uc berkeley phd admission statistics

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Grad Applicant Count Report

The Grad Applicant Count report provides a high-level view of graduate admissions for one or more academic years.  You can view headcounts for applied, admitted, and SIRed, as well as admit rate and yield rate for graduate applicants by year, semester, derived residency, and degree level/goal by college/school, division, department, major, academic plan, and academic subplan.

You can filter data in the report by academic year, derived residency, semester, and degree level.

The tabular reports for headcount by applied, admitted, and SIRed are organized by college in the rows and year in the columns. You can drill down on college/school to see division, department, and intended majors.

The trend reports for admit rate and yield rate are at the college level and do not have a drill down by major.

Sample Questions

How many prospective graduate students applied for entry into uc berkeley for 2022-23.

• Select 2022-23 for the Academic Year and click Apply
• The Applicant Headcount tabular chart on the left shows the applicant headcount by school/college and the total for campus. A total of 40,881 students applied to graduate programs (that have data included in this dashboard) for the 2022-23 academic year.

How many of these applicants were admitted? How does this admit rate vary?

• The Admitted Headcount chart shows the number of applicants admitted by school or college.
• The Admit Rate line chart shows the admit rate for each school or college so you can make comparisons by school/college, by year and overall trend.

How many admitted students accepted their admissions offer by submitting a Statement of Intent to Register (SIR)? How does the yield rate vary?

• The Statement of Intent to Register Headcount chart shows the number of admitted students that submitted a statement of intent to register by school or college.
• The Yield Rate chart shows the yield rate for each school or college so you can make comparisons by school/college, by year and overall trend.
• With so many lines in the chart, it can be difficult to see the college or school you are interested in. Use the Export link below the chart to export data. You can export to a formatted file, e.g. PDF, Excel, etc. or as raw data in a comma separated values (CSV) file.

What were that subplan's admit and yield rates?

• Right-click on the Applied Headcount column, select Include column, and select Admit Rate.
• Right-click on the Admit Rate column, select Include column, and select Yield Rate.

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Berkeley Berkeley Academic Guide: Academic Guide 2023-24

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 PhD program 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 Biology  and Computational Precision Health .

Visit Department Website

Admission to the University

Admission to the program, 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. A complete list of graduate academic 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 can be found on the Graduate Division website .

Admission Requirements

The minimum graduate admission requirements are:

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

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

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 .

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

• The Online Graduate Application for Admission and Fellowships :
• 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?
• Personal History  Statement  :  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? 
• 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.
• 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. 

T he 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 

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:

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 2022  are 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 course should be numbered 204-272 to 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 a  Graduate 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 a  Graduate 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. 

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 of   STAT 201A and  STAT 201B . All students taking the exam will receive copies of previous examinations. 

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

Terms offered: Fall 2018, Fall 2011, Fall 2010 Probability spaces, random variables, distributions in probability and statistics, central limit theorem, Poisson processes, transformations involving random variables, estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory. Introduction to Probability and Statistics at an Advanced Level: Read More [+]

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 Format: Three hours of Lecture and Two hours of Laboratory per week for 15 weeks.

Additional Details

Subject/Course Level: Statistics/Graduate

Grading: Letter grade.

Introduction to Probability and Statistics at an Advanced Level: Read Less [-]

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

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 [+]

Credit Restrictions: Students will receive no credit for Statistics 200A-200B after completing Statistics 201A-201B.

STAT C200C Principles and Techniques of Data Science 4 Units

Terms offered: 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 [+]

Prerequisites: COMPSCI C8 / INFO C8 / STAT C8 or ENGIN 7 ; and either COMPSCI 61A or COMPSCI 88. Corequisites: MATH 54 or EECS 16A

Credit Restrictions: Students will receive no credit for DATA C200 \ COMPSCI C200A \ STAT C200C after completing DATA C100 .

Fall and/or spring: 8 weeks - 6 hours of lecture, 2 hours of discussion, and 2 hours of laboratory per week 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 Format: Three hours of lecture and one hour of discussion and one hour of laboratory per week. Six hours of lecture and two hours of discussion and two hours of laboratory per week for 8 weeks. Six hours of lecture and two hours of discussion and two hours of laboratory per week for 8 weeks.

Formerly known as: Statistics C200C/Computer Science C200A

Also listed as: COMPSCI C200A/DATA C200

Principles and Techniques of Data Science: Read Less [-]

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

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

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 .

Additional Format: Three hours of lecture and two hours of laboratory per week.

Introduction to Probability at an Advanced Level: Read Less [-]

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

Terms offered: Fall 2023, Fall 2022, Fall 2021 Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory. Introduction to Statistics at an Advanced Level: Read More [+]

Credit Restrictions: Students will receive no credit for Statistics 201B after completing Statistics 200B.

Introduction to Statistics at an Advanced Level: Read Less [-]

STAT 204 Probability for Applications 4 Units

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 [+]

Credit Restrictions: Students will receive no credit for Statistics 204 after completing Statistics 205A-205B.

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Format: Three hours of Lecture per week for 15 weeks.

Instructor: Evans

Probability for Applications: Read Less [-]

STAT C205A Probability Theory 4 Units

Terms offered: Fall 2023, Fall 2022, Fall 2021, Fall 2019 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 [+]

Also listed as: MATH C218A

Probability Theory: Read Less [-]

STAT C205B Probability Theory 4 Units

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 [+]

Also listed as: MATH C218B

STAT C206A Advanced Topics in Probability and Stochastic Process 3 Units

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 [+]

Prerequisites: Statistics C205A-C205B or consent of instructor

Repeat rules: Course may be repeated for credit with instructor consent.

Also listed as: MATH C223A

Advanced Topics in Probability and Stochastic Process: Read Less [-]

STAT C206B Advanced Topics in Probability and Stochastic Processes 3 Units

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 [+]

Also listed as: MATH C223B

Advanced Topics in Probability and Stochastic Processes: Read Less [-]

STAT 210A Theoretical Statistics 4 Units

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

Prerequisites: Linear algebra, real analysis, and a year of upper division probability and statistics

Additional Format: Three hours of lecture per week.

Theoretical Statistics: Read Less [-]

STAT 210B Theoretical Statistics 4 Units

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 [+]

Prerequisites: Statistics 210A and a graduate level probability course; a good understanding of various notions of stochastic convergence

STAT 212A Topics in Theoretical Statistics 3 Units

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 [+]

Prerequisites: 210 or 205 and 215

Formerly known as: 216A-216B and 217A-217B

Topics in Theoretical Statistics: Read Less [-]

STAT 212B Topics in Theoretical Statistics 3 Units

Terms offered: Spring 2016 This course introduces the student to topics of current research interest in theoretical statistics. Recent topics include information theory, multivariate analysis and random matrix theory, high-dimensional inference. Typical topics have been model selection; empirical and point processes; the bootstrap, stochastic search, and Monte Carlo integration; information theory and statistics; semi- and non-parametric modeling; time series and survival analysis. Topics in Theoretical Statistics: Read More [+]

STAT 215A Statistical Models: Theory and Application 4 Units

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

Prerequisites: Linear algebra, calculus, upper division probability and statistics, and familiarity with high-level programming languages. Statistics 133, 134, and 135 recommended

Statistical Models: Theory and Application: Read Less [-]

STAT 215B Statistical Models: Theory and Application 4 Units

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 [+]

Prerequisites: Statistics 215A or consent of instructor

STAT 222 Masters of Statistics Capstone Project 4 Units

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 [+]

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

Fall and/or spring: 15 weeks - 3 hours of seminar and 1 hour of laboratory per week

Additional Format: One hour of laboratory and three hours of seminar per week.

Masters of Statistics Capstone Project: Read Less [-]

STAT 230A Linear Models 4 Units

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 [+]

Prerequisites: Matrix algebra, a year of calculus, two semesters of upper division or graduate probability and statistics

Linear Models: Read Less [-]

STAT 232 Experimental Design 4 Units

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 [+]

Prerequisites: Statistics 134 and Statistics 135 and experience with Software R, or consent of instructor

Repeat rules: Course may be repeated for credit without restriction.

