Statistics | Berkeley Academic Guide (2024)

About the Program

The Department of Statistics offers the Master of Arts (MA)and Doctor of Philosophy (PhD) degrees.

Master of Arts (MA)

The Statistics MA program prepares students for careers that require statistical skills.It focuses on tackling statistical challenges encountered by industry rather than preparing for a PhD. The program is for full-time students and is designed to be completed in two semesters (fall and spring).

There is no way to transfer into the PhD program from the MA program. Students must apply to the PhD program.

Doctor of Philosophy (PhD)

The Statistics PhD program is rigorous, yet welcoming to students with interdisciplinary interests and different levels of preparation. The standard PhDprogram in statistics provides a broad background in probability theory and applied and theoretical statistics.

There are three designated emphasis (DE) tracks available to students in the PhD program who wish to pursue interdisciplinary work formally:Computational and Data Science and Engineering,Computational and Genomic Biologyand Computational Precision Health.

Visit Department Website

Admissions

  • Admission to the University
  • Admission to the Program

Admission to the University

Applying for Graduate Admission

Thank you for considering UC Berkeley for graduate study! UC Berkeley offers more than 120 graduate programs representing the breadth and depth of interdisciplinary scholarship. The Graduate Division hosts a complete list of graduate academic programs, departments, degrees offered, and application deadlines can be found on the Graduate Division website.

Prospective students must submit an online application to be considered for admission, in addition to any supplemental materials specific to the program for which they are applying. The online application and steps to take to apply can be found on the Graduate Division website.

Admission Requirements

The minimum graduate admission requirements are:

  1. A bachelor’s degree or recognized equivalent from an accredited institution;

  2. A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3.0 (B) on a 4.0 scale; and

  3. Enough undergraduate training to do graduate work in your chosen field.

For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page. It is also important to check with the program or department of interest, as they may have additional requirements specific to their program of study and degree. Department contact information can be found here.

Where to apply?

Visit the Berkeley Graduate Division application page.

Admission to the Program

In addition to the minimum requirements listed above, the following materials are required for admission:

  1. The Online Graduate Application for Admission and Fellowships:
  2. Statement of Purpose:Why are you applying to this program?What are your expectations for this degree?Where do you want this degree to take you,professionally and personally?How will your professional and personal experiences add value to the program?
  3. Personal HistoryStatement:What past experiences made you decide to go into this field? How will your personal history help you succeed in this program and your future goals?
  4. Descriptive List of Upper Division/Graduate Statistics and Math Coursework:Please include a Descriptive List of Upper Division/Graduate Statistics and Math Coursework. List the department, course number and title, instructor, grade, school, texts used and subject matter covered for all upper division and graduate level statistics and math courses you have taken. You should also include courses outside statistics and math departments that have a significant quantitative component. This list should be uploaded as a PDF document via the online application.
  5. GPA Worksheet: Please upload a GPA calculation worksheet.
  6. Resume:Include a full resume/CV listing your experience and education.

The application process is entirely online. All supplemental materials such as transcripts and the descriptive list of courses must be uploaded as PDF files via the online application by the application deadline. Please do not mail copies of your transcripts, statement of purpose, letters of recommendations, GRE and TOEFL scores, resumes, or any other documents as they will not be included with your application.

The GRE is no longer required for applicants applying to the MA or PhD program. For the PhD program, while it is not required, if you wish to include your GRE Math Subject test you will have the option to do so.

For more information about graduate programs in statistics, including admission information, please visit our graduate programs page.

Doctoral Degree Requirements

Normative Time Requirements

Normative Time to Advancement

In the first year, students must perform satisfactorily in preliminary course work. In the summer, students are required to embark on a short-term research project, internship, graduate student instructorship, reading course, or on another research activity.

In the second and third years, students continue to take courses, serve as a graduate student instructor, find an area for the oral qualifying exam, a potential thesis adviser and pass the oral qualifying exam in the spring semester of second year or in the fall semester of third year. With the successful passing of the exam, students then advance to candidacy.

Normative Time in Candidacy

In the third and fourth years, students finalize a thesis topic, continue to conduct research and make satisfactory progress.

By the end of the fifth year, students are expected to finish their thesis and give a lecture based on their work in a department seminar.

Total Normative Time

Total normative time is five years.

