Course Level: Statistics/Professional course for teachers or prospective teachers
Grading: Offered for satisfactory/unsatisfactory grade only.
Formerly known as: Statistics 300
Professional Preparation: Teaching of Probability and Statistics: Read Less [-]
Terms offered: Fall 2020, Spring 2020, Fall 2019
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 [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit up to a total of 16 units.
Hours & Format
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 week
8 weeks - 1-15 hours of independent study per week
10 weeks - 1-12 hours of independent study per week
Additional Details
Subject/Course Level: Statistics/Graduate examination preparation
Grading: Offered for satisfactory/unsatisfactory grade only.
Terms offered: Fall 2020, Spring 2020, Fall 2019
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 [+]
Rules & Requirements
Prerequisites: One year of full-time graduate study and permission of the graduate adviser
Credit Restrictions: Course does not satisfy unit or residence requirements for doctoral degree.
Repeat rules: Course may be repeated for credit up to a total of 16 units.
Hours & Format
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 week
8 weeks - 1-15 hours of independent study per week
10 weeks - 1-12 hours of independent study per week
Additional Details
Subject/Course Level: Statistics/Graduate examination preparation
Grading: Offered for satisfactory/unsatisfactory grade only.
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 [+]
Hours & Format
Fall and/or spring: 15 weeks - 1-2 hours of colloquium per week
Additional Details
Subject/Course Level: Statistics/Graduate examination preparation
Grading: The grading option will be decided by the instructor when the class is offered.
Formerly known as: Statistics 999
Faculty and Instructors
+ Indicates this faculty member is the recipient of the Distinguished Teaching Award.
Faculty
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.
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, 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 and other measure-valued processes, probability on algebraic structures -particularly local fields, applications of stochastic processes to biodemography, mathematical finance, phylogenetics and historical linguistics.
Research Profile
Avi Feller, Assistant Professor. Program Evaluation, Quantitative Methods.
Will Fithian, Assistant Professor.
Research Profile
Shirshendu Ganguly, Assistant Professor.
Lisa Goldberg, Adjunct Professor.
Adityanand Guntuboyina, Assistant Professor. Nonparametric and high-dimensional statistics, shape constrained statistical estimation, empirical processes, statistical information theory.
Alan Hammond, Associate Professor. Statistical mechanics.
Haiyan Huang, Professor. Applied statistics, functional genomics, translational bioinformatics, high dimensional and integrative genomic/genetic data analysis, network modeling, hierarchical multi-lable classification.
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, Adjunct Professor. Bioinformatics, machine learning, nonparametrics, convex optimization, statistical computing, prediction, supervised learning.
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
Christopher Paciorek, Adjunct Professor.
Fernando Perez, Assistant Professor.
Sam Pimentel, Assistant Professor.
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, Associate Professor. Computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics.
Research Profile
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
Yun Song, 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.
Thomas Bengtsson, Lecturer.
Fletcher H. Ibser, Lecturer.
Cari Kaufman, Lecturer.
Brett Kolesnik, Lecturer.
Adam R. Lucas, Lecturer.
Libor Pospisil, Lecturer.
Gaston Sanchez Trujillo, Lecturer.
Shobhana Murali Stoyanov, Lecturer.
Visiting Faculty
Merle Behr, Visiting Assistant Professor.
Oscar Madrid Padilla, Visiting Assistant Professor.
Emeritus Faculty
David Aldous, Professor Emeritus & Professor in the Graduate School. Mathematical probability, applied probability, analysis of algorithms, phylogenetic trees, complex networks, random networks, entropy, spatial networks.
Research Profile
Peter J. Bickel, Professor Emeritus & Professor in the Graduate School. 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
Nicholas P. Jewell, Professor Emeritus. AIDS, statistics, epidemiology, infectious diseases, Ebola Virus Disease, SARS, H1N1 influenza, adverse cardiovascular effects of pharmaceuticals, counting civilian casualties during conflicts.
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, Senior Lecturer Emeritus. Statistics, foundations of probability, measurability.
Research Profile
John A. Rice, Professor Emeritus. Transportation, astronomy, statistics, functional data analysis, time series analysis.
Research Profile
Juliet Shaffer, Teaching Professor Emeritus. .
Terence P. Speed, Professor Emeritus. Genomics, statistics, genetics and molecular biology, protein sequences.
Research Profile
Aram Thomasian, Professor Emeritus.
Kenneth Wachter, Professor Emeritus. Mathematical demography stochastic models, simulation, biodemography, federal statistical system.
Research Profile
Contact Information
Undergraduate Student Services Adviser & Course and Curriculum Officer
Denise Yee
369 Evans Hall
Phone: 510-643-6131
Undergraduate Student Services Adviser
Majabeen Samadi
367 Evans Hall
Phone: 510-643-2459