Experimental Design: Read Less [-]

STAT 238 Bayesian Statistics 3 Units

Terms offered: Fall 2016 Bayesian methods and concepts: conditional probability, one-parameter and multiparameter models, prior distributions, hierarchical and multi-level models, predictive checking and sensitivity analysis, model selection, linear and generalized linear models, multiple testing and high-dimensional data, mixtures, non-parametric methods. Case studies of applied modeling. In-depth computational implementation using Markov chain Monte Carlo and other techniques. Basic theory for Bayesian methods and decision theory. The selection of topics may vary from year to year. Bayesian Statistics: Read More [+]

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

Prerequisites: Probability and mathematical statistics at the level of Stat 134 and Stat 135 or, ideally, Stat 201A and Stat 201B

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of laboratory per week

Additional Format: Three hours of lecture and one hour of laboratory per week.

Bayesian Statistics: Read Less [-]

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

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

Prerequisites: At least one graduate matrix based multivariate regression course in addition to introductory statistics and probability

Fall and/or spring: 15 weeks - 3-3 hours of lecture and 1-2 hours of discussion per week

Additional Format: Three hours of lecture and one to two hours of discussion per week.

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 [-]

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

Terms offered: Spring 2016, Spring 2015 A seminar on successful research designs and a forum for students to discuss the research methods needed in their own work, supplemented by lectures on relevant statistical and computational topics such as matching methods, instrumental variables, regression discontinuity, and Bayesian, maximum likelihood and robust estimation. Applications are drawn from political science, economics, sociology, and public health. Experience with R is assumed. Quantitative Methodology in the Social Sciences Seminar: Read More [+]

Prerequisites: Statistics 239A or equivalent

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 [-]

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

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

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week

Additional Format: Three hours of Lecture and Two hours of Discussion per week for 15 weeks.

Also listed as: POL SCI C236A

STAT C239B Quantitative Methodology in the Social Sciences Seminar 4 Units

Terms offered: Spring 2018, Spring 2017 A seminar on successful research designs and a forum for students to discuss the research methods needed in their own work, supplemented by lectures on relevant statistical and computational topics such as matching methods, instrumental variables, regression discontinuity, and Bayesian, maximum likelihood and robust estimation. Applications are drawn from political science, economics, sociology, and public health. Experience with R is assumed. Quantitative Methodology in the Social Sciences Seminar: Read More [+]

Additional Format: Three hours of lecture and two hours of discussion per week.

Also listed as: POL SCI C236B

STAT 240 Nonparametric and Robust Methods 4 Units

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 [+]

Prerequisites: A year of upper division probability and statistics

Nonparametric and Robust Methods: Read Less [-]

STAT C241A Statistical Learning Theory 3 Units

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 [+]

Instructors: Bartlett, Jordan, Wainwright

Also listed as: COMPSCI C281A

Statistical Learning Theory: Read Less [-]

STAT C241B Advanced Topics in Learning and Decision Making 3 Units

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 [+]

Also listed as: COMPSCI C281B

Advanced Topics in Learning and Decision Making: Read Less [-]

STAT 243 Introduction to Statistical Computing 4 Units

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

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.

Prerequisites: Graduate standing

Introduction to Statistical Computing: Read Less [-]

STAT 244 Statistical Computing 4 Units

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

Prerequisites: Knowledge of a higher level programming language

Statistical Computing: Read Less [-]

STAT C245A Introduction to Modern Biostatistical Theory and Practice 4 Units

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 [+]

Prerequisites: Statistics 200A (may be taken concurrently)

Instructor: Hubbard

Also listed as: PB HLTH C240A

Introduction to Modern Biostatistical Theory and Practice: Read Less [-]

STAT C245B Biostatistical Methods: Survival Analysis and Causality 4 Units

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

Prerequisites: Statistics 200B (may be taken concurrently)

Instructor: van der Laan

Also listed as: PB HLTH C240B

Biostatistical Methods: Survival Analysis and Causality: Read Less [-]

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

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 [+]

Prerequisites: Statistics 200A or equivalent (may be taken concurrently)

Instructor: Dudoit

Also listed as: PB HLTH C240C

Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine: Read Less [-]

STAT C245D Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine II 4 Units

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 [+]

Prerequisites: Statistics 200A-200B or Statistics 201A-201B (may be taken concurrently) or consent of instructor

Also listed as: PB HLTH C240D

Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine II: Read Less [-]

STAT C245F Statistical Genomics 4 Units

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 [+]

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Format: Three hours of lecture and one hour of discussion per week.

Instructors: Dudoit, Huang, Nielsen, Song

Also listed as: PB HLTH C240F

Statistical Genomics: Read Less [-]

STAT C247C Longitudinal Data Analysis 4 Units

Terms offered: Fall 2023, Fall 2021, Fall 2019 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 [+]

Course Objectives: After successfully completing the course, you will be able to: • frame data science questions relevant to longitudinal studies as the estimation of statistical parameters generated from regression, • derive consistent statistical inference in the presence of correlated, repeated measures data using likelihood-based mixed models and estimating equation approaches (generalized estimating equations; GEE), • implement the relevant methods using R. • interpret the regression output, including both coefficients and variance components and

Prerequisites: 142, 145, 241 or equivalent courses in basic statistics, linear and logistic regression

Also listed as: PB HLTH C242C

Longitudinal Data Analysis: Read Less [-]

STAT 248 Analysis of Time Series 4 Units

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 [+]

Prerequisites: 102 or equivalent

Analysis of Time Series: Read Less [-]

STAT 251 Stochastic Analysis with Applications to Mathematical Finance 3 Units

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 [+]

Prerequisites: 205A or consent of instructor

Stochastic Analysis with Applications to Mathematical Finance: Read Less [-]

STAT 254 Modern Statistical Prediction and Machine Learning 4 Units

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

Prerequisites: STAT 135 , the combination of DATA/STAT/COMPSCI C100 and DATA/STAT C140, or equivalent

Modern Statistical Prediction and Machine Learning: Read Less [-]

STAT 256 Causal Inference 4 Units

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

Prerequisites: Statistics 201B or Statistics 210A

Causal Inference: Read Less [-]

STAT 259 Reproducible and Collaborative Statistical Data Science 4 Units

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 [+]

Prerequisites: Statistics 133, Statistics 134, and Statistics 135 (or equivalent)

Credit Restrictions: Students will receive no credit for Statistics 259 after taking Statistics 159.

Reproducible and Collaborative Statistical Data Science: Read Less [-]

STAT 260 Topics in Probability and Statistics 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Special topics in probability and statistics offered according to student demand and faculty availability. Topics in Probability and Statistics: Read More [+]

Topics in Probability and Statistics: Read Less [-]

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

Terms offered: Spring 2016, Spring 2015, Spring 2014 Selected topics in quantitative/statistical methods of research in the social sciences and particularly in sociology. Possible topics include: analysis of qualitative/categorical data; loglinear models and latent-structure analysis; the analysis of cross-classified data having ordered and unordered categories; measure, models, and graphical displays in the analysis of cross-classified data; correspondence analysis, association analysis, and related methods of data analysis. Quantitative/Statistical Research Methods in Social Sciences: Read More [+]

Prerequisites: Consent of instructor

Fall and/or spring: 15 weeks - 2 hours of lecture per week

Additional Format: Two hours of Lecture per week for 15 weeks.

Also listed as: SOCIOL C271D

Quantitative/Statistical Research Methods in Social Sciences: Read Less [-]

STAT 265 Forecasting 3 Units

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 [+]

Course Objectives: We’ll discuss several historical instances of successful and unsuccessful forecasts, and practice making forecasts about our own lives, about current events, and about scientific progress.

Student Learning Outcomes: Formulate questions that are relevant to their own life or work. Identify well-defined versus poorly-defined forecasting questions. Provide forecasts that are well-calibrated. Understand common forecasting pitfalls, such as improper independence assumptions, and how to identify and guard against them. Understand how forecasts evolve across time in response to new information. Use forecasts to inform decisions. Utilize a variety of forecasting tools, such as base rates, to improve their forecasts. Utilize and filter data across a variety of sources to inform their forecasts. Work in teams to improve forecasts.

Prerequisites: Stat 134, Data/Stat C140, EECS 126 , Math 106, IND ENG 172 , or equivalent; and familiarity with Python; or consent of instructor. Strongly Recommended: Compsci 61A, Data/Compsci C88C, or equivalent

Forecasting: Read Less [-]

STAT 272 Statistical Consulting 3 Units

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

Prerequisites: Some course work in applied statistics and permission of instructor

Fall and/or spring: 15 weeks - 2 hours of session per week

Additional Format: Two hours of session per week and individual meetings as necessary.