Time in Advancement

Curriculum

During their first year, students are normally expected to take four of the following seven core PhD courses in Probability, Theoretical Statistics, and Applied Statistics:

Course List
CodeTitleUnits
Courses Required
STAT204Probability for Applications4
STATC205AProbability Theory4
STATC205BProbability Theory4
STAT210ATheoretical Statistics4
STAT210BTheoretical Statistics4
STAT215AApplied Statistics and Machine Learning4
STAT215BStatistical Models: Theory and Application4

A member of the PhD program committee may consent to substitute courses at a comparable level in other disciplines for some of these departmental graduate courses. These requirements can also be altered by the PhD program committee.

Students entering the program before 2022are required to take five additional graduate courses beyond the four required in the first year, resulting in a total of nine graduate courses required for completion of their PhD. In their second year, students are required to take three graduate courses, at least two of them from the department offerings, and in their third year, they are required to take at least two graduate courses. Students are allowed to change the timing of these five courses with approval of their faculty mentor. Of the nine required graduate courses, students are required to take for credit a total of 24 semester hours of courses offered by the Statistics department numbered 204-272 inclusive. The Head Graduate Advisor (in consultation with the faculty mentor and after submission of a graduate student petition) may consent to substitute courses at a comparable level in other disciplines for some of these departmental graduate courses. In addition, the HGA may waive part of this unit requirement.

Starting with the cohort entering in the 2022-23 academic year, students are required to take at least three additional graduate courses beyond the four required in the first year, resulting in a total of seven graduate courses required for completion of their PhD. Of the seven required graduate courses, five of these courses must be from courses offered by the Statistics department and numbered 204-272, inclusive. With these reduced requirements, there is an expectation of very few waivers from the HGA. We emphasize that these are minimum requirements, and we expect that students will take additional classes of interest, for example on a S/U basis, to further their breadth of knowledge.

For courses to count toward the coursework requirements students must receive at least a B+ in the course (courses taken S/U do not count, except for STAT 272 which is only offered S/U). Courses that are research credits, directed study, reading groups, or departmental seminars do not satisfy coursework requirements (for courses offered by the Statistics department the courseshould be numbered 204-272to satisfy the requirements). Upper-division undergraduate courses in other departments can be counted toward course requirements with the permission of the Head Graduate Advisor. This will normally only be approved if the courses provide necessary breadth in an application area relevant to the student’s thesis research.

Qualifying Examination

The oral qualifying examination is meant to determine whether the student is ready to enter the research phase of graduate studies. It consists of a 50-minute lecture by the student on a topic selected jointly by the student and the thesis advisor. The examination committee consists of at least four faculty members to be approved by the department. At least two members of the committee must consist of faculty from the Statistics and must be members of the Academic Senate. The chair must be a member of the student’s degree-granting program.

Time in Candidacy

Advancement

Advancing to candidacy means a student is ready to write a doctoral dissertation. Students must apply for advancement to candidacy once they have successfully passed the qualifying examination.

Dissertation Presentation/Finishing Talk

The Ph.D. degree is granted upon completion of an original thesis acceptable to a committee of at least three faculty members. 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.

Required Professional Development

Students enrolled in the graduate program before fall 2016 are required to serve as aGraduate Student Instructor(GSI) for a minimum of 20 hours(equivalent to a 50% GSI appointment) during a regular academic semester by the end of their third year in the program.

Effective with the fall 2016 entering class, students are required to serve as aGraduate Student Instructor(GSI) for a minimum of two regular academic semesters and complete at least 40 hours prior to graduation (20 hours is equivalent to a 50% GSI appointment for a semester) for a course numbered 150 and above. Exceptions to this policy are routinely made by the department.

Master's Degree Requirements

Unit Requirements

In order to obtain the MA in Statistics, admitted MA students must complete a minimum of 24 units of courses and pass a comprehensive examination.

In extremely rare cases, a thesis option may be considered by the MA advisers. Typically, this will be when either the option has been offered to the student at the time of admission, or if the student arrives with substantial progress in research in an area of interest to our faculty.

Curriculum

Course List
CodeTitleUnits
Courses Required
STAT201AIntroduction to Probability at an Advanced Level4
STAT201BIntroduction to Statistics at an Advanced Level4
STAT243Introduction to Statistical Computing4
STAT230ALinear Models4
STAT222Masters of Statistics Capstone Project4
Elective4

The capstone will consist of a team-based learning experience that will give students the opportunity to work on a real-world problem and carry out a substantial data analysis project. It will culminate with a written report and an oral presentation of findings. The elective will depend on the student’s interests and will be decided in consultation with advisers.

