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 two designated emphasis (DE) tracks available to students in the PhD program who wish to pursue interdisciplinary work formally: Computational and Data Science and Engineering and Computational and Genomic Biology.
Minimum Requirements for Admission
The following minimum requirements apply to all graduate programs and will be verified by the Graduate Division:
- A bachelor’s degree or recognized equivalent from an accredited institution;
- A grade point average of B or better (3.0);
- If the applicant has completed a basic degree from a country or political entity (e.g., Quebec) where English is not the official language, adequate proficiency in English to do graduate work, as evidenced by a TOEFL score of at least 90 on the iBT test, 570 on the paper-and-pencil test, or an IELTS Band score of at least 7 on a 9-point scale (note that individual programs may set higher levels for any of these); and
- Sufficient undergraduate training to do graduate work in the given field.
Applicants Who Already Hold a Graduate Degree
The Graduate Council views academic degrees not as vocational training certificates, but as evidence of broad training in research methods, independent study, and articulation of learning. Therefore, applicants who already have academic graduate degrees should be able to pursue new subject matter at an advanced level without the need to enroll in a related or similar graduate program.
Programs may consider students for an additional academic master’s or professional master’s degree only if the additional degree is in a distinctly different field.
Applicants admitted to a doctoral program that requires a master’s degree to be earned at Berkeley as a prerequisite (even though the applicant already has a master’s degree from another institution in the same or a closely allied field of study) will be permitted to undertake the second master’s degree, despite the overlap in field.
The Graduate Division will admit students for a second doctoral degree only if they meet the following guidelines:
- Applicants with doctoral degrees may be admitted for an additional doctoral degree only if that degree program is in a general area of knowledge distinctly different from the field in which they earned their original degree. For example, a physics PhD could be admitted to a doctoral degree program in music or history; however, a student with a doctoral degree in mathematics would not be permitted to add a PhD in statistics.
- Applicants who hold the PhD degree may be admitted to a professional doctorate or professional master’s degree program if there is no duplication of training involved.
Applicants may apply only to one single degree program or one concurrent degree program per admission cycle.
Required Documents for Applications
- Transcripts: Applicants may upload unofficial transcripts with your application for the departmental initial review. Unofficial transcripts must contain specific information including the name of the applicant, name of the school, all courses, grades, units, & degree conferral (if applicable).
- Letters of recommendation: Applicants may request online letters of recommendation through the online application system. Hard copies of recommendation letters must be sent directly to the program, by the recommender, not the Graduate Admissions.
Evidence of English language proficiency: All applicants who have completed a basic degree from a country or political entity in which the official language is not English are required to submit official evidence of English language proficiency. This applies to institutions from Bangladesh, Burma, Nepal, India, Pakistan, Latin America, the Middle East, the People’s Republic of China, Taiwan, Japan, Korea, Southeast Asia, most European countries, and Quebec (Canada). However, applicants who, at the time of application, have already completed at least one year of full-time academic course work with grades of B or better at a US university may submit an official transcript from the US university to fulfill this requirement. The following courses will not fulfill this requirement:
courses in English as a Second Language,
courses conducted in a language other than English,
courses that will be completed after the application is submitted, and
courses of a non-academic nature.
Applicants who have previously applied to Berkeley must also submit new test scores that meet the current minimum requirement from one of the standardized tests. Official TOEFL score reports must be sent directly from Educational Test Services (ETS). The institution code for Berkeley is 4833 for Graduate Organizations. Official IELTS score reports must be sent electronically from the testing center to University of California, Berkeley, Graduate Division, Sproul Hall, Rm 318 MC 5900, Berkeley, CA 94720. TOEFL and IELTS score reports are only valid for two years prior to beginning the graduate program at UC Berkeley. Note: score reports can not expire before the month of June.
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?
- GRE General Test Scores: The GRE is no longer required for applicants applying to the MA or PhD program. For the PhD program, while it is not required, if you wish to include your GRE Math Subject test you will have the option to do so.
- 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.