Grading: Offered for satisfactory/unsatisfactory grade only.

Statistical Consulting: Read Less [-]

STAT 278B Statistics Research Seminar 1 - 4 Units

Terms offered: Spring 2024, Fall 2023, Spring 2023 Special topics, by means of lectures and informational conferences. Statistics Research Seminar: Read More [+]

Fall and/or spring: 15 weeks - 0 hours of seminar per week

Additional Format: Two or more hours of seminar per week.

Statistics Research Seminar: Read Less [-]

STAT 298 Directed Study for Graduate Students 1 - 12 Units

Terms offered: Spring 2024, Fall 2023, Spring 2023 Special tutorial or seminar on selected topics. Directed Study for Graduate Students: Read More [+]

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 Format: Zero hours of Independent study per week for 15 weeks. One to Twelve hour of Independent study per week for 8 weeks. One to Sixteen hour of Independent study per week for 6 weeks.

Directed Study for Graduate Students: Read Less [-]

STAT 299 Individual Study Leading to Higher Degrees 0.5 - 12 Units

Terms offered: Spring 2024, Fall 2023, Spring 2023 Individual study Individual Study Leading to Higher Degrees: Read More [+]

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 Format: Work hours to be arrange based on unit value. Offered for 1-6 units during Summer Session.

Individual Study Leading to Higher Degrees: Read Less [-]

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

Terms offered: Spring 2024, Fall 2023, Spring 2023 Discussion, problem review and development, guidance of laboratory classes, course development, supervised practice teaching. Professional Preparation: Teaching of Probability and Statistics: Read More [+]

Prerequisites: Graduate standing and appointment as a graduate student instructor

Fall and/or spring: 15 weeks - 2 hours of lecture and 4 hours of laboratory per week

Additional Format: One or two hours of lecture and two to four of laboratory per week.

Subject/Course Level: Statistics/Professional course for teachers or prospective teachers

Formerly known as: Statistics 300

Professional Preparation: Teaching of Probability and Statistics: Read Less [-]

STAT 601 Individual Study for Master's Candidates 0.5 - 8 Units

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

Repeat rules: Course may be repeated for credit up to a total of 16 units.

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 Format: One-half to eight hours of independent study per week. One to twelve hours of independent study per week for 10 weeks. One to fifteen hours of independent study per week for 8 weeks. One and one-half to twenty hours of independent study per week for 6 weeks.

Subject/Course Level: Statistics/Graduate examination preparation

Individual Study for Master's Candidates: Read Less [-]

STAT 602 Individual Study for Doctoral Candidates 0.5 - 8 Units

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

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.

Individual Study for Doctoral Candidates: Read Less [-]

STAT 700 Statistics Colloquium 0.0 Units

Terms offered: Prior to 2007 The Statistics Colloquium is a forum for talks on the theory and applications of Statistics to be given to the faculty and graduate students of the Statistics Department and other interested parties. Statistics Colloquium: Read More [+]

Fall and/or spring: 15 weeks - 1-2 hours of colloquium per week

Additional Format: One to two hours of colloquium per week.

Grading: The grading option will be decided by the instructor when the class is offered.

Formerly known as: Statistics 999

Statistics Colloquium: Read Less [-]

Contact Information

Department of statistics.

367 Evans Hall

Phone: 510-642-2781

Fax: 510-642-7892

Department Chair

Haiyan Huang

[email protected]

PhD Program Coordinator

La Shana Porlaris

373 Evans Hall

Phone: 510-642-5361

[email protected]

Master's Program Coordinator

David Apilado Jr.

375 Evans Hall

Phone: (510) 643-0589

[email protected]

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Ph.D. in Economics Graduate Admissions Profile

Admission is based primarily on four factors:

• Mathematical ability and training as evidenced by courses taken in mathematics and related subjects, grades in those courses, and the quantitative GRE score.
• General academic ability as evidenced by the applicant's academic record, including both grades and the difficulty of courses taken.
• Training in economics as evidenced by the applicant's academic record in economics and related fields, letters of recommendation, and work experience related to economics.
• Evidence of motivation and aptitude for research judged primarily on the basis of the letters of recommendation, the statement of purpose, work experience in economics, and previous research projects.

In past years most applicants recommended for graduate admission were in the following profile areas:

• Major GPA of 3.8 or higher with consideration for the degree of difficulty of the course work.
• Upper level mathematical course work including real analysis or honors advanced calculus with grades of A- minus or better.
• Quantitative GRE score of 165 or higher.*
• Grades of A-minus or better in intermediate level theory courses (microeconomics, macroeconomics, econometrics) with a strong preference for honors or mathematical track versions of all three courses.
• Advanced undergraduate or graduate-level course work in economics.
• Proven independent research ability (e.g., honors thesis).
• Very strong letters of recommendations from at least two faculty members who know the candidate in both classroom and out-of-class settings (e.g. independent research).
• Statement of purpose that expresses coherent ideas about why the candidate is interested in pursuing a Ph.D. in economics and describes likely areas of research interest.
• TOEFL (if applicable) at least 90 for the Internet-based test (iBT).
• IELTS (is accepted) most recent overall Band score must be at least 7 on a 9-point scale.

*GRE scores will not be required for the 2021-22 admissions season (for matriculation in Fall 2022).

UPDATE 12/18/2020 (a note on P/NP grades since the Covid-19/Coronavirus Pandemic):

UC Berkeley evaluates applicants for admission to its graduate and professional schools holistically, meaning that we consider an applicant’s combination of personal accomplishments, letters of recommendation, personal statements, academic record, and test scores in making our admissions decisions. Such a review will take into account the significant disruptions of COVID-19 when reviewing students’ transcripts and other admissions materials during the pandemic. Thus, we will not penalize students for the adoption of P/NP grades and reduced research experiences during this unprecedented period, whether the choices were made by institutions or by individual students. What is most important is that applicants demonstrate that they pursued a challenging curriculum that was relevant to their plan for graduate or professional school.

As we have continued to observe in admissions cycles, Berkeley graduates have a remarkable track record of success in graduate school admissions to the most competitive programs in the country. Our graduates are admired for their academic abilities and resilience, and are assessed, as always, on their overall performance. We are confident that graduate admissions committees will make generous allowances for the academic challenges produced by the COVID-19 pandemic.

CBE Ph.D. Admission Requirements

At Berkeley, graduate work towards a PhD in Chemical and Biomolecular Engineering emphasizes the excitement of original research in frontier areas of applied science. While formal courses are necessary to provide scientific fundamentals and intellectual breadth, the primary characteristic of Berkeley's graduate experience is to participate in the quest for new knowledge. Graduate students and faculty collaborate as partners in scholarship, in learning, and in intellectual discovery.

The online application is available starting in September at the Graduate Division Website .   Applications are only accepted for the fall semester.

Application Fee Waivers

Application fee waivers are provided to members of various professional societies and program participants, including those that are members of (or previously participated in) NSF-REUs, NSBE, NOBCChE, SACNAS, AmeriCorps, Teach for America, and the Peace Corps, among many others. Fee waivers are also considered for those with a demonstrated financial need. Please see the following Graduate Divison website for more information on requesting an application fee waiver:  https://grad.berkeley.edu/admissions/apply/fee-waiver/

Application Materials

Admission is granted by the University's Graduate Division on the recommendation of the department. Applicants are required to provide the following: evidence of superior performance and research experience in the last 2 years of undergraduate studies; a statement of purpose; personal history statement (see FAQ's ) and 3 letters of recommendation from professors or colleagues familiar with the applicant's academic, research, and professional aptitudes. International students whose native language is not English must provide evidence of English language proficiency. The weight of evidence from all sources determines admission. Students do not need a master's degree to apply for a doctoral degree. Most applicants will have completed a typical undergraduate program in chemical engineering. However, admission may be granted to students with undergraduate degrees in a related discipline. In this case, necessary background courses in chemical engineering are taken as part of the program for the first year.

GRE (Graduate Record Exam)

The faculty of the Department of Chemical and Biomolecular Engineering (CBE) at UC Berkeley have determined the Graduate Records Exam (GRE) is a proven identifier of race and socio-economic status. Its association with successful completion of the doctorate is minimal, and its fulfillment is an economic burden on applicants. CBE will no longer consider it for admission.

