Berkeley statistics program

Statistics or Electrical Engineering and Computer Science are preferred. Terms offered: Spring , Fall , Spring 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.

Strongly recommended corequisite: Statistics 33A or Statistics Terms offered: Spring , Fall , Spring 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.

Terms offered: Summer 8 Week Session, Spring , Fall 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. Credit Restrictions: Students will not receive credit for after taking or A. Terms offered: Summer 8 Week Session, Spring , Fall 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. Strongly recommended corerequisite: STAT Terms offered: Spring , Fall , Spring 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. Course Objectives: The emphasis on simulation and the bootstrap in Data 8 gives students a concrete sense of randomness and sampling variability.

Stat 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 So Stat will start faster than Stat 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. Data 8 , there is considerable demand for follow-on courses that build on the skills acquired in that class.

Stat 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.

Further topics such as: continuous time Markov chains, queueing theory, point processes, branching processes, renewal theory, stationary processes, Gaussian processes. Terms offered: Fall , Spring , Fall 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. STAT recommended. Terms offered: Spring , Spring , Spring 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.

Terms offered: Spring , Fall , Spring 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. Terms offered: Spring , Fall , Spring 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. Prerequisites: Mathematics 53 or equivalent; Mathematics 54, Electrical Engineering 16A, Statistics 89A, Mathematics or equivalent linear algebra; Statistics or equivalent; experience with some programming language. Recommended prerequisite: Mathematics 55 or equivalent exposure to counting arguments.

Terms offered: Spring , Summer 8 Week Session, Spring General theory of zero-sum, two-person games, including games in extensive form and continuous games, and illustrated by detailed study of examples. Terms offered: Fall , Fall , Fall 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.

Terms offered: Spring , Fall , Spring 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. Prerequisites: Mathematics , Statistics , Knowledge of scientific computing environment R or Matlab often required.

Prerequisites might vary with instructor and topics. Terms offered: Spring , Spring , Spring 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. Prerequisites: Statistics and or consent of instructor. Statistics may be taken concurrently. Statistics is recommended. Terms offered: Spring , Spring , Fall 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. Prerequisites: Statistics , Statistics , and Statistics or equivalent. Summer: 6 weeks - hours of independent study per week 8 weeks - hours of independent study per week. Terms offered: Fall , Fall , Spring Supervised experience relevant to specific aspects of statistics in on-campus or off-campus settings.

Credit Restrictions: Enrollment is restricted; see the Introduction to Courses and Curricula section of this catalog. Summer: 6 weeks - hours of fieldwork per week 8 weeks - hours of fieldwork per week 10 weeks - hours of fieldwork per week. Terms offered: Fall , Spring , Fall Special tutorial or seminar on selected topics.

Directed Study for Undergraduates: Read Less [-]. Summer: 6 weeks - hours of independent study per week 8 weeks - hours of independent study per week 10 weeks - hours of independent study per week. Peter L. Bartlett, Professor.

Statistics, machine learning, statistical learning theory, adaptive control. Research Profile. Andrew Bray, Teaching Professor. Phase transitions in computer science, structural and dynamical properties of networks, graphons, machine learning, ethical decision making, climate change. Peng Ding, Associate Professor. Statistical causal inference, missing data, Bayesian statistics, applied statistics. 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.

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.

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.

Avi Feller, Assistant Professor. Applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences. Will Fithian, Assistant Professor. Theoretical and Applied Statistics. Shirshendu Ganguly, Assistant Professor. Probability theory, statistical mechanics.

Adityanand Guntuboyina, Associate Professor. Nonparametric and high-dimensional statistics, shape constrained statistical estimation, empirical processes, statistical information theory. Alan Hammond, Professor.

Statistical mechanics. Haiyan Huang, Professor. Jiantao Jiao, Assistant Professor. Artificial intelligence, control and intelligent systems and robotics, communications and networking. Michael I. Jordan, Professor. Computer science, artificial intelligence, bioinformatics, statistics, machine learning, electrical engineering, applied statistics, optimization. Professional Networking Set up a LinkedIn profile.

Job Search Tips 1. Where to Look? LinkedIn Google Jobs Indeed. Networking Coming soon Research, Research, Research Coming soon Teaching innovations Our instruction is at the cutting edge of statistical science. Prospective Students Information about our degree programs and the application process, for prospective undergraduate and graduate students. Current Students Information, policies, and forms related to our degree programs, for current undergraduate and graduate students.

Department Members Administrative information and resources for current department members, including support for hiring, reimbursements, teaching, grants, and events. Outside Community Find out how you can interact with the Statistics Department, including our Industry Alliance Program, alumni programs, and Statistical Consulting service.

Sandrine Dudoit, Elizabeth Purdom, and students work on the first comprehensive atlas of brain cells. Sandrine Dudoit, Elizabeth Purdom, and students work on the first comprehensive atlas of brain cells October 6, The Ph. The majority or at least half of the committee must consist of faculty from Statistics and must be members of the Academic Senate. The thesis should be presented at an appropriate seminar in the department prior to filing with the Dean of the Graduate Division.

See Alumni if you would like to view thesis titles of former PhD Students. Graduate Division offers various resources, including a workshop, on how to write a thesis, from beginning to end. Requirements for the format of the thesis are rather strict.

For workshop dates and guidelines for submitting a dissertation, visit the Graduate Division website. Students who have advanced from candidacy i. This annual review is required by Graduate Division. Exceptions to this policy are routinely made by the department. Both students and industry alliance partners present research in the form of posters and lightning talks. This requirement is intended to acclimate students to presenting their research and allow the department generally to see the fruits of their research.

It is also an opportunity for less advanced students to see examples of research of more senior students. However, any students who do not yet have research to present can be exempted at the request of their thesis advisor or their faculty mentors if an advisor has not yet been determined. Initial Mentoring: PhD students will be assigned a faculty mentor in the summer before their first year. The job of this faculty mentor is primarily to advise the student on how to find a thesis advisor and in selecting appropriate courses, as well as other degree-related topics such as applying for fellowships.

Students should meet with their faculty mentors twice a semester. This faculty member will be the designated faculty mentor for the student during roughly their first two years, at which point students will find a qualifying exam chair who will take over the role of mentoring the student.

For students who have two thesis advisors, however, there is not an additional faculty mentor, and the quals chair does NOT serve as the faculty mentor. Students should determine their qualifying exam chair either at the beginning of the semester of the qualifying exam or in the fall semester of the third year, whichever is earlier.

Students are expected to have narrowed in on a thesis advisor and research topic by the fall semester of their third year and may have already taken qualifying exams , but in the case where this has not happened, such students should find a quals chair as soon as feasible afterward to serve as faculty mentor.

Students are required to meet with their QE chair once a semester during the academic year. In the fall, this meeting will generally be just a meeting with the student and the QE chair, but in the spring it must be a joint meeting with the student, the QE chair, and the PhD advisor.

If students are co-advised, this should be a joint meeting with their co-advisors. If there is a need for a substitute faculty mentor e. Each of these milestones is not complete until you have filled out the requisite form and submitted it to the GSAO.

If you are not meeting these milestones by the below deadline, you need to meet with the Head Graduate Advisor to ask for an extension. Otherwise, you will be in danger of not being in good academic standing and being ineligible for continued funding including GSI or GSR appointments, and many fellowships.

Jointly with PhD advisor s and Research mentor.