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:
|STAT 204||Probability for Applications||4|
|STAT C205A||Probability Theory||4|
|STAT C205B||Probability Theory||4|
|STAT 210A||Theoretical Statistics||4|
|STAT 210B||Theoretical Statistics||4|
|STAT 215A||Statistical Models: Theory and Application||4|
|STAT 215B||Statistical Models: Theory and Application||4|
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 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 units of courses in the department numbered 204-272 inclusive. 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. In addition, the committee member may waive part of this unit requirement.
The qualifying examination is intended to determine whether students are ready to enter the research phase and are on track toward successfully completing the PhD. It consists of a 50-minute lecture by the student on a topic selected jointly by the student and the thesis adviser. The topic usually involves the student's research.
Time in Candidacy
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
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.
Master's Degree 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.
|STAT 201A||Introduction to Probability at an Advanced Level||4|
|STAT 201B||Introduction to Statistics at an Advanced Level||4|
|STAT 243||Introduction to Statistical Computing||4|
|STAT 230A||Linear Models||4|
|STAT 222||Masters of Statistics Capstone Project||4|
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.
Faculty and Instructors
* Indicates this faculty member is the recipient of the Distinguished Teaching Award.
* Ani Adhikari, Teaching Professor.
Peter L. Bartlett, Professor. Statistics, machine learning, statistical learning theory, adaptive control.
Andrew Bray, Associate Teaching Professor.
James Bentley Brown, Assistant Adjunct Professor. Applications in Biology and Medicine.
Jennifer Chayes, Professor and Associate Provost and Dean, Division of Computing, Data Science, and Society. Phase transitions in computer science, structural and dynamical properties of networks, graphons, machine learning, ethical decision making, climate change.
Peng Ding, Associate Professor. Statistical causal inference, missing data, Bayesian statistics, applied statistics.
Sandrine Dudoit, Professor. Genomics, classification, statistical computing, biostatistics, cross-validation, density estimation, genetic mapping, high-throughput sequencing, loss-based estimation, microarray, model selection, multiple hypothesis testing, prediction, RNA-Seq.
Steven N. Evans, Professor. Genetics, random matrices, superprocesses and other measure-valued processes, probability on algebraic structures -particularly local fields, applications of stochastic processes to biodemography, mathematical finance, phylogenetics and historical linguistics.
Avi Feller, Associate Professor. Applied statistics, theoretical statistics, Bayesian statistics, machine learning, statistics in social sciences.
Will Fithian, Assistant Professor. Theoretical and Applied Statistics.
Shirshendu Ganguly, Associate Professor. Probability theory, statistical mechanics.
Vadim Gorin, Associate Professor. Integrable probability, random matrices, asymptotic representation theory.
Adityanand Guntuboyina, Associate Professor. Nonparametric and high-dimensional statistics, shape constrained statistical estimation, empirical processes, statistical information theory.
Alan Hammond, Professor. Statistical mechanics.
Erin Hartman, Assistant Professor. Causal inference and survey design and analysis.
Giles Hooker, Professor. Data analysis for dynamical systems and differential equations, machine learning and data mining, functional data analysis.
Haiyan Huang, Professor. Applied statistics, functional genomics, translational bioinformatics, high dimensional and integrative genomic/genetic data analysis, network modeling, hierarchical multi-lable classification.
Jiantao Jiao, Assistant Professor. Artificial intelligence, control and intelligent systems and robotics, communications and networking.
Michael I. Jordan, Professor. Computer science, artificial intelligence, bioinformatics, statistics, machine learning, electrical engineering, applied statistics, optimization.
Michael William Mahoney, Associate Adjunct Professor. Statistical Computing, Applications in the Physical and Environmental Sciences, Applications in the Social Sciences, High Dimensional Data Analysis, Artificial Intelligence/Machine Learning .
Jon Mcauliffe, Adjunct Professor. Bioinformatics, machine learning, nonparametrics, convex optimization, statistical computing, prediction, supervised learning.
Song Mei, Assistant Professor. Data science, statistics, machine learning.
Rasmus Nielsen, Professor. Statistical and computational aspects of evolutionary theory and genetics.
Christopher Paciorek, Adjunct Professor. Environmental statistics, statistical computing, spatial statistics, Bayesian statistics.
Fernando Perez, Associate Professor. High-level languages, interactive and literate computing, and reproducible research.