Evidence of English Language Proficiency

All international applicants are required to provide evidence of English language proficiency by providing test scores from either the TOEFL or International English Language Testing System (IELTS). The minimum score for the TOEFL is at least a 90 on the (Internet-based test) iBT. You may also submit an IELTS score of an overall Band score of 7 on a 9-point scale.The TOEFL/IELTS requirement can be waived if you have studied at a U.S. institution of higher education for at least a year and received grades of B or better. In that case, an official transcript from that institution can be used as evidence of English language proficiency.

We welcome inquiries concerning admission to our graduate program by email at [email protected] . Prospective students are encouraged to visit the department only after receiving an offer of admission.

• Test of English as a Foreign Language (TOEFL)
• Frequently Asked Questions (FAQs)
• Information regarding UC Fellowships for Entering PhD students

PhD Application Deadline

Dec 4, 2023 8:59 PM

Graduate Research Degree Programs (M.S./Ph.D.)

The Master of Science (M.S.) and Doctor of Philosophy (Ph.D.) programs emphasize research preparation and experience.

Please note that Berkeley EECS no longer requires, nor accepts, GRE scores.

The Fall 2024 Admission application is now closed

The next available application cycle will be for Fall 2025.  The application will reopen in September 2024.

When choosing between our degree programs (M.S., M.S./Ph.D., Ph.D.), please carefully consider the descriptions at the bottom of this page.

Application Prerequisites for All Graduate Research Degree Programs

The minimum graduate admission requirements are:

• A bachelor’s degree or recognized equivalent from an accredited institution . If you are in your final year of studies, and you expect to earn your degree by mid-August of the following year, you may apply. If you are admitted, you will be required to provide proof at that time that you have earned your bachelor’s degree, usually in the form of a final official transcript.
• If you attended a university that uses a 4.0 grade-point average (GPA) scale, a satisfactory scholastic average with a minimum GPA of 3.0 (B) is required. If you attended a university that does not use the 4.0 GPA scale, please do not try to convert your grades to the 4.0 scale for the application.
• Three Letters of Recommendation uploaded as PDFs through the link provided in the online application. Your letters could include details about your goals, research accomplishments, technical and leadership skills, academic work, etc. We suggest you give your recommenders at least two months to write your letters.
• If you received or are a candidate to receive a degree from an institution outside the United States , please see Minimum Degree Requirements for International Applicants and Evidence of English Language Proficiency .

*The GRE is no longer required nor accepted.

Research Areas

You can apply for the M.S., M.S./Ph.D., or Ph.D. program in either Electrical Engineering (EECS) or Computer Science (CS) .  To apply you will need to choose a division.  The table below shows which specialization areas fall under the EE division, under the CS division, and under both.

**Please note that on the official Grad Division application page EE is referred to as Electrical Engineering and Computer Sciences (EECS) and CS is referred to as Computer Science (CS) .  EE applicants must make sure to apply to EECS, and CS applicants make sure to apply to CS.

Electrical Engineering

Information, Data, Network, and Communication Sciences (IDNCS)

Computer Vision and Artificial Intelligence (CVAI)

Control, Intelligent Systems, and Robotics (CIR)

Integrated Circuits (INC)

Micro/Nano Electro Mechanical Systems (MEMS)

Physical Electronics (PHY)

Signal Processing (SP)

Biosystems & Computational Biology (BIO)

Computer Architecture & Engineering (ARC)

Cyber-Physical Systems and Design Automation (CPSDA)

Energy (ENE)

Computer Science

Artificial Intelligence (AI)

Database Management Systems (DBMS)

Education (EDUC)

Graphics (GR)

Human-Computer Interaction (HCI)

Operating Systems & Networking (OSNT)

Programming Systems (PS)

Scientific Computing (SCI)

Security (SEC)

Theory (THY)

Admissions Checklist

The current application period is closed.  The next application period will open in early September 2022.

• Open an online application.
• Proof of English Proficiency – If your previous degree is from a university in a non-English speaking country, then you need to take the TOEFL or IELTS exam by November. TOEFL scores will remain valid for up to two years .  Send your scores electronically to Institution Code 4833.
• Statement of Purpose – Why are you applying for this program? What are your research goals and interests? What do you hope to accomplish in this degree program? What do you want to do after and how will this help you?
• Personal History Statement – What from your past made you decide to go into this field? And how will your personal history help you succeed in this program and your future goals?
• 3 Letters of Recommendation – Letters from professors are highly preferred. Letters could include details about your goals, research accomplishments, technical and leadership skills, academic work, etc. We suggest you give your recommenders at least two months to write your letters and upload them as PDFs through the link (provided from your online application).
• Unofficial Transcripts – Upload unofficial copies/PDFs of your transcripts from all the higher education institutions you attended. Hard copy or emailed transcripts will not be accepted. Transcripts not in English or Spanish must be translated prior to uploading to the application. If you graduated from a university outside of the U.S. you should also upload a copy of your degree certificate if your transcripts do not show the award of your degree.
• GPA – If you attended a university that does not use a 4.0 GPA scale, please enter your GPA as listed on your transcript in the section “Other Scale GPA.”
• Resume/CV – List any education, work, research, volunteer experience, awards, or future coursework.
• Pay the application fee. Eligible US citizens and Permanent Residents may apply in advance for application  waivers .
• Submit your completed application by the deadline. To avoid any technical issues, we recommend submitting your application well before the deadline. Late applications or materials will not be accepted.

Please review our Frequently Asked Questions for more information.

Master of Science (M.S.) Only*

Full-time Commitment: ~2 years

Focus: Leads to career in industrial R&D or Ph.D.

The MS only degree is intended mostly for currently registered UC Berkeley Ph.D. students that want to add the degree. Occasionally we admit exceptional applicants with research experience, but the cohort generally is limited to less than 10. Students interested in a research oriented degree should consider applying directly to the MS/PhD program.

If you are planning to immediately join the engineering profession without pursuing a Ph.D., the Master of Engineering (M.Eng.) is specifically designed as a professional master’s degree.

Doctor of Philosophy (Ph.D.) Only

Full-time Commitment: 5-6 years (starting with a Bachelor’s),  3-5 years (starting with a Master’s)

Focus: Research + teaching experience

A Ph.D. in EECS combines coursework and original research with exceptional faculty mentoring to  prepare students for a career in academia or industry.

The EECS Department offers two types of Ph.D. degrees (EE and CS). The principal requirements for the Ph.D. are:

• coursework (a major field and two minor fields)
• departmental preliminary requirement (an oral exam and breadth courses, which are different for EE and CS)
• the qualifying exam
• the dissertation

* The Master of Science (M.S.) Only program is a very small research program for exceptional applicants with research experience.  Applicants should consider applying to the MS/PhD program, since there are very few students in the M.S. only program.

Equal Access to Application Assistance Program

The student-run Equal Access Assistance (EAAA) program aims to ensure that all applicants to higher degree programs (M.S./Ph.D.) at Berkeley EECS have access to guidance on the higher degree application process. One current (or recent graduate) in EECS will provide feedback on your statement of purpose, personal history statement, CV/resume, and other application materials in advance of higher-degree application deadlines in the fall semester.  This feedback will be Berkeley- and admissions-focused, as opposed to grammar or formatting advice.

Application for admission to Berkeley EECS is a process that must be completed separately; participation in EAAA does not guarantee admission nor affect the admissions decision-making process in any way.

More information about the EAAA

Biostatistics PhD

Many issues in the health, medical and biological sciences are addressed by collecting and exploring relevant data. The development and application of techniques to better understand such data is a fundamental concern of our program.

This program offers training in the theory of statistics and biostatistics, computer implementation of analytic methods and opportunities to use this knowledge in areas of biological/medical research. The resources of Berkeley Public Health and the UC Berkeley Department of Statistics, together with those of other university departments, offer a broad set of opportunities to satisfy the needs of individual students. Furthermore, the involvement of UCSF faculty from the Department of Biostatistics and Epidemiology also enriches instructional and research activities.

