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 comes 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. If the applicant is admitted, then official transcripts of all college-level work will be required. Official transcripts must be in sealed envelopes as issued by the school(s) attended. If you have attended Berkeley, upload your unofficial transcript with your application for the departmental initial review. If you are admitted, an official transcript with evidence of degree conferral will not be required.
- 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, not the Graduate Division.
- Evidence of English language proficiency: All applicants from countries or political entities in which the official language is not English are required to submit official evidence of English language proficiency. This applies to applicants 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.
If applicants have previously been denied admission to Berkeley on the basis of their English language proficiency, they must submit new test scores that meet the current minimum 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. Official IELTS score reports must be mailed directly to our office from the British Council. TOEFL and IELTS score reports are only valid for two years.
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:
2. GRE General Test Scores: The GRE is required of all applicants. The test is composed of three sections. Please send your scores electronically to Institution Code 4833. To be valid, the GRE must have been taken within the past five years.
3. Descriptive List of Upper Division/Graduate Statistics and Math Coursework: Include the department, course number, title, instructor, grade, school, texts used and subject matter covered for all upper division and graduate level statistics and math courses you have taken.
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
All students are required to take a minimum of 24 semester units of courses in the department numbered 204-272 inclusive for a letter grade. 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 C205A||Probability Theory||4|
|STAT C205B||Probability Theory||4|
|STAT 204||Probability for Applications||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|
|STAT Electives from 204-272 (4 courses) - one may be upper division||12|
|STAT 375||Professional Preparation: Teaching of Probability and Statistics||2-4|
A member of the PhD program committee (in consultation with the faculty mentor) may consent to substitute courses at a comparable level in other disciplines for some of these departmental graduate courses. These requirements can be altered by the PhD program committee (in consultation with the faculty mentor) in the following cases:
For students with strong interests in another discipline, when the faculty mentor recommends delaying one core PhD course to the second year and substituting a relevant graduate course from another department.
For students who need additional mathematical preparation, they could take MATH 105 (and MATH 104, if needed) in the first year, and only take two of the core PhD courses during that year, thus delaying one or two core PhD courses to the second year.
Students arriving with advanced standing, having done successful graduate course work at another institution prior to joining the program.
After the first year in the program, the PhD program committee will decide if the student has passed the preliminary stage of the program or if the decision is reserved until the end of the second year. To continue in the program, students must pass the preliminary stage by the end of their second year.
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
Prior to filing, the thesis should be presented at an appropriate seminar in the department.
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. When taking the thesis option, a total of 20 units is needed to complete the degree.
|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 the Saturday before the spring semester begins in January, students will take a comprehensive exam on the theoretical foundations of statistics. There will be a two-hour exam on the material of STAT 201A and a two-hour exam on the material of 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.
Peter L. Bartlett, Professor. Statistics, machine learning, statistical learning theory, adaptive control.
David R. Brillinger, Professor. Risk analysis, statistical methods, data analysis, animal and fish motion trajectories, statistical applications in engineering and science, sports statistics.
James Bentley Brown, Assistant Adjunct Professor.
Peng Ding, Assistant Professor.
Sandrine Dudoit, Professor. Genomics, classification, statistical computing, biostatistics, cross-validation, density estimation, genetic mapping, high-throughput sequencing, loss-based estimation, microarray, model selection, multiple hypothesis testing, prediction, RNA-Seq.
Noureddine El Karoui, Professor. Applied statistics, theory and applications of random matrices, large dimensional covariance estimation and properties of covariance matrices, connections with mathematical finance.
Steven N. Evans, Professor. Genetics, random matrices, superprocesses and other measure-valued processes, probability on algebraic structures -particularly local fields, applications of stochastic processes to biodemography, mathematical finance, phylogenetics and historical linguistics.
Avi Feller, Assistant Professor.
Will Fithian, Assistant Professor.
Shirshendu Ganguly, Assistant Professor.
Lisa Goldberg, Adjunct Professor.
Adityanand Guntuboyina, Assistant Professor.
Alan Hammond, Associate Professor.
Haiyan Huang, Professor. Applied statistics, functional genomics, translational bioinformatics, high dimensional and integrative genomic/genetic data analysis, network modeling, hierarchical multi-lable classification.