Capstone/Thesis (Plan I)

If approved for the thesis option, you must find three faculty to be on your thesis committee. Though not required, it is strongly encouraged that one of the faculty members is from outside the Statistics Department. Both you and the thesis committee chair must agree on the topic of your thesis. Further information on how to file a thesis is available on the MA program web page.

Capstone/Comprehensive Exam (Plan II)

On a Saturday shortly after the spring semester begins in January, students will take a comprehensive exam on the theoretical foundations of statistics. There will be a 3-hour exam on the material ofSTAT201A andSTAT201B. All students taking the exam will receive copies of previous examinations.

Courses

Statistics

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 [+]

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 [+]

Terms offered: Fall 2024, Spring 2024, Fall 2023, Spring 2023, Spring 2022, Spring 2021, Spring 2020
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 [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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 [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.

Introduction to Statistics at an Advanced Level: Read More [+]

Terms offered: Fall 2023, Fall 2019, Spring 2017
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 [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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 [+]

Terms offered: Spring 2024, Spring 2023, Spring 2022
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 [+]

Terms offered: Fall 2024, Fall 2020, Fall 2016, 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 [+]

Terms offered: Spring 2024, Spring 2023, Spring 2022
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 [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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 [+]

Terms offered: Spring 2024, Spring 2023, Spring 2022
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 [+]

Terms offered: Spring 2021, Fall 2015, Fall 2012
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 [+]

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 [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
Applied statistics and machine learning, focusing on answering scientific questions using data, the data science life cycle, critical thinking, reasoning, methodology, and trustworthy and reproducible computational practice. Hands-on-experience in open-ended data labs, using programming languages such as R and Python. Emphasis on understanding and examining the assumptions behind standard statistical models and methods and the match between the
assumptions and the scientific question. Exploratory data analysis. Model formulation, fitting, model testing and validation, interpretation, and communication of results. Methods, including linear regression and generalizations, decision trees, random forests, simulation, and randomization methods.
Applied Statistics and Machine Learning: Read More [+]

Terms offered: Spring 2024, Spring 2023, Spring 2022
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 [+]

Terms offered: Spring 2024, Spring 2023, Spring 2022
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 [+]

Terms offered: Spring 2024, Spring 2023, Spring 2022
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 [+]

Terms offered: Spring 2023, Spring 2022, Fall 2018
This course will review the statistical foundations of randomized experiments and study principles for addressing common setbacks in experimental design and analysis in practice. We will cover the notion of potential outcomes for causal inference and the Fisherian principles for experimentation (randomization, blocking, and replications). We will also cover experiments with complex structures (clustering in units, factorial design, hierarchy
in treatments, sequential assignment, etc). We will also address practical complications in experiments, including noncompliance, missing data, and measurement error.
Experimental Design: Read More [+]

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 [+]

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 [+]

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 [+]

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 [+]

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 [+]

Terms offered: Spring 2023, Spring 2021, Fall 2017
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 [+]

Terms offered: Fall 2023, Fall 2021, Fall 2020
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 [+]

Terms offered: Spring 2024, Spring 2023, Spring 2022
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 [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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 [+]

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 [+]

Terms offered: Spring 2024, Spring 2023, Spring 2022
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 [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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 [+]

Terms offered: Fall 2023, Fall 2022, Fall 2021
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 exp
eriments.
Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine: Read More [+]

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 [+]

Terms offered: Spring 2022, Spring 2021, Spring 2020, Spring 2018, Spring 2017
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 [+]

Terms offered: Fall 2024, Fall 2023, Fall 2021
Course covers statistical issues surrounding estimation of effects using data on units followed through time. Course emphasizes a regression model approach for estimating associations of disease incidence modeling, continuous outcome data/linear models & longitudinal extensions to nonlinear models forms (e.g., logistic). Course emphasizes complexities that repeated measures has on the estimation process & opportunities it provides if data
is modeled appropriately. Most time is spent on 2 approaches: mixed models based upon explicit (latent variable) maximum likelihood estimation of the sources of the dependence, versus empirical estimating equation approaches (generalized estimating equations). Primary focus is from the analysis side.
Longitudinal Data Analysis: Read More [+]