A PhD degree in Biostatistics requires a program of courses selected from biostatistics, statistics, and at least one other subject area (such as environmental health, epidemiology, or genomics), an oral qualifying examination, and a dissertation. Courses cover traditional topics as well as recent advances in biostatistics and statistics. Those completing the PhD will have acquired a deep knowledge and understanding of the MA subject areas. Since graduates with doctorates often assume academic research and teaching careers, a high degree of mastery in research design, theory, methodology, and execution is expected, as well as the ability to communicate and present concepts in a clear, understandable manner.

The PhD degree program requires 4–6 semesters of coursework, the completion of the qualifying examination and dissertation (in total, a minimum of four semesters of registration is required). Since there are no formal course requirements for the PhD, a program of courses appropriate to a student’s background and interests may be developed with a graduate adviser.

Qualifications

A Master’s degree in Biostatistics or a related field is recommended but not required for admission to the PhD program. Strongly recommended prerequisite courses are calculus, linear algebra, and statistics. Applicants admitted without a Master’s degree may be required to go through the Biostatistics MA curriculum; students can concurrently earn that degree with no additional cost or time to degree. Normative time to degree is 5 years.

Students entering with a relevant master’s degree in biostatistics or statistics must have a faculty advisor who is a member of the Biostatistics Graduate Group committing funding and mentorship support.

GRE Exemption Criteria

GRE General Test scores are required for admission to the Biostatistics PhD program however applicants are exempted from the requirement if they meet all of the following criteria:

• Completed two semesters of calculus for a letter grade and earned a grade of “B” or higher.
• Completed one semester of linear algebra for a letter grade and earned a grade of “B” or higher.
• Completed one semester of statistics for a letter grade and earned a grade of “B” or higher.
• Cumulative undergraduate GPA of 3.0 or higher.
• Overall quantitative/math GPA of 3.0 or higher.
• For students with a Master’s in Biostatistics or a related field, graduate GPA of 3.0 or higher.
• For international students: TOEFL score of 100 or higher OR IELTS score of 7.0 or higher.

Berkeley Public Health also exempts applicants who already hold a doctoral level degree from the GRE requirement.You can find more information on the application instructions page . There is a program page in the Berkeley Graduate Application where you can indicate you meet the criteria for GRE exemption. Applicants who are exempted from the GRE are not at a disadvantage in the application review process.

Many doctoral graduates accept faculty positions in schools of public health, medicine, and statistics and/or math departments at colleges and universities, both in the United States and abroad. Some graduates take research positions, including with pharmaceutical companies, hospital research units, non-profits, and within the tech sector.

Funding and Fee Remission

Prospective students who are US citizens or permanent residents can find more information about applying for an application fee waiver for the Berkeley Graduate Application. Fees will be waived based on financial need or participation in selected programs described on the linked website. International applicants (non-US citizens or Permanent Residents) are not eligible for application fee waivers.

All PhD students are fully funded (including tuition and fees and a stipend or salary) with the exception of Non-Resident Supplemental Tuition (NRST) for the second year, if applicable. NRST is typically waived after the first year of study for PhD students when they advance to candidacy. Information on applying to GSI positions for biostatistics students can be found in the Biostatistics Division student handbook .

Tuition and fees change each academic year. To view the current tuition and fees, see the fee schedule on the Office of the Registrar website (in the Graduate: Academic section).

Please contact [email protected] if you have any questions about funding opportunities for the biostatistics programs.

Diversity, Equity and Inclusion

The Division of Biostatistics is committed to challenging systemic inequities in the areas of health, medical, and biological sciences, and to advancing the goals of diversity, equity, and inclusivity in Biostatistics and related fields.

Diversity, Equity and Inclusion in Biostatistics

Admissions Statistics

Emeritus faculty, faculty associated in biostatistics graduate group.

• Peter Bickel PhD Statistics
• David R. Brillinger PhD Statistics
• Perry de Valpine PhD Environmental Science, Policy, and Management
• Haiyan Huang PhD Statistics
• Michael J. Klass PhD Statistics
• Priya Moorjani PhD Molecular & Cell Biology
• Rasmus Nielsen PhD Integrative Biology and Statistics
• Elizabeth Purdom PhD Statistics
• Sophia Rabe-Hesketh PhD Education
• John Rice PhD Statistics
• Yun S. Song PhD Statistics; Electrical Engineering and Computer Sciences
• Bin Yu PhD Statistics

UC stirs furious debate over what high school math skills are needed to succeed in college

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Briana Hampton, a San Gabriel High School junior, is determined to get into a four-year university to achieve her dream of becoming a social worker or psychiatrist. But she feared she would fail a third-year math course heavy on advanced algebra.

To meet her math requirement, she opted instead for an introductory data science course, approved a few years ago by the University of California as an alternative to advanced algebra. She loves the challenge of learning how to code, conduct surveys and analyze data on topics relevant to her life — sleep hours, stress levels, snacks consumed. She’s also boasting a B average in the class, compared to the Ds and Fs earned in her first-year math class.

“I’ve always struggled with math, but I heard that [data science] was like a really good class and something new and easier than algebra,” Briana said.

But the data science option is gone, at least for now. Last month UC notified California high schools that three of the most popular data science courses no longer count toward the advanced math requirement because the classes fail to teach the upper level algebra content all incoming students must know.

The decision has ratcheted up math anxiety and fomented confusion among high school students throughout California as they chart their high-stakes path for coveted UC admissions. California high schools offering data science classes — about 435 across the state — are also uncertain over how to revise curriculum and counsel their students.

UC first approved a data science course submitted in 2013 by Los Angeles Unified as a move to expand math options for students. At the time, the decision drew little notice. Over the years, efforts spread to increase data science in schools. After the subject was given a more prominent place in California’s new state framework for math instruction adopted last year, UC reviewed its years-old decision.

Faculty math experts concluded the course and two others were too weak on algebra and nixed them as an advanced math alternative. The decision also applies to the California State University system, which shares coursework admission requirements with UC.

The turnabout has unleashed furious debate over what high school math skills are needed to succeed in college — and how best to deliver them equitably to a diverse range of students. Currently, UC and CSU require for admission a three-year sequence of algebra, geometry and advanced algebra — or the equivalent integrated math courses. Both also recommend a fourth year of mathematics beyond those foundational skills.

Where data science fits in and what advanced math courses need to include to meet admission requirements is hotly contested. The UC Board of Regents will take up the issue at its meeting Wednesday and has the authority to overrule the faculty decision.

Opponents of UC’s reversal argue that data science courses give students essential skills to extract meaning from the modern world’s information deluge. They also offer an alternative path to college for those who may struggle with algebra and don’t plan to pursue calculus and STEM majors. About 44% of high school seniors fail to complete two semesters of advanced algebra, according to UC.

Denise Jaramillo, Alhambra Unified School District superintendent, said she was concerned by the UC decision because many of her students have taken the now-disallowed data science courses and have gone on to succeed in college. The district has used a course developed by UCLA since 2017.

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“Limiting student options and choices has never been an equitable practice in schools or the classroom, and doing so has the potential of placing historically marginalized groups at a great disadvantage after high school,” Jaramillo said in a statement.

But supporters of the UC decision counter that all students should be equipped with advanced algebra skills and not tracked into set pathways at such young ages — especially Black, Latino, female and others who are underrepresented in the high-demand, high-paying fields of science, technology, engineering and math.

“Students from underrepresented groups are the most vulnerable to make misinformed pathway choices in high school that could lead them away from preparedness for quantitative majors,” said Jelani Nelson, a UC Berkeley professor of electrical engineering and computer sciences.

Nelson, who is Black, has been particularly outspoken in the debate because he said “misleading marketing” from some data science courses claim they prepare students for STEM coursework and data science majors. But, he said, hundreds of faculty members, along with Elon Musk and industry leaders from OpenAI, Apple, Microsoft, NVIDIA, Google Research and other firms, agree that advanced algebra is essential for those fields.

Jennifer Chayes, dean of UC Berkeley’s new College of Computing, Data Science and Society , said “thousands” of students change their minds about majors once they come to campus. Many intend to study such fields as environmental science, health or criminal justice — then discover an affinity and aptitude for data science and other STEM disciplines and need the math skills for them.

“People are pitting this as data science versus algebra, but that’s really not right,” Chayes said. “It’s data science using algebra 2 that we’re really going to want to do.”

Nelson added that efforts are underway to develop data science courses that include advanced algebra — including one by Bootstrap, an educational nonprofit — and he and other STEM professionals welcome such moves.