Michael I. Jordan, Professor. Computer science, artificial intelligence, bioinformatics, statistics, machine learning, electrical engineering, applied statistics, optimization.
Michael J. Klass, Professor. Statistics, mathematics, probability theory, combinatorics independent random variables, iterated logarithm, tail probabilities, functions of sums.
Michael William Mahoney, Associate Adjunct Professor.
Jon Mcauliffe, Adjunct Professor. Bioinformatics, machine learning, nonparametrics, convex optimization, statistical computing, prediction, supervised learning.
Rasmus Nielsen, Professor. Statistical and computational aspects of evolutionary theory and genetics.
+ Deborah Nolan, Professor. Statistics, empirical process, high-dimensional modeling, technology in education.
Christopher Paciorek, Adjunct Professor.
Fernando Perez, Assistant Professor.
Sam Pimentel, Assistant Professor.
James W. Pitman, Professor. Fragmentation, statistics, mathematics, Brownian motion, distribution theory, path transformations, stochastic processes, local time, excursions, random trees, random partitions, processes of coalescence.
Elizabeth Purdom, Associate Professor. Computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics.
Jasjeet S. Sekhon, Professor. Program evaluation, statistical and computational methods, causal inference, elections, public opinion, American politics .
Alistair Sinclair, Professor. Algorithms, applied probability, statistics, random walks, Markov chains, computational applications of randomness, Markov chain Monte Carlo, statistical physics, combinatorial optimization.
Yun Song, Professor. Computational biology, population genomics, applied probability and statistics.
Philip B. Stark, Professor. Astrophysics, law, statistics, litigation, causal inference, inverse problems, geophysics, elections, uncertainty quantification, educational technology.
Bernd Sturmfels, Professor. Mathematics, combinatorics, computational algebraic geometry.
Nike Sun, Assistant Professor.
Mark J. Van Der Laan, 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 algorithmss .
Bin Yu, Professor. Neuroscience, remote sensing, networks, statistical machine learning, high-dimensional inference, massive data problems, document summarization .
+ Ani Adhikari, Senior Lecturer SOE.
Thomas Bengtsson, Lecturer.
Fletcher H. Ibser, Lecturer.
Cari Kaufman, Lecturer.
Brett Kolesnik, Lecturer.
Adam R. Lucas, Lecturer.
Libor Pospisil, Lecturer.
Gaston Sanchez Trujillo, Lecturer.
Shobhana Murali Stoyanov, Lecturer.
Merle Behr, Visiting Assistant Professor.
Oscar Madrid Padilla, Visiting Assistant Professor.
David Aldous, Professor Emeritus & Professor in the Graduate School. Mathematical probability, applied probability, analysis of algorithms, phylogenetic trees, complex networks, random networks, entropy, spatial networks.
Peter J. Bickel, Professor Emeritus & Professor in the Graduate School. Statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology.
Ching-Shui Cheng, Professor Emeritus. Statistics, statistical design of experiments, combinatorial problems, efficient experimental design.
Kjell A. Doksum, Professor Emeritus. Statistics, curve estimation, nonparametric regression, correlation curves, survival analysis, semiparametric, nonparametric settings, regression quantiles, analysis of financial data.
Leo Goodman, Professor Emeritus. Sociology, statistics, log-linear models, correspondence analysis models, mathematical demography, categorical data analysis, survey data analysis, logit models, log-bilinear models, association models.
Nicholas P. Jewell, Professor Emeritus. AIDS, statistics, epidemiology, infectious diseases, Ebola Virus Disease, SARS, H1N1 influenza, adverse cardiovascular effects of pharmaceuticals, counting civilian casualties during conflicts.
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.
Roger A. Purves, Senior Lecturer Emeritus. Statistics, foundations of probability, measurability.
John A. Rice, Professor Emeritus. Transportation, astronomy, statistics, functional data analysis, time series analysis.
Juliet Shaffer, Teaching Professor Emeritus. .
Terence P. Speed, Professor Emeritus. Genomics, statistics, genetics and molecular biology, protein sequences.
Aram Thomasian, Professor Emeritus.
Kenneth Wachter, Professor Emeritus. Mathematical demography stochastic models, simulation, biodemography, federal statistical system.