Sam Pimentel, Assistant Professor. Causal inference, health services & policy analysis, biostatistics, discrete optimization.
Elizabeth Purdom, Associate Professor. Computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics.
Juliet Shaffer, Teaching Professor Emerita.
Alistair Sinclair, Professor. Algorithms, applied probability, statistics, random walks, Markov chains, computational applications of randomness, Markov chain Monte Carlo, statistical physics, combinatorial optimization.
Yun Song, Professor. Computational biology, population genomics, applied probability and statistics.
Philip B. Stark, Distinguished Professor. Astrophysics, law, statistics, litigation, causal inference, inverse problems, geophysics, elections, uncertainty quantification, educational technology.
Jacob Steinhardt, Assistant Professor. Artificial intelligence, machine learning.
Bernd Sturmfels, Professor. Mathematics, combinatorics, computational algebraic geometry.
Ryan Tibshirani, Professor. High-dimensional statistics, nonparametric estimation, distribution-free inference, machine learning, convex optimization, numerical methods, tracking and forecasting epidemics.
Mark J. Van Der Laan, Distinguished Professor. Statistics, computational biology and genomics, censored data and survival analysis, medical research, inference in longitudinal studies.
Martin Wainwright, Professor. Statistical machine learning, High-dimensional statistics, information theory, Optimization and algorithms.
Bin Yu, Distinguished Professor. Neuroscience, remote sensing, networks, statistical machine learning, high-dimensional inference, massive data problems, document summarization .
Nikita Zhivotovskiy, Assistant Professor. Mathematical statistics, probability, and learning theory.
Thomas Bengtsson, Lecturer.
Fletcher H. Ibser, Lecturer.
Eaman Jahani, Lecturer.
Adam R. Lucas, Lecturer.
Libor Pospisil, Lecturer.
Gaston Sanchez Trujillo, Lecturer.
Shobhana Murali Stoyanov, Lecturer.
Benson Au, Visiting Assistant Professor.
Wooseok Ha, Visiting Assistant Professor.
Yan Shuo Tan, Visiting Assistant Professor.
David Aldous, Professor Emeritus. Mathematical probability, applied probability, analysis of algorithms, phylogenetic trees, complex networks, random networks, entropy, spatial networks.
Peter J. Bickel, Professor Emeritus & Professor of the Graduate School. Statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology.
David R. Brillinger, Professor Emeritus & Professor of the Graduate School. Risk analysis, statistical methods, data analysis, animal and fish motion trajectories, statistical applications in engineering and science, sports statistics.
Ching-Shui Cheng, Professor Emeritus. Statistics, statistical design of experiments, combinatorial problems, efficient experimental design.
Nicholas P. Jewell, Professor Emeritus & Professor of the Graduate School. AIDS, statistics, epidemiology, infectious diseases, Ebola Virus Disease, SARS, H1N1 influenza, adverse cardiovascular effects of pharmaceuticals, counting civilian casualties during conflicts.
Michael J. Klass, Professor Emeritus. Statistics, mathematics, probability theory, combinatorics independent random variables, iterated logarithm, tail probabilities, functions of sums.
Pressley W. Millar, Professor Emeritus. Statistics, Martingales, Markov processes, Gaussian processes, excursion theory, asymptotic statistical decision theory, nonparametrics, robustness, stochastic procedures, asymptotic minimas theory, bootstrap theory.
* Deborah Nolan, Professor Emerita. Statistics, empirical process, high-dimensional modeling, technology in education.
James W. Pitman, Professor Emeritus. Fragmentation, statistics, mathematics, Brownian motion, distribution theory, path transformations, stochastic processes, local time, excursions, random trees, random partitions, processes of coalescence.
Roger A. Purves, Senior Teaching Professor. Statistics, foundations of probability, measurability.
John A. Rice, Professor Emeritus. Transportation, astronomy, statistics, functional data analysis, time series analysis.
Terence P. Speed, Professor Emeritus. Genomics, statistics, genetics and molecular biology, protein sequences.
Aram Thomasian, Professor Emeritus.
Kenneth Wachter, Professor Emeritus & Professor of the Graduate School. Mathematical demography stochastic models, simulation, biodemography, federal statistical system.
Department of Statistics
367 Evans Hall