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James Steintrager, chair of the UC Academic Senate, said the faculty organization is open to appropriate high school data science courses counting for admission in some way.

“There’s nothing wrong with data science, per se,” he said. “But if it’s not rigorous enough, you’re not preparing UC students, including diverse UC students, for the variety of careers, STEM careers in particular, that they could have. They’re not going to thrive. So you’re really doing those students a disservice.”

A letter to Board of Regents Chair Rich Leib signed by more than 230 school, district and county education leaders castigated UC for the flip-flop, saying they were shut out of the decision, yet have to manage the impact, including a “significantly” reduced ability to teach 21st century data and statistical skills. In another letter Monday to Leib, a University of Chicago data science advocate said the bitter California conflict has reverberated nationally.

The uncertainty over the future of data science courses may cause districts to pause or shut down programs — as San Diego Unified has done — cut resources for teacher professional development, and lead students to believe the field is different from math, said Zarek Drozda, executive director of Data Science 4 Everyone. His group concurred that the current data science courses don’t cover advanced algebra, but believed they can and should be allowed as a fourth-year math option.

Fewer than 400 of about 250,000 applicants to UC last year had taken data science or statistics instead of an algebra 2-type course. CSU data were not available. But as both public university systems collectively enroll the top one-third of California high school graduates, it is likely that the overwhelming majority of applicants take advanced algebra or its equivalent. About two-thirds of California high school students have taken algebra 2 by 11th grade, according to Michal Kurlaender, a UC Davis professor of education policy.

Hollywood High School, for instance, directs all students to complete the three-year math sequence ending in advanced algebra and take data science as a fourth year math option. Securing approval for data science and statistics courses to count for that fourth year of math recommended by UC and CSU is the lobbying focus by the Campaign for College Opportunity and other equity advocates.

But the ramifications of any UC action on the issue could be far-reaching even for students who don’t plan to apply to UC and CSU. Los Angeles Unified, for instance, requires all students to complete that college-prep coursework to graduate. In a districtwide memo issued last spring, L.A. Unified told schools that the data science course it had piloted could substitute for advanced algebra.

LAUSD initiated data science courses

In 2013, LAUSD became the first in California to seek and receive UC approval to offer a data science course.

L.A. schools Supt. Alberto Carvalho called the situation an “age-old dilemma in mathematics regarding the theory of mathematics versus the application.” The data science course includes “sufficient algebraic thinking and operations to satisfy the practicality of mathematics, but may, in fact, fall short of a true course in algebra that provides the A through Z of all algebraic concepts,” he said Tuesday.

School board member Nick Melvoin said the debate underscores the need for better early math instruction. Board members Jackie Goldberg and Tanya Ortiz Franklin support data science, but not at the expense of mathematical rigor.

The district has yet to provide information requested three weeks ago about how many schools currently offer the introductory data science course and how many students had taken it in lieu of advanced algebra.

Robert Gould, a UCLA teaching professor and undergraduate vice chair in the Department of Statistics and Data Science, led efforts to create coursework as part of a National Science Foundation-funded project to help students develop computational thinking and increase engagement in STEM studies. Initial four- to six-week programs that were developed using data in algebra and biology were too short for meaningful learning, Gould said.

But the move to establish new statewide learning standards, part of a national effort, offered an opportunity to create a year-long course because the new “common core” put more emphasis on statistics. UC approved the data science course in the statistics category. The university listed statistics as an allowable substitute for algebra 2, according to a 2013 UC document.

Data science scrutinized

Gould said the 2020 release of the initial draft of the state guidance for math instruction, known as the California Math Framework, intensified scrutiny because of an emphasis on data science courses. He joined a UC committee that recommended data science courses should be allowed to count for third- and fourth-year math — and the UC faculty board overseeing admission approved the recommendation in October 2020. A year later, a faculty board statement on math included sample course sequences that showed data science and statistics following algebra and geometry instead of algebra 2.

But CSU faculty objected.

In March 2023, the CSU Academic Senate approved a resolution saying Gould’s course represented “inadequate preparation for college and career readiness” that could put 11th-graders at risk in state math testing and “in turn threatens to increase the number of students entering the CSU who are identified as needing extra support to succeed.”

Rick Ford, who headed a CSU faculty committee on academic preparation at the time, said concerns about racial and ethnic disparities in math were well-meaning but misplaced.

“A major issue that I share with many of my equity-minded mathematics education colleagues is that too often we are seeing folks wanting to change standards or curriculum to address the ethnic and racial differentials in success in foundational high school mathematics courses,” he said in an email. “Instead, we should be focusing our efforts on building the proper support necessary to improve those success rates.”

The issue came to a head in July 2023, as state education officials prepared to vote on the new math framework that included data science as an alternative pathway. Amid ferocious lobbying on all sides, the UC faculty board voted to reverse the earlier approval of data science courses.

A working group, appointed to delve into the issues, affirmed the turnabout last month and UC then notified high school counselors.

Gould said he disagrees with conclusions that his course fails to prepare students for college. Still, he said he would do “what it takes” to revise the course as needed to make sure students can continue to gain exposure to data and statistical skills. Today, 7,681 California students are taking his course in 89 schools, along with thousands more in other states and countries.

Inside a data science class

Nearly 100 of those students attend San Gabriel High — and judging by a recent visit, love the data science course.

On this day, they are delving into data on whether females or males die more often in horror slasher films. Leah Ulloa Ruiz, the teacher, leads her class through data files of 485 characters in 50 films. She peppers them with questions and directs them to use coding to find answers.

Ruiz said many of her students say they hate math or don’t understand it. But data science lights them up, gives them confidence and teaches them the importance of data and statistical skills. The UC decision to not count data science as an advanced mathematics course for admission, she said, is wrong.

“I really think it’s a shame and a step backwards,” she said. “A student can be successful in life with a career, job and money without taking algebra 2 or integrated math 3.”

Two of her students, Noah Minchaca and Valentina He, both struggled with algebra-heavy integrated math courses. Valentina failed her second-year course and had to repeat it in the summer, while Noah managed to pass it but said he spent most of the time in class with his head on his desk, napping. He dropped out of his third-year course after two weeks.

“You can’t understand it,” Noah said of his integrated math course. “You keep seeing Ds on your tests and ask, ‘Am I dumb?’ Your self-esteem is blown. And it’s useless, just equations.”

“Yeah, polynomials — you can’t use it in the future,” Valentina said.

But the students said the data science course has taught them skills they expect to use in the future. Noah already has been accepted into an architecture program with Glendale Community College and San Diego State University, while Valentina is thinking about a trade school for training as a medical assistant.

Briana, meanwhile, won’t have to take the third-year integrated math course she skipped in favor of data science since the UC ruling won’t affect students until next year. That’s a relief since she’s planning to apply to both CSU and UC, although her first choice is a historically Black college. But she feels for students who follow her.

“That really sucks,” she said. “I know a lot of kids here are really bad at math and struggle with math. And they’re kind of being forced into a class that I’m not sure it’s gonna help them succeed.”

Staff writer Howard Blume contributed to this report.

More to Read

Editorial: Not every student needs Algebra 2. UC should be flexible on math requirement

March 28, 2024

Letters to the Editor: Hey, UC, who has ever thought, ‘I sure wish I had taken more algebra’?

March 22, 2024

Editorial: Why it’s smart for universities to bring back the SAT requirement

March 17, 2024

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Teresa Watanabe covers education for the Los Angeles Times. Since joining the Times in 1989, she has covered immigration, ethnic communities, religion, Pacific Rim business and served as Tokyo correspondent and bureau chief. She also covered Asia, national affairs and state government for the San Jose Mercury News and wrote editorials for the Los Angeles Herald Examiner. A Seattle native, she graduated from USC in journalism and in East Asian languages and culture.

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Project Jupyter Recognized by White House

The White House Office of Science & Technology Policy recently announced the year of  Open Science Recognition Challenge winners , including  Project Jupyter , an open-source computing platform co-founded by Associate Professor Fernando Pérez. Project Jupyter is a non-profit, open-source project that evolved from the  IPython Project  in 2014 to support interactive data science and scientific computing across all programming languages. 