Terms offered: Spring 2022, Spring 2021, Spring 2020
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 [+]

Terms offered: Spring 2008, Spring 2006, Spring 2005
The essentials of stochastic analysis, particularly those most relevant to financial engineering, will be surveyed: Brownian motion, stochastic integrals, Ito's formula, representation of martingales, Girsanov's theorem, stochastic differential equations, and diffusion processes. Examples will be taken from the Black-Scholes-Merton theory of pricing and hedging contingent claims such as options, foreign market derivatives, and interest rate
related contracts.
Stochastic Analysis with Applications to Mathematical Finance: Read More [+]

Terms offered: Fall 2024, Spring 2024, Fall 2023
This course is about statistical learning methods and their use for data analysis. Upon completion, students will be able to build baseline models for real world data analysis problems, implement models using programming languages and draw conclusions from models. The course will cover principled statistical methodology for basic machine learning tasks such as regression, classification, dimension reduction and clustering. Methods discussed will
include linear regression, subset selection, ridge regression, LASSO, logistic regression, kernel smoothing methods, tree based methods, bagging and boosting, neural networks, Bayesian methods, as well as inference techniques based on resampling, cross validation and sample splitting.
Modern Statistical Prediction and Machine Learning: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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 [+]

Terms offered: Spring 2023, Spring 2022, Spring 2021
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 [+]

Terms offered: Fall 2024, Spring 2024, Spring 2023
Special topics in probability and statistics offered according to student demand and faculty availability.

Topics in Probability and Statistics: Read More [+]

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 [+]

Terms offered: Spring 2024
Forecasting has been used to predict elections, climate change, and the spread of COVID-19. Poor forecasts led to the 2008 financial crisis. In our daily lives, good forecasting ability can help us plan our work, be on time to events, and make informed career decisions. This practically-oriented class will provide students with tools to make good forecasts, including Fermi estimates, calibration training, base rates, scope sensitivity, and power laws.

Forecasting: Read More [+]

Terms offered: Fall 2024, Spring 2024, Fall 2023
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 [+]

Terms offered: Fall 2024, Spring 2024, Fall 2023
Special topics, by means of lectures and informational conferences.

Statistics Research Seminar: Read More [+]

Terms offered: Fall 2024, Spring 2024, Fall 2023
Special tutorial or seminar on selected topics.

Directed Study for Graduate Students: Read More [+]

Terms offered: Fall 2024, Spring 2024, Fall 2023
Individual study

Individual Study Leading to Higher Degrees: Read More [+]

Terms offered: Fall 2024, Spring 2024, Fall 2023
Discussion, problem review and development, guidance of laboratory classes, course development, supervised practice teaching.

Professional Preparation: Teaching of Probability and Statistics: Read More [+]

Terms offered: Fall 2024, Spring 2024, Fall 2023
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 [+]

Terms offered: Fall 2024, Spring 2024, Fall 2023
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 [+]

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 [+]

Contact Information

Department of Statistics

367 Evans Hall

Phone: 510-642-2781

Fax: 510-642-7892

Visit Department Website

Department Chair

Haiyan Huang

367 Evans Hall

chair-stat@berkeley.edu

PhD Program Coordinator

La Shana Porlaris

373 Evans Hall

Phone: 510-642-5361

stat-phd@berkeley.edu

Master's Program Coordinator

David Apilado Jr.

375 Evans Hall

Phone: (510) 643-0589

stat-ma@berkeley.edu

Statistics | Berkeley Academic Guide (2024)
Top Articles
Latest Posts
Article information

Author: Lakeisha Bayer VM

Last Updated:

Views: 5988

Rating: 4.9 / 5 (69 voted)

Reviews: 84% of readers found this page helpful

Author information

Name: Lakeisha Bayer VM

Birthday: 1997-10-17

Address: Suite 835 34136 Adrian Mountains, Floydton, UT 81036

Phone: +3571527672278

Job: Manufacturing Agent

Hobby: Skimboarding, Photography, Roller skating, Knife making, Paintball, Embroidery, Gunsmithing

Introduction: My name is Lakeisha Bayer VM, I am a brainy, kind, enchanting, healthy, lovely, clean, witty person who loves writing and wants to share my knowledge and understanding with you.