Pérez serves as an Associate Professor in the Department of Statistics, where his research focuses on creating tools for modern computational research and data science across domain disciplines, emphasizing high-level languages, interactive and literate computing, and reproducible research. Pérez also serves as Faculty Director for the  Berkeley Institute for Data Science  (BIDS). Through tools like IPython and Project Jupyter, Pérez builds foundational blocks that enable scientists to tackle all stages of computational research (from exploration through publication) with a coherent approach, thus improving scientific productivity, collaboration, and reproducibility. A native of Medellín, Colombia, he earned his BSc in Physics from the University of Antioquia and a PhD in particle physics from the University of Colorado Boulder.

Statistics staff member  Ryan Lovett  '99 is a Jupyter Distinguished Contributor (JDC). Lovett serves as Director of Computing, overseeing the Statistical Computing Facility (SCF).

-Alex Coughlin

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• Check out our YouTube , Instagram , and online brochure where we highlight FPF instructors and the FPF student experience
• Review our Frequently Asked Questions

Innovation & Entrepreneurship

• Whether you are simply entrepre-curious or ready for another round of funding, visit the BEGIN site to see all the UC Berkeley resources available to you.
• Get inspired by stories of how Berkeley innovators change the world.
• Please contact us at [email protected] to learn more. Go to https://iande.berkeley.edu/ie-prospective-new-students for more information and a virtual tour.

L&S First-Year Pathways

[email protected]

L&S First-Year Pathways is an exciting new program for first-year undergraduate students at UC Berkeley. It consists of three courses — linked by a common theme — that you take in your first semester at Berkeley. By combining interdisciplinary course clusters and immersive learning within a small cohort experience, Pathways helps new students successfully transition to life at Berkeley. Pathways provides:

• Early Class Enrollment: Incoming first-year students will have access to priority enrollment into three courses
• Small Class Experience: Exciting small-class experiences with a professors renowned in their fields
• New Friendships: A chance to make friends with other new students through shared personal interests
• Degree Progress: < Progress through the L&S breadth requirements in a topic that interests you

To learn more about L&S First-Year Pathways:

• Bookmark and visit lsfirstyear.berkeley.edu for the latest information and updates
• Watch the L&S First-Year Pathways video featuring students from Fall 2023
• Look for us at Cal Day!

Student Tech Services

• Long-term laptop and accessory loans for students with financial need ( STEP )
• Free on-campus tech support for all students
• Opportunities to work in IT departments across campus

• Meet CED Alums
• Work at CED
• Undergraduate Majors + Minors
• Graduate Programs
• Degrees + Certificates
• Summer Programs
• Study Abroad
• Undergraduate Admissions
• Graduate Admissions
• Fees + Financial Aid
• CED Undergraduate Advising
• Graduate Advising
• Centers & Institutes
• Climate Solutions
• Equity + Social Justice
• Technology + Material Innovations
• Publications
• Research Collaborations
• Environmental Design Archives
• Student Work
• Student Organizations
• Student Support
• Building Safety
• Student Fees and Waivers
• Fabrication + Materials
• IT + Computing
• Environmental Design Library
• Facility Services
• Awards, Scholarships and Fellowships
• Careers & Work Opportunities
• Accreditation and Licensure
• Bachelor of Arts
• Minor in Environmental Design and Urbanism in Developing Countries
• Minor in the History of the Built Environment
• Minor in Social and Cultural Factors in Environmental Design
• Minor in Sustainable Design

Master of Architecture (MArch)

Master of advanced architectural design (maad).

• Master of Science
• Concurrent Programs
• 2024 Spring Courses
• 2024 Summer Courses
• 2024 Fall Courses
• + About LAEP
• Minor in Landscape Architecture
• Master of Landscape Architecture

Concurrent Degrees

• + About DCRP
• Master of City Planning
• Bachelor of Arts in Urban Studies
• Faculty Work
• Studio Work
• + About IURD
• About MRED+D
• For Students
• News + Events
• Research + Design
• Student Resources

As a Berkeley graduate student, you’re part of a diverse community of change-makers known worldwide for innovation and academic excellence. At the College of Environmental Design, we’re committed to launching our students to be agents of environmental and social change, poised to make a tangible impact on our world. We offer six professional degrees and five academic graduate degrees, as well as opportunities for earning concurrent degrees within the college and with other Berkeley departments. Whichever you choose,  your CED degree will set you the path to succeed in your chosen field.  

Professional Degrees 

Our top-ranked, rigorous MArch prepares you for the practice of architecture in an environment that fosters design excellence and encourages multidisciplinary collaboration and experimentation. The nationally accredited professional program is committed to addressing the most pressing issues of our time, including the climate crisis, housing affordability, and the integration of technology into both research and practice. As an MArch student, you’ll be part of an international cohort of motivated and talented design students from diverse backgrounds and work with faculty who are leaders in their fields.

Master of City Planning (MCP)

Berkeley’s top-tier MCP is a two-year nationally accredited STEM program that empowers you to successfully practice planning in a variety of urban, metropolitan, and regional settings. As an MCP student, you’ll develop analytical, research, and communication skills, as well as expertise in various fields and subfields of city and regional planning. Berkeley’s MCP is distinguished by its focus on the human impacts of planning decisions, with particular attention to equity, diversity, and social justice.

Master of Landscape Architecture (MLA)

Berkeley’s MLA program is known for centering ecological integrity and social equity. It offers accredited degrees in landscape architecture and environmental planning, which focuses on land use and the management of natural resources. You’ll study with faculty who have expertise in design, planning, urbanism, history, sustainability, hydrology, ecology, geographic information science, landscape modeling, and social practices. Your MLA will prepare you to serve as a leader in the field, building thriving communities in a world of rapid political, economic, and environmental change.

Master of Urban Design (MUD)

Berkeley’s MUD, the only degree program of its kind in California,  is a one-year interdisciplinary program of advanced study for architects, landscape architects, and planners who already hold professional degrees. As a MUD student, you’ll innovate design strategies for sites at the urban, suburban, and territorial scales and develop a practice supported by research and analysis that is grounded in economic, political, and social contexts.

Abbey Master of Real Estate Development + Design (MRED+D)

Integrating design with the fundamentals of real estate markets, the one-year Abbey MRED+D prepares you to build sustainable, equitable, and prosperous cities. The curriculum focuses on finance, urban economics, property and land use law, and project feasibility analysis, as well as  excellence in urban design, planning, and sustainability. As an MRED+D student, you’ll discover how  design thinking is foundational to producing the most valued — and valuable — real estate projects.

Master of Design (MDes)

A three-semester professional graduate program jointly offered with the College of Engineering, the MDes prepares you to be a thought leader in the area of emerging technologies like artificial intelligence, virtual reality, and the internet of things. You’ll develop core skills in design process, prototyping, and communication, as well as a critical lens on technology and design. You’ll leave the program as an engaged and responsive designer with an informed awareness of people, contextual needs, and societal impacts.

Academic Degrees

Master of science in architecture (ms).

Our MS in Architecture is an academic, nonprofessional degree program that offers you the opportunity to engage in advanced research in the ever-broadening and increasingly complex subfields within architecture. Two tracks are offered:  Building Science, Technology and Sustainability , focused on how the environmental conditions and processes of buildings affect occupants, and History, T h eory and Society , which situates a history of the built environment in broad social, political, and cultural frameworks.

If you already have a professional degree in architecture and wish to continue to explore current design issues in a stimulating, rigorous, and experimental studio setting, our one-year MAAD post-professional program is for you. A year-long studio is at the core of this non-accredited program, and it’s integrated with seminars and lectures in design theory, history, digital applications, and building technology.

PhD in Architecture

Berkeley’s PhD program in architecture is interdisciplinary in outlook, reaching into the various disciplines related to architecture and incorporating substantial knowledge from outside fields. You have the option to choose between two areas of focus: Two tracks are offered:  Building Science, Technology and Sustainability , focused on how environmental conditions and processes of buildings affect occupants,  and  History, Theory and Society , which situates a history of the built environment in broad social, political, and cultural frameworks.

PhD in City & Regional Planning

The PhD in City & Regional Planning provides training in urban and planning theory and the practice of planning. You’ll work with faculty who have expertise in community and economic development, transportation planning, urban design, international development, environmental planning, and global urbanism. 

PhD in Landscape Architecture & Environmental Planning

Berkeley’s PhD in Landscape Architecture & Environmental Planning focuses on the theories and methods that underlie the field and the processes of planning and design as they relate to the solution of problems in the natural and urban environments. 

Concurrent Master of Architecture/Master of City Planning

Concurrent Master of Architecture/Master of Landscape Architecture

Concurrent Master of Architecture/ Master of Science in Engineering

Concurrent Master of City Planning/Master of City Landscape Architecture

Concurrent Master of City Planning/Master of Public Health

Concurrent Master of City Planning/Master of Science in Transportation Engineering

Concurrent Master of City Planning/Juris Doctor (Law)

• Admitted Students

What is IEOR?

Faculty spotlights.

• Alumni Profiles
• What IEOR Students Are Saying
• Campus Tour

Explore More

Admitted undergraduate students, welcome to cal.

Congratulations! Your acceptance to UC Berkeley marks an exciting milestone in your academic journey, and we are delighted to welcome you to our community. We eagerly anticipate the remarkable achievements you will accomplish during your time here.

Now that you're in, it's time to start planning for your future with us. Below, you'll find essential information and next steps to help you prepare for your arrival on campus. Be sure to keep an eye out for updates on summer and fall programming, as we will provide additional details and deadlines.

Dear Newly Admitted Undergraduate Students,

Congratulations on being accepted to the University of California, Berkeley Department of Industrial Engineering & Operations Research! Being admitted to Cal means that you have already triumphed through challenges and excelled in many ways.

It’s truly an exciting time to join the IEOR department. Since its establishment in 1966, our department has been at the forefront of IE/OR research and education, with some of the most successful, world-renowned alumni graduating from our program.

Our alumni embark on exciting, rewarding paths in academia and industry. Picture yourself as a professor leading a large research program at a top university making groundbreaking discoveries that can benefit future generations. Or envision contributing to a tech giant, analyzing pricing data, or streamlining warehouse operations with cutting-edge optimization and machine learning techniques.

Nestled in the vibrant San Francisco Bay Area, UC Berkeley's IEOR department stands at the forefront of innovation and entrepreneurship. Amidst this hub of creativity, the demand for data-driven, AI-powered decision-making is surging across industries, from e-commerce to healthcare and beyond. Companies and governments alike are leveraging data to adapt swiftly, manage costs, mitigate risks, and enhance operational efficiency.

With a focus on optimization, stochastics, and data analytics, our IEOR faculty and students are creative innovative solutions for a myriad of sectors, including healthcare, supply chains, energy, finance, and risk management.

Feel free to contact me or our staff with any questions about the program. During the upcoming Cal Day, we'll showcase the diverse opportunities at Berkeley IEOR. We hope you'll join us in accepting your admission to Cal, and we eagerly anticipate welcoming you in the Fall!

Go Bears! Alper Atamturk Professor and IEOR Department Chair

Here's what do next

• Learn more about academic opportunities, extracurricular activities and student life at Berkeley. Check out the  Events Page .
• Attend Cal Day!   Join us for our annual open house where the Berkeley community welcomes you, our newly admitted students and families. YOU ARE BERKELEY
• Confirm your acceptance : Are you ready to commit to Berkeley? Go to  MAP@Berkeley to formally accept your offer of admission. Confirm your enrollment before April 15 for admitted first-year students and June 1 for admitted transfer students.

In this award-winning video , Berkeley IEOR students explain what industrial engineering & operations research is and how their skills make an impact and improve the world.

Supply Chain & Logistics

IEOR students are the premier experts in getting anything from one place to another in the most efficient and optimal manner.

Stochastics & Data Science

IEOR students learn the latest statistics, artificial intelligence and machine learning algorithms to help companies make better  decisions.

Optimization & Algorithms

IEOR students can find the most elegant way to make any system more efficient, saving companies millions of dollars, time and other resources.

IEOR graduates lead the way in lucrative careers

Recently, CNBC featured IEOR as the second highest-paying college major. A separate report from the HEA group also identified operations research as the most financially rewarding path for graduates. This positions our alumni for success in a competitive job market and underscores the tremendous potential that awaits our graduates, especially with a projected 23% increase in positions for operations research analysts.

IEOR In the News:

IEOR Degree Holds a Top Spot Among Highest-Paying Majors

• Berkeley IEOR Graduates Lead the Way in Lucrative Careers: Report Highlights Operations Research as Highest-Earning Field

By the Numbers

73% of our students secure full-time employment by graduation, and 11% pursue graduate school as part of their post-graduation plans.

$100,000 average starting salary for undergraduates.

11:1 student-to-faculty ratio allows IEOR students to build strong relationships with professors who go above and beyond to ensure students get the most from their classes.

CalDay Program

Saturday, April 13 9 AM to 4 PM UC Berkeley Campus

Imagine seeing the most exciting, outrageous, informative aspects of Berkeley and the College of Engineering, all in one day. It’s called Cal Day, an extravaganza of events that showcase the brilliance and fun of Berkeley.

Cal Day 2024 , on Saturday, April 13, is only for newly admitted students and their families. We are looking forward to getting to know you and helping you get to know us. Join us from 9 a.m.–4 p.m. for a full day of events, exploration, and interactions with engineering students, advisers and faculty.

At Berkeley, you'll be part of a dynamic global network that spans over 500,000 UC Berkeley alums across 180 countries. Benefit from valuable connections, mentorship, and knowledge sharing within our thriving community and engage with industry leaders seeking to leverage UC Berkeley's top talent in analytics. Our students have successfully secured internships and full-time positions with renowned companies such as Tesla, Deloitte, HP, KONE, and many others.

IEOR Alumni Profiles

Andrew Laffoon

Courtney Z. Chow

Lucie Kresl

Vishrut Rana

Ieor alumni news.

Berkeley IEOR Alum Sonita Lontoh Featured in Manufacturing Today Magazine

Celebrating Valentine’s Day with Berkeley IEOR Alums Steve Tai and KC Wu

Ask an Alum: Jack O’Donoghue, MAnalytics ’23

5 Questions for Anika Ramachandran, Class of 2022

Paul Grigas

Assistant professor.

"Berkeley IEOR gives me the opportunity to work with world-class students and colleagues on challenging, exciting, and impactful projects at the forefront of research in machine learning and optimization."

Rhonda Righter

Professor and head graduate advisor.

"We have great students - curious, passionate, hard-working! Berkeley's an amazing institution that excels in education and research, yet still prioritizes concerns for society and the environment. IEOR at Berkeley is even more amazing - a small, tight-knit community within this great institution."

Student Spotlights

Rishika gorai, bs ieor 2026.

Berkeley IEOR has helped me shine brighter by giving me many opportunities to explore future career paths. Last summer, I was able to complete a summer internship at Yukka Lab in Berlin, Germany as a Data Intelligence & Business Development intern. This past fall, I acquired a summer internship at a Big 4 consulting firm & this semester, I am interning as a Product Management & Business Development intern at Daicus Technology. The diverse opportunities & overarching curriculum that IEOR offers definitely aided me in gaining such helpful experiences to learn about my interests & strengths in depth.

Jelo Francisco

Bs ieor 2024.

"Transferring into IEOR has been one of the most rewarding decisions in my life. The program's academic rigor, dedicated faculty and staff, and vibrant community have all equipped me with the confidence and skills to thrive in any career path I choose. With an ever-expanding toolbox of data-driven strategies to make decisions amid uncertainty, I feel empowered to tackle complex problems and create positive change on the global scale."

Meet Your Advisor

Ginnie Sadil

Meet your IEOR student services advisor, Ginnie Sadil.

Email her about any questions you have at [email protected] or you can   book  a virtual meeting time to chat online.

What Alums are Saying

Class of 2021.

I love the IEOR community. I've met amazing friends and love the warmth of the entire department. Particularly at such a large university, I've truly appreciated having a tight knit department to lean on for support throughout my years at Cal. - Sarina Xin

Class of 2022

I chose to study IEOR as it introduces me to tools and techniques for problem solving and allows me to explore some of the grandest challenges faced by our modern world. - Vishrut Rana

Class of 2023

The IEOR community welcomed me with open arms from my first Cal Day and it has been one of my favorite parts of Berkeley. From our friendly students, excellent faculty, and supportive staff, my Berkeley experience would not be what it is without the members of this amazing community. - Duncan Barcelona

Why Berkeley

Self-Guided Tour Instructions for Berkeley Campus

Social media, student life.

Campus Vibes

Berkeley and Beyond

Food and Fun

Music and arts, how